Conversations – Dataconomy https://dataconomy.ru Bridging the gap between technology and business Tue, 14 Jan 2025 08:13:22 +0000 en-US hourly 1 https://dataconomy.ru/wp-content/uploads/2025/01/DC_icon-75x75.png Conversations – Dataconomy https://dataconomy.ru 32 32 Navigating the AI revolution: Exclusive insights on innovation and ethics from industry leaders https://dataconomy.ru/2024/11/28/navigating-the-ai-revolution-exclusive-insights-on-innovation-and-ethics-from-industry-leaders/ Thu, 28 Nov 2024 10:28:24 +0000 https://dataconomy.ru/?p=60972 Artificial intelligence (AI) is evolving and reshaping the way industries work, and it is doing so in almost every sector without exception. Dataconomy sat down with the leaders from NVIDIA, Siemens, Capgemini, and Scaleway at VivaTech 2024 to hear their views on how AI is reforming industries and driving innovation. In these exclusive interviews, we […]]]>

Artificial intelligence (AI) is evolving and reshaping the way industries work, and it is doing so in almost every sector without exception. Dataconomy sat down with the leaders from NVIDIA, Siemens, Capgemini, and Scaleway at VivaTech 2024 to hear their views on how AI is reforming industries and driving innovation. In these exclusive interviews, we have discussed AI’s opportunities, ethical considerations, and long-term challenges of implementing such powerful technologies. The common theme? AI is not just changing processes; it’s changing mindsets, strategies, and entire markets, opening up incredible opportunities and bringing huge responsibilities. 

AI at scale in the new era of computing

Navigating the AI revolution: Exclusive insights on innovation and ethics from industry leaders

NVIDIA has been one of the leading pioneers of the AI revolution for over 15 years, with its technology driving AI advancements. We talked to Nat Ives, Enterprise Director at NVIDIA France, who emphasized two major innovations on their front: AI-enabled robotics and the NVIDIA Inference Microservice (NIMS).

“AI-enabled robotics will be the physical interface of AI in the real world,” said Ives, explaining how these advancements will allow robots to operate autonomously in real-world settings. This opens up a world of possibilities for industries such as healthcare and manufacturing, where robots will manage tasks that previously required human intervention. 

“AI-enabled robotics will be
the physical interface of AI in the real world”

What does this mean for human workers? The days of robots simply performing pre-programmed tasks are coming to an end. These AI-powered robots will be able to learn, adapt, and collaborate with human teams in real-time, creating a seamless relationship between humans and machines. This shift will change how industries view workforce productivity, with robots handling repetitive tasks, allowing humans to focus on problem-solving and decision-making.

NVIDIA Inference Microservice (NIM), another innovation emphasized by Ives, is a tool that offers pre-configured microservices that simplify AI integration into existing IT infrastructures. This allows companies, from startups to large corporations, to deploy AI at scale without rebuilding their entire systems. Pointing out that NIMS aims to democratize AI, Ives said, “We want to make sure AI is not just for the big players. Smaller companies, startups—they should have access too.” 

However, the transition to this next phase of AI is not without its challenges. Ives highlighted the need for significant upgrades to data centers, saying, There’s a huge piece of work to evolve all the data centers to embrace this innovation, but we have the answers. It’s a matter of implementing them.”

AI’s strategic evolution in the industry

Navigating the AI revolution: Exclusive insights on innovation and ethics from industry leaders

At Siemens, AI is not a groundbreaking addition but a natural evolution of their long-standing mission to optimize industrial operations. Jean-Marie Saint-Paul, General Director of Siemens Digital Industries France, explained that AI enhances their industrial automation processes, aligning with their broader goal of efficiency and knowledge retention. “AI is pervading across society in the same way it is within industries,” Jean-Marie told me.

Siemens has integrated AI into its Xcelerator portfolio, focusing on improving productivity and decision-making, particularly in industries grappling with an aging workforce and a shortage of skilled labor. AI plays a pivotal role in predictive maintenance, smart automation, and workflows, identifying potential failures before they occur, optimizing performance, and reducing downtime.

“AI needs to be deployed
with a mindset focused on real world production”

However, Jean-Mariel stressed the importance of reliability when deploying AI in critical environments like production lines. The company takes a cautious approach, rigorously testing AI systems before integrating them. AI should not just be a fancy tool. It needs to be deployed with a mindset focused on real-world production,” he said.

Siemens’ strategy involves introducing AI as an advisor before fully entrusting it with control. This ensures that AI is thoroughly tested and proven reliable before taking on more responsibilities. This phased approach to AI implementation is designed to prevent errors in highly sensitive environments, ensuring that AI contributes to efficiency while maintaining safety and ethical standards.

The AI renaissance

Navigating the AI revolution: Exclusive insights on innovation and ethics from industry leaders

Andy Vickers, CTO of Generative AI at Capgemini Engineering, referred to this period as an “AI Renaissance,” signaling a new chapter in technological evolution. Vickers believes that AI will not transform industries by itself but by integrating with other cutting-edge technologies. As he explained during our interview, “This isn’t just about AI working on its own; it’s about AI working in tandem with other powerful tools to create something far greater.” He further emphasized that AI, combined with technologies like edge computing and the Internet of Things (IoT), sparks a new industrial revolution.

Capgemini focuses on building applications that offer hyper-personalized experiences. According to Vickers, “Our strategy is about creating AI solutions that empower businesses to deliver individualized services while maintaining transparency and trust.” 

“AI systems should be humble, acknowledging their limitations
while enhancing human creativity and decision-making”

But Capgemini focuses on more than technology—ethics also play a central role in its approach. Vickers was clear that ethical AI is about more than compliance. “AI must acknowledge its limitations. Having an ethical policy isn’t just about compliance; it’s about building a healthy relationship between humans and machines,” he said.

To ensure AI’s responsible development, Capgemini has implemented a rigorous ethics policy addressing bias, data compliance, and transparency. “AI systems should be humble, acknowledging their limitations while enhancing human creativity and decision-making,” Vickers explained. For Capgemini, responsible AI isn’t just a corporate responsibility; it’s about securing long-term trust and ensuring that AI serves as a tool to complement human capabilities rather than replace them.

Empowering Europe with ethical AI commitment

Navigating the AI revolution: Exclusive insights on innovation and ethics from industry leaders

During our conversation, Adrienne Jan, Chief Product Officer at Scaleway, presented a unique European perspective on AI. Scaleway has positioned itself as a vital cloud provider for startups, focused on training AI models in Europe without relying on U.S. or Chinese infrastructure. We want to be the cloud of choice for European startups, supporting innovation while complying with European data laws,” Jan explained.

Jan explained that data sovereignty has become crucial for European startups. With increased global concern over data privacy and security, European companies are looking for ways to innovate without sacrificing control over their data. “We’re offering European startups a cloud solution that respects European data laws,” Jan explained. This ensures that companies can innovate and remain compliant with strict privacy regulations.

“Mission Possible: Supporting innovation
while complying with European data laws”

Scaleway’s infrastructure, which includes sustainable adiabatic data centers, operates with sustainability at its core. The company is proud of these data centers, which use 90 percent less water and dramatically cut electricity usage. We’re building the cloud industry’s first environmental calculator,” Jan revealed, highlighting the company’s mission to help clients track and reduce their environmental impact.

This ethical approach to AI development is grounded in European data sovereignty and privacy values. The goal of Scaleway is to enable European startups and companies to lead in AI innovation within a fair balance of technical progress and ethical responsibility.

Building a responsible AI future

At VivaTech 2024, leaders from NVIDIA, Scaleway, Siemens, and Capgemini shared their perspectives on AI’s growing role in industries worldwide. While these companies are pioneering some of the most advanced AI technologies, they also emphasize ethical considerations, understanding that innovation without responsibility could lead to unintended consequences.

From AI-powered robots to sustainable cloud infrastructure, AI’s future lies in pushing technological limits and creating systems that enhance human creativity and foster trust. It’s about building ‘humble’ AI—tools that work alongside humans, acknowledge their limitations, and ensure transparency and fairness. This human-centered approach to AI isn’t just about compliance; it’s about forging a relationship between people and machines that benefits everyone.

When applied thoughtfully in industries like manufacturing, AI needs to be a reliable advisor before it can take on more responsibilities. This careful approach is mirrored across the board, with companies championing ethical AI development that aligns with sustainability and data sovereignty principles.

As AI continues to become part of society’s fabric, these industry leaders are setting the tone for a future where AI empowers rather than replaces the human workforce. These insights remind me that the AI revolution is still young, and the choices we make now will shape its impact on industries, economies, and everyday life. By aligning innovation with ethical standards, we can build an AI-driven future prioritizing human creativity, trust, and collaboration.

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Will private data work in a new-era AI world? https://dataconomy.ru/2024/11/19/will-private-data-work-in-a-new-era-ai-world/ Tue, 19 Nov 2024 09:18:21 +0000 https://dataconomy.ru/?p=60393 At the last AI Conference, we had a chance to sit down with Roman Shaposhnik and Tanya Dadasheva, the co-founders of Ainekko/AIFoundry, and discuss with them an ambiguous topic of data value for enterprises in the times of AI. One of the key questions we started from was: are most companies running the same frontier […]]]>

At the last AI Conference, we had a chance to sit down with Roman Shaposhnik and Tanya Dadasheva, the co-founders of Ainekko/AIFoundry, and discuss with them an ambiguous topic of data value for enterprises in the times of AI. One of the key questions we started from was: are most companies running the same frontier AI models, is incorporating their data the only way they have a chance to differentiate? Is data really a moat for enterprises?

Roman recalls: “Back in 2009, when he started in the big data community, everyone talked about how enterprises would transform by leveraging data. At that time, they weren’t even digital enterprises; the digital transformation hadn’t occurred yet. These were mostly analog enterprises, but they were already emphasizing the value of the data they collected—data about their customers, transactions, supply chains, and more. People likened data to oil, something with inherent value that needed to be extracted to realize its true potential.”

However, oil is a commodity. So, if we compare data to oil, it suggests everyone has access to the same data, though in different quantities and easier to harvest for some. This comparison makes data feel like a commodity, available to everyone but processed in different ways.

When data sits in an enterprise data warehouse in its crude form, it’s like an amorphous blob—a commodity that everyone has. However, once you start refining it, that’s when the real value comes in. It’s not just about acquiring data but building a process from extraction to refining all the value through the pipeline.

Interestingly, this reminds me of something an oil corporation executive once told me” – shares Roman. “That executive described the business not as extracting oil but as reconfiguring carbon molecules. Oil, for them, was merely a source of carbon. They had built supply chains capable of reconfiguring these carbon molecules into products tailored to market demands in different locations—plastics, gasoline, whatever the need was. He envisioned software-defined refineries that could adapt outputs based on real-time market needs. This concept blew my mind, and I think it parallels what we’re seeing in data now—bringing compute to data, refining it to get what you need, where you need it” – was Roman’s insight.

In enterprises, when you start collecting data, you realize it’s fragmented and in many places—sometimes stuck in mainframes or scattered across systems like Salesforce. Even if you manage to collect it, there are so many silos, and we need a fracking-like approach to extract the valuable parts. Just as fracking extracts oil from places previously unreachable, we need methods to get enterprise data that is otherwise locked away.

A lot of enterprise data still resides in mainframes, and getting it out is challenging. Here’s a fun fact: with high probability, if you book a flight today, the backend still hits a mainframe. It’s not just about extracting that data once; you need continuous access to it. Many companies are making a business out of helping enterprises get data out of old systems, and tools like Apache Airflow are helping streamline these processes.

But even if data is no longer stuck in mainframes, it’s still fragmented across systems like cloud SaaS services or data lakes. This means enterprises don’t have all their data in one place, and it’s certainly not as accessible or timely as they need. You might think that starting from scratch would give you an advantage, but even newer systems depend on multiple partners, and those partners control parts of the data you need.

The whole notion of data as a moat turns out to be misleading then. Conceptually, enterprises own their data, but they often lack real access. For instance, an enterprise using Salesforce owns the data, but the actual control and access to that data are limited by Salesforce. The distinction between owning and having data is significant.

Things get even more complicated when AI starts getting involved” – says Tanya Dadasheva, another co-founder of AInekko and AIFoundry.org. “An enterprise might own data, but it doesn’t necessarily mean a company like Salesforce can use it to train models. There’s also the debate about whether anonymized data can be used for training—legally, it’s a gray area. In general, the more data is anonymized, the less value it holds. At some point, getting explicit permission becomes the only way forward”.

This ownership issue extends beyond enterprises; it also affects end-users. Users often agree to share data, but they may not agree to have it used for training models. There have been cases of reverse-engineering data from models, leading to potential breaches of privacy.

At an early stage of balancing data producers, data consumers, and the entities that refine data, legally and technologically it is extremely complex figuring out how these relationships will work. Europe, for example, has much stricter privacy rules compared to the United States (https://artificialintelligenceact.eu/). In the U.S., the legal system often figures things out on the go, whereas Europe prefers to establish laws in advance.

Tanya addresses data availability here: “This all ties back to the value of data available. The massive language models we’ve built have grown impressive thanks to public and semi-public data. However, much of the newer content is now trapped in “walled gardens” like WeChat, Telegram or Discord, where it’s inaccessible for training – true dark web! This means the models may become outdated, unable to learn from new data or understand new trends.

In the end, we risk creating models that are stuck in the past, with no way to absorb new information or adapt to new conversational styles. They’ll still contain older data, and the newer generation’s behavior and culture won’t be represented. It’ll be like talking to a grandparent—interesting, but definitely from another era.

Will private data work in a new-era AI world
(Image credit)

But who are the internal users of the data in an enterprise? Roman recalls the three epochs of data utilization concept within the enterprises: “Obviously, it’s used for many decisions, which is why the whole business intelligence part exists. It all actually started with business intelligence. Corporations had to make predictions and signal to the stock markets what they expect to happen in the next quarter or a few quarters ahead. Many of those decisions have been data-driven for a long time. That’s the first level of data usage—very straightforward and business-oriented.

The second level kicked in with the notion of digitally defined enterprises or digital transformation. Companies realized that the way they interact with their customers is what’s valuable, not necessarily the actual product they’re selling at the moment. The relationship with the customer is the value in and of itself. They wanted that relationship to last as long as possible, sometimes to the extreme of keeping you glued to the screen for as long as possible. It’s about shaping the behavior of the consumer and making them do certain things. That can only be done by analyzing many different things about you—your social and economic status, your gender identity, and other data points that allow them to keep that relationship going for as long as they can.

Now, we come to the third level or third stage of how enterprises can benefit from data products. Everybody is talking about these agentic systems because enterprises now want to be helped not just by the human workforce. Although it sounds futuristic, it’s often as simple as figuring out when a meeting is supposed to happen. We’ve always been in situations where it takes five different emails and three calls to figure out how two people can meet for lunch. It would be much easier if an electronic agent could negotiate all that for us and help with that. That’s a simple example, but enterprises have all sorts of others. Now it’s about externalizing certain sides of the enterprise into these agents. That can only be done if you can train an AI agent on many types of patterns that the enterprise has engaged in the past.”

Getting back to who collects and who owns and, eventually, benefits from data: the first glimpse of that Roman got when working back at Pivotal on a few projects that involved airlines and companies that manufacture engines:

“What I didn’t know at the time is that apparently you don’t actually buy the engine; you lease the engine. That’s the business model. And the companies producing the engines had all this data—all the telemetry they needed to optimize the engine. But then the airline was like, “Wait a minute. That is exactly the same data that we need to optimize the flight routes. And we are the ones collecting that data for you because we actually fly the plane. Your engine stays on the ground until there’s a pilot in the cockpit that actually flies the plane. So who gets to profit from the data? We’re already paying way too much to engine people to maintain those engines. So now you’re telling us that we’ll be giving you the data for free? No, no, no.”

This whole argument is really compelling because that’s exactly what is now repeating itself between OpenAI and all of the big enterprises. Big enterprises think OpenAI is awesome; they can build this chatbot in minutes—this is great. But can they actually send that data to OpenAI that is required for fine-tuning and all these other things? And second of all, suppose those companies even can. Suppose it’s the kind of data that’s fine, but it’s their data – collected by those companies. Surely it’s worth something to OpenAI, so why don’t they drop the bill on the inference side for companies who collected it?

And here the main question of today’s data world kicks in: Is it the same with AI?

In some way, it is, but with important nuances. If we can have a future where the core ‘engine’ of an airplane, the model, gets produced by these bigger companies, and then enterprises leverage their data to fine-tune or augment these models, then there will be a very harmonious coexistence of a really complex thing and a more highly specialized, maybe less complex thing on top of it. If that happens and becomes successful technologically, then it will be a much easier conversation at the economics and policy level of what belongs to whom and how we split the data sets.

As an example, Roman quotes his conversation with an expert who designs cars for a living: “He said that there are basically two types of car designers: one who designs a car for an engine, and the other one who designs a car and then shops for an engine. If you’re producing a car today, it’s much easier to get the engine because the engine is the most complex part of the car. However, it definitely doesn’t define the product. But still, the way that the industry works: it’s much easier to say, well, given some constraints, I’m picking an engine, and then I’m designing a whole lineup of cars around that engine or that engine type at least.

This drives us to the following concept: we believe that’s what the AI-driven data world will look like. There will be ‘Google’ camp and ‘Meta camp’, and you will pick one of those open models – all of them will be good enough. And then, all of the stuff that you as an enterprise are interested in, is built on top of it in terms of applying your data and your know-how of how to fine-tune them and continuously update those models from different ‘camps’. In case this works out technologically and economically, a brave new world will emerge.


Featured image credit: NASA/Unsplash

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The Future is in Your Pocket: How to Move AI to Smartphones https://dataconomy.ru/2024/11/18/how-to-move-ai-to-smartphones/ Mon, 18 Nov 2024 09:44:44 +0000 https://dataconomy.ru/?p=60298 For years, the promise of truly intelligent, conversational AI has felt out of reach. We’ve marveled at the abilities of ChatGPT, Gemini, and other large language models (LLMs) – composing poems, writing code, translating languages – but these feats have always relied on the vast processing power of cloud GPUs. Now, a quiet revolution is […]]]>

For years, the promise of truly intelligent, conversational AI has felt out of reach. We’ve marveled at the abilities of ChatGPT, Gemini, and other large language models (LLMs) – composing poems, writing code, translating languages – but these feats have always relied on the vast processing power of cloud GPUs. Now, a quiet revolution is brewing, aiming to bring these incredible capabilities directly to the device in your pocket: an LLM on your smartphone.

This shift isn’t just about convenience; it’s about privacy, efficiency, and unlocking a new world of personalized AI experiences. 

However, shrinking these massive LLMs to fit onto a device with limited memory and battery life presents a unique set of challenges. To understand this complex landscape, I spoke with Aleksei Naumov, Lead AI Research Engineer at Terra Quantum, a leading figure in the field of LLM compression. 

Indeed, Naumov recently published a paper on this subject which is being heralded as an extraordinary and significant innovation in neural network compression – ‘TQCompressor: Improving Tensor Decomposition Methods in Neural Networks via Permutations’ – at the IEEE International Conference on Multimedia Information Processing and Retrieval (IEEE MIPR 2024), a conference where researchers, scientists, and industry professionals come together to present and discuss the latest advancements in multimedia technology.

“The main challenge is, of course, the limited main memory (DRAM) available on smartphones,” Naumov said. “Most models cannot fit into the memory of a smartphone, making it impossible to run them.”

He points to Meta’s Llama 3.2-8B model as a prime example. 

“It requires approximately 15 GB of memory,” Naumov said. “However, the iPhone 16 only has 8 GB of DRAM, and the Google Pixel 9 Pro offers 16 GB. Furthermore, to operate these models efficiently, one actually needs even more memory – around 24 GB, which is offered by devices like the NVIDIA RTX 4090 GPU, starting at $1800.”

This memory constraint isn’t just about storage; it directly impacts a phone’s battery life.

“The more memory a model requires, the faster it drains the battery,” Naumov said. “An 8-billion parameter LLM consumes about 0.8 joules per token. A fully charged iPhone, with approximately 50 kJ of energy, could only sustain this model for about two hours at a rate of 10 tokens per second, with every 64 tokens consuming around 0.2% of the battery.”

So, how do we overcome these hurdles? Naumov highlights the importance of model compression techniques.

“To address this, we need to reduce model sizes,” Naumov said. “There are two primary approaches: reducing the number of parameters or decreasing the memory each parameter requires.”

He outlines strategies like distillation, pruning, and matrix decomposition to reduce the number of parameters and quantization to decrease each parameter’s memory footprint.

“By storing model parameters in INT8 instead of FP16, we can reduce memory consumption by about 50%,” Naumov said.

While Google’s Pixel devices, with their TensorFlow-optimized TPUs, seem like an ideal platform for running LLMs, Naumov cautions that they don’t solve the fundamental problem of memory limitations.

“While the Tensor Processing Units (TPUs) used in Google Pixel devices do offer improved performance when running AI models, which can lead to faster processing speeds or lower battery consumption, they do not resolve the fundamental issue of the sheer memory requirements of modern LLMs, which typically exceed smartphone memory capacities,” Naumov said.

The drive to bring LLMs to smartphones goes beyond mere technical ambition. It’s about reimagining our relationship with AI and addressing the limitations of cloud-based solutions.

“Leading models like ChatGPT-4 have over a trillion parameters,” Naumov said. “If we imagine a future where people depend heavily on LLMs for tasks like conversational interfaces or recommendation systems, it could mean about 5% of users’ daily time is spent interacting with these models. In this scenario, running GPT-4 would require deploying roughly 100 million H100 GPUs. The computational scale alone, not accounting for communication and data transmission overheads, would be equivalent to operating around 160 companies the size of Meta. This level of energy consumption and associated carbon emissions would pose significant environmental challenges.”

The vision is clear: a future where AI is seamlessly integrated into our everyday lives, providing personalized assistance without compromising privacy or draining our phone batteries.

“I foresee that many LLM applications currently relying on cloud computing will transition to local processing on users’ devices,” Naumov said. “This shift will be driven by further model downsizing and improvements in smartphone computational resources and efficiency.”

He paints a picture of a future where the capabilities of LLMs could become as commonplace and intuitive as auto-correct is today. This transition could unlock many exciting possibilities. Thanks to local LLMs, imagine enhanced privacy where your sensitive data never leaves your device.

Picture ubiquitous AI with LLM capabilities integrated into virtually every app, from messaging and email to productivity tools. Think of the convenience of offline functionality, allowing you to access AI assistance even without an internet connection. Envision personalized experiences where LLMs learn your preferences and habits to provide truly tailored support.

For developers eager to explore this frontier, Naumov offers some practical advice.

“First, I recommend selecting a model that best fits the intended application,” Naumov said. “Hugging Face is an excellent resource for this. Look for recent models with 1-3 billion parameters, as these are the only ones currently feasible for smartphones. Additionally, try to find quantized versions of these models on Hugging Face. The AI community typically publishes quantized versions of popular models there.”

He also suggests exploring tools like llama.cpp and bitsandbytes for model quantization and inference.

The journey to bring LLMs to smartphones is still in its early stages, but the potential is undeniable. As researchers like Aleksei Naumov continue to push the boundaries of what’s possible, we’re on the cusp of a new era in mobile AI, one where our smartphones become truly intelligent companions, capable of understanding and responding to our needs in ways we’ve only begun to imagine.

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The Rise of AI Lip-sync: From Uncanny Valley to Hyperrealism https://dataconomy.ru/2024/11/05/rise-ai-lip-sync-uncanny-valley-hyperrealism/ Tue, 05 Nov 2024 11:30:23 +0000 https://dataconomy.ru/?p=59764 Remember the awkward dubbing in old kung-fu movies? Or the jarring lip-sync in early animated films? Those days are fading fast, and thanks to the rise of AI-powered lip-sync technology, could forever be behind us. Since April 2023, the number of solutions and the volume of “AI lip-sync” keyword searches has grown dramatically, coming from […]]]>

Remember the awkward dubbing in old kung-fu movies? Or the jarring lip-sync in early animated films? Those days are fading fast, and thanks to the rise of AI-powered lip-sync technology, could forever be behind us. Since April 2023, the number of solutions and the volume of “AI lip-sync” keyword searches has grown dramatically, coming from nowhere to becoming one of the critical trends in generative AI

This cutting-edge field is revolutionizing how we create and consume video content, with implications for everything from filmmaking and animation to video conferencing and gaming.

To delve deeper into this fascinating technology, I spoke with Aleksandr Rezanov, a Computer Vision and Machine Learning Engineer who previously spearheaded lip-sync development at Rask AI and currently works at Higgsfield AI in London. Rezanov’s expertise offers a glimpse into AI lip-sync’s intricate workings, challenges, and transformative potential.

Deconstructing the Magic: How AI lip-sync Works

“Most lip-sync architectures operate on a principle inspired by the paper ‘Wav2Lip: Accurately Lip-syncing Videos In The Wild‘,” Rezanov told me. These systems utilize a complex interplay of neural networks to analyze audio input and generate corresponding lip movements. “The input data includes an image where we want to alter the mouth, a reference image showing how the person looks, and an audio input,” Rezanov said.

Three separate encoders process this data, creating compressed representations that interact to generate realistic mouth shapes. “The lip-sync task is to ‘draw’ a mouth where it’s masked (or adjust an existing mouth), given the person’s appearance and what they were saying at that moment,” Rezanov said.

This process involves intricate modifications, including using multiple reference images to capture a person’s appearance, employing different facial models, and varying audio encoding methods. 

“In essence, studies on lip-syncing explore which blocks in this framework can be replaced while the basic principles remain consistent: three encoders, internal interaction, and a decoder,” Rezanov said.

Developing AI lip-sync technology is a challenging feat. Rezanov’s team at Rask AI faced numerous challenges, particularly in achieving visual quality and accurate audio-video synchronization. 

“To resolve this, we applied several strategies,” Rezanov said. “That included modifying the neural network architecture, refining and enhancing the training procedure, and improving the dataset.” 

Rask also pioneered lip-sync support for videos with multiple speakers, a complex task requiring speaker diarization – automatically identifying and segmenting an audio recording into distinct speech segments – and active speaker detection.

Beyond Entertainment: The Expanding Applications of AI lip-sync

The implications of AI lip-sync extend far beyond entertainment. “Lip-sync technology has a wide range of applications,” Rezanov said. “By utilizing high-quality lip-sync, we can eliminate the audio-visual gap when watching translated content, allowing viewers to stay immersed without being distracted by mismatches between speech and video.” 

This has significant implications for accessibility, making content more engaging for viewers who rely on subtitles or dubbing. Furthermore, AI lip-sync can streamline content production, reducing the need for multiple takes and lowering costs. 

“This technology could streamline and reduce the cost of content production, saving game studios significant resources while likely improving animation quality,” Rezanov said.

The Quest for Perfection: The Future of AI lip-sync

While AI lip-sync has made remarkable strides, the quest for perfect, indistinguishable lip-syncing continues. 

“The biggest challenge with lip-sync technology is that humans, as a species, are exceptionally skilled at recognizing faces,” Rezanov said. “Evolution has trained us for this task over thousands of years, which explains the difficulties in generating anything related to faces.”

He outlines three stages in lip-sync development: achieving basic mouth synchronization with audio, creating natural and seamless movements, and finally, capturing fine details like pores, hair, and teeth. 

“Currently, the biggest hurdle in lip-sync lies in enhancing this level of detail,” Rezanov said. “Teeth and beards remain particularly challenging.” As an owner of both teeth and a beard, I can attest to the disappointment (and sometimes belly-laugh-inducing Dali-esque results) I’ve experienced when testing some AI lip-sync solutions

Despite these challenges, Rezanov remains optimistic.

“In my opinion, we are steadily closing in on achieving truly indistinguishable lip-sync,” Rezanov said. “But who knows what new details we’ll start noticing when we get there?”

From lip-sync to Face Manipulation: The Next Frontier

Rezanov’s work at Higgsfield AI builds upon his lip-sync expertise, focusing on broader face manipulation techniques. 

“Video generation is an immense field, and it’s impossible to single out just one aspect,” Rezanov said. “At the company, I primarily handle tasks related to face manipulation, which closely aligns with my previous experience.”

His current focus includes optimizing face-swapping techniques and ensuring character consistency in generated content. This work pushes the boundaries of AI-driven video manipulation, opening up new possibilities for creative expression and technological innovation.

As AI lip-sync technology evolves, we can expect even more realistic and immersive experiences in film, animation, gaming, and beyond. The uncanny valley is shrinking, and a future of hyperrealistic digital humans is within reach.

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How developers cope with climate challenges: Insights from Aigerim Tleshova https://dataconomy.ru/2024/10/30/how-developers-cope-with-climate-challenges-insights-from-aigerim-tleshova/ Wed, 30 Oct 2024 14:54:49 +0000 https://dataconomy.ru/?p=59649 With global temperatures having risen by approximately 1.3°C since the pre-industrial era, the Earth’s climate is undergoing significant changes. The consequences – such as heatwaves, floods, and wildfires – are now affecting the world’s population. While these natural disasters disrupt everyday life for millions, they also inflict billions of dollars in economic losses, as reported […]]]>

With global temperatures having risen by approximately 1.3°C since the pre-industrial era, the Earth’s climate is undergoing significant changes. The consequences – such as heatwaves, floods, and wildfires – are now affecting the world’s population. While these natural disasters disrupt everyday life for millions, they also inflict billions of dollars in economic losses, as reported by the World Meteorological Organization (WMO). In 2024, devastating floods hit Kazakhstan, affecting 224 settlements and damaging over 17,000 residential homes, with estimated damages reaching approximately $444 million – surpassing the total economic losses from water-related disasters over the past 30 years. To understand how a construction business copes with such challenges, we turned to Aigerim Tleshova, an MBA and head of the Investment Department at BI-Group, a leading construction holding in Central Asia.

Aigerim, in your view, what is the most significant investment risk posed by climate change for the construction sector?

Aigerim TleshovaIn my view, the biggest investment risk climate change poses to the construction sector is increased costs and delays due to extreme weather and regulatory changes.

Extreme weather—like floods, storms, and heatwaves—can damage sites, disrupt supply chains, and delay projects, leading to higher costs for repairs, labor, and protective measures. Insurance premiums may also rise in areas with unstable weather.

Additionally, stricter climate regulations will require investments in greener technologies, sustainable practices, and eco-friendly materials, increasing expenses. Failure to comply could result in penalties or project cancellations.

Lastly, buildings in climate-prone areas or those that don’t meet future sustainability standards could lose value, risking long-term asset devaluation.

You manage a remarkable investment portfolio, and you aim to increase the company’s investment opportunities by 15%. How do you stay ahead of these risks while maintaining profitability?

To achieve a 15% increase in investment opportunities while managing risks, a comprehensive strategy is essential. This should focus on diversification, sustainability, innovation, risk management, and stakeholder engagement to ensure long-term profitability.

Diversification is crucial – I ensure the portfolio covers different sectors, balancing projects with quick returns and those that offer longer-term stability. I also integrate ESG factors into my decisions, as companies that align with these principles are often more resilient to regulatory changes and market shifts.

Risk assessment is ongoing. I regularly evaluate the portfolio and adjust it based on emerging risks or new opportunities. Innovation plays a big role too – investing in technology, like AI in construction, allows me to enhance efficiency and sustainability. Staying engaged with stakeholders and policymakers is equally important; it helps me stay on top of regulatory changes and market demands, ensuring my strategies are both flexible and forward-thinking.

By staying actively involved in monitoring performance and regularly reviewing market conditions, I’m able to pivot strategies quickly in response to emerging risks or opportunities. This agility allows me to manage risks effectively while also uncovering new paths for growth. Overall, it’s about staying flexible and being ready to adapt at any moment, all while keeping a focus on long-term success.

BI-Group prioritizes environmental, social, and governance (ESG) principles. Given your success in leading a team to drive profit growth, especially through strategic cash flow management, how do you see ESG investments enhancing profitability while addressing climate challenges?

ESG-focused investments can significantly contribute to profitability while addressing climate challenges in several ways. First, companies that prioritize sustainable practices often experience reduced operational costs through energy efficiency and waste reduction, enhancing overall profit margins. Second, by investing in environmentally friendly technologies and practices, BI-Group can tap into the growing market demand for sustainable products, attracting eco-conscious consumers and expanding our customer base.

Additionally, strong ESG performance can mitigate risks related to regulatory compliance and reputational damage, ultimately leading to more stable financial returns. Engaging in ESG initiatives aligns with our corporate values and positions BI-Group as a leader in sustainability, which can enhance brand loyalty and drive long-term profitability.

In 2023, you adapted the McKinsey 5-Block methodology to BI-Group’s investment portfolio, facilitating strategic decisions. Did this impact the company’s initiative to provide over 500 million tenge in relief during the recent floods? What motivates you most about this work?

Adopting the McKinsey 5-Block methodology for the investment portfolio helped us identify investments where the value from disposal exceeded the potential future returns. This analysis allowed us to increase profitability within the investment portfolio, which positively impacted the company’s consolidated profit and loss statement, contributing to overall profitability. As a result of these enhanced financial outcomes, top management was able to make the decision to provide substantial financial support during the recent floods.

What motivates me most about this is the ability to use structured, strategic frameworks not just for financial gain, but also for meaningful, real-world impact. The ability to support communities in times of crisis while maintaining a strong business foundation is a powerful example of how corporate strategy can align with social responsibility. Helping the company make these impactful decisions such as supporting charitable projects Juldyzay, flooding issues, heating collapse in Ekibastuz or supplying breathing apparatuses during Covid-19, motivates me to continue seeking innovative ways to combine profitability with purpose.

As an executive overseeing investment strategy, what policy shifts would you like to see from governments to help real estate developers better address climate challenges?

I believe governments should implement several key policy changes to help the real estate sector effectively address climate challenges. First, providing incentives for sustainable practices, such as tax credits or subsidies for companies investing in eco-friendly construction methods, would encourage more developers to prioritize sustainability.

Establishing stronger building codes that mandate energy efficiency and sustainable design principles would also ensure that new developments meet higher environmental standards. It’s also important to support the integration of renewable energy sources into construction projects, perhaps through grants or low-interest loans, which could significantly reduce both carbon footprints and operational costs.

In addition, increased investment in green infrastructure, like public transit systems, renewable energy grids, and sustainable water management, can empower developers to create resilient, low-impact communities.

I think such policy shifts would help the development sector be more effective in addressing climate challenges and create opportunities for innovation and growth.

You implemented automated calculation models and optimized the decision-making process within Investment Committees. How does this help in responding to large-scale challenges such as significant flooding?

I think implementing automated calculation models and streamlining decision-making within investment committees enhances the ability to respond to large-scale risks like severe flooding. Automated models allow rapid risk assessment, providing real-time evaluation of flood impacts on investments for timely decisions. Streamlined processes enable efficient scenario planning, preparing the company for various flood-related risks.

With clearer insights, resources can be efficiently allocated to high-risk areas, and quick access to accurate data supports timely stakeholder communication, ensuring transparency. These advancements strengthen flood preparedness and reinforce the company’s commitment to responsible investing and sustainability.

As a renowned expert in investment management, what recommendations would you offer to other companies in the construction industry facing escalating climate threats?

There are a few key strategies I’d recommend for companies in the construction industry as they grapple with the growing threat of climate change.

First, it’s important to integrate ESG principles into every aspect of decision-making. This approach enhances a company’s reputation and helps identify potential risks and opportunities related to climate change. Next, it is crucial to leverage technology. By using advanced tools such as data analytics, modeling software, and AI, companies can improve risk assessment and project planning. These tools will allow them to predict climate impacts and optimize resource allocation.
Collaboration is another key factor. Partnering with governments and other stakeholders can lead to shared solutions for climate adaptation and sustainability. These partnerships can also open doors to funding and resources for innovative projects.

Investing in education and training for your teams is also critical. When employees understand climate risks and sustainable practices, they can make better decisions and drive innovation to address these challenges. Consider diversifying investments into sectors that are less vulnerable to climate risks, such as renewable energy or green technology. This can help reduce overall portfolio risk. Lastly, it’s important to regularly review and adapt strategies in response to evolving climate threats. Being proactive, rather than reactive, will enhance long-term sustainability and resilience.

With these approaches, construction companies can effectively manage the climate change risks while positioning themselves as leaders in sustainability.


Featured image credit: Emre Çıtak/Ideogram AI

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Taming the Cloud Beast: Expert Advice on Cost Optimization in the Age of AI https://dataconomy.ru/2024/10/23/expert-advice-cost-optimization-ai/ Wed, 23 Oct 2024 10:30:47 +0000 https://dataconomy.ru/?p=59439 With its promise of scalability and access to cutting-edge technologies like AI, the cloud has become the digital backbone of modern business. But this convenience comes with a catch: the potential for runaway costs and the need for cost optimization. According to Gartner, by 2024, companies worldwide are expected to spend a massive $675.4 billion […]]]>

With its promise of scalability and access to cutting-edge technologies like AI, the cloud has become the digital backbone of modern business. But this convenience comes with a catch: the potential for runaway costs and the need for cost optimization. According to Gartner, by 2024, companies worldwide are expected to spend a massive $675.4 billion on public cloud services. This represents a significant jump from the $561 billion spent in 2023, highlighting the rapid growth of cloud adoption. 

Businesses scramble to optimize their IT budgets in an economic climate where every penny counts, particularly cloud expenditures. And yet, it is clear that enterprises see significant returns from the cloud. In a recent report conducted by Forrester Research, almost two-thirds of respondents said they increased their cloud spending in the last year, up from 56% in 2023.

To shed light on this crucial topic, I talked with Aaron Sandeen, VP of Technology Services at Crayon, an IT company that offers cloud migration support, artificial intelligence, and software asset management services. A seasoned expert in cost optimization and cloud management, Sandeen offers invaluable insights into the challenges and opportunities facing businesses today.

The Holistic Approach: Aligning IT with Your North Star

“In the era of AI and cloud computing,” Sandeen said, “the biggest challenge organizations face is aligning their IT spending with their core business objectives.”

He champions a holistic approach, borrowing wisdom from Jim Collins’ much-lauded book “Good to Great: Why Some Companies Make the Leap… and Others Don’t.” The book describes how companies transition from good to great and how most fail to do so.

“Leaders need to define their ‘Big Hairy Audacious Goal (BHAG’),” Sandeen said, “and ensure every IT investment contributes to achieving it.”

According to Sandeen, this strategy transforms budget discussions from mere number crunching into strategic prioritization. 

“By categorizing systems into ‘Run-The-Business’ and ‘Change-The-Business,’ organizations can effectively evaluate costs, risks, and impact, ultimately driving innovation while reducing expenses,” Sandeen said.

AI: A Double-Edged Sword for Your Budget

AI presents a unique conundrum, particularly the dazzling realm of generative AI. While brimming with potential, it can also be a costly endeavor. 

“Companies need a plan to educate their employees on using AI responsibly,” Sandeen said. “Loading valuable intellectual property into a random tool can erode your competitive edge, and using chatbots without guardrails can lead to legal issues.”

Sandeen emphasizes the importance of understanding AI capabilities and choosing the right tools for the job. 

“Microsoft, AWS, Google, and OpenAI have built amazing generative AI platforms,” Sandeen said. “But some customers want to go further, implementing AI in their cloud environment with their own data and security standards.”

Continuous Cloud Optimization: A Marathon, Not a Sprint

Cloud optimization isn’t a one-and-done affair; it’s an ongoing journey. 

“Just as business needs evolve, so should your cloud and licensing strategies,” Sandeen said. 

He points to the Well-Architected Frameworks from AWS and Microsoft as valuable companions on this journey. According to Sandeen, these frameworks offer a set of evolving best practices applicable to any workload. 

“From whiteboarding sessions to API-driven solutions that validate thousands of controls in real-time, these frameworks have matured significantly,” Sandeen said. “Our goal is to ensure clients continuously reap the benefits of cloud cost savings.”

Security: The Unsung Hero of Cloud Optimization

In the cloud, security is non-negotiable. 

“It’s everyone’s job, and it should be incorporated from the beginning,” Sandeen said. He observes that cost and security often top the priority list for businesses. “But if cost is initially the primary focus, security often rises to the top as we assess and review potential vulnerabilities.”

Layered security and regular checkups are crucial to identify and prioritize remediation plans. 

“Misconfiguration is a major risk in the cloud, but most issues can be easily identified and fixed,” Sandeen said. He also highlights the need for compensating controls to protect the environment in case of a security breach.

The Future of IT Cost Optimization: AI-Powered and Nimble

Gazing into the crystal ball, Sandeen sees AI and machine learning playing an even more prominent role in IT cost optimization. 

“We’re already using AI to assess, identify, and suggest remediation strategies for costs and security,” Sandeen said. “Whether it’s chatting with security logs or predicting cost increases, AI is transforming how we work.”

However, Sandeen acknowledges the challenges of keeping pace with technology’s relentless march. He anticipates increased adoption of AI agents in the near future, which has the potential to dramatically amplify productivity.

But with these advancements come new perils. 

“New features are released weekly, and what was science fiction 18 months ago is reality today. However, bad actors also have access to AI,” Sandeen said. “It’s cheaper and easier than ever to do bad things. Businesses need to be even more vigilant in their security.”

Preparing for the Future, Embracing Agility

The advice to businesses is concise and direct.

“Stay up to date and remain agile,” Sandeen said. “Regularly review your cloud strategies, automate where possible, and lean on an expert partner where needed. Strategic optimization and efficiency can maximize innovation and cost savings.”

In the final analysis, taming the cloud beast requires a holistic approach, a keen understanding of AI’s potential and pitfalls, a commitment to continuous optimization, and an unwavering focus on security. Both in-house and with external expert guidance and solutions, businesses can confidently embrace the cloud and AI, driving innovation and achieving their business goals without falling prey to excessive IT costs.

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The Blurring Lines Between AI Academia and Industry https://dataconomy.ru/2024/10/22/blurring-lines-ai-academia-industry/ Tue, 22 Oct 2024 13:51:51 +0000 https://dataconomy.ru/?p=59426 The world of AI research is in constant flux, with breakthroughs emerging at a dizzying pace. But where are these advancements happening? While universities have traditionally been the hotbed of scientific discovery, a significant shift is underway. Increasingly, big tech companies play a pivotal role in AI research, blurring the lines between academia and industry.  […]]]>

The world of AI research is in constant flux, with breakthroughs emerging at a dizzying pace. But where are these advancements happening? While universities have traditionally been the hotbed of scientific discovery, a significant shift is underway. Increasingly, big tech companies play a pivotal role in AI research, blurring the lines between academia and industry. 

In 2019, 65% of graduating North American PhDs in AI opted for industry roles, a significant jump from 44.4% in 2010. This trend highlights the growing influence of industry labs in shaping the future of AI.

To understand this evolving landscape, I spoke with Shakarim Soltanayev, a Research Scientist at Sony Interactive Entertainment and a former Research Engineer at Huawei. His insights shed light on the motivations, benefits, and challenges of conducting AI research within a large company and how this interplay with academia drives innovation.

Why Companies Embrace Academic Publishing

Tech giants like Google, Meta, Microsoft, and NVIDIA publish research at academic conferences for various reasons.

“First and foremost, publishing research at conferences can be a powerful marketing tool for companies,” Soltanayev said. “These publications serve as a form of indirect marketing, demonstrating the company’s technical prowess and commitment to advancing the field. This boosts their brand image within the research community and in the eyes of customers, partners, and investors. These publications help companies stand out from competitors and strengthen their overall market presence.”

The role that publishing plays in talent acquisition is vital.

“Top-tier conferences such as NeurIPS and CVPR are a prime venue for networking with leading researchers and engineers and recruiting promising students,” Soltanayev said. “By showcasing their work, research laboratories such as Google Deepmind and Meta AI can attract the brightest minds in the field, as top talent often wants to work on groundbreaking problems with access to high-quality resources and collaborators.”

A Two-Way Street: The Exchange of Value

The relationship between academia and industry is not one-sided; it’s a dynamic exchange of knowledge and resources that benefits both sides.

“A great example of academic research directly influencing industry is the development of the convolutional neural network (CNN) architecture,” Soltanayev said. “It was pioneered by Yann LeCun and his colleagues in the academic space, and it has had a major impact on tech products, particularly in computer vision. When AlexNet, a CNN-based model, won the ImageNet competition in 2012, it sparked widespread adoption in the industry. Nowadays, CNNs have a wide range of applications, including image recognition for facial identification and object detection, medical imaging for disease diagnosis, and autonomous vehicles for real-time object recognition.”

On the other hand, the industry has significantly contributed to academic research in several ways.

“One of the most notable contributions is the development of large-scale datasets and powerful computing frameworks,” Soltanayev said. “For example, companies have released massive datasets, such as those for image recognition, language models, and self-driving car simulations, that have become critical for academic research. These datasets provide the necessary scale for training advanced machine learning models, which would be difficult for most academic labs to collect independently. Industry also drives innovation in hardware and software, with the development of GPUs by NVIDIA and deep learning frameworks like TensorFlow by Google and PyTorch by Meta, now standard tools in academic and industrial research.”

Different Priorities, Different Cultures

As AI advances, academia and industry are taking different paths to prioritize and approach these developments.

“The main difference between academia and industry research is the focus,” Soltanayev said. “In academia, the priority is often on long-term, fundamental questions that push the boundaries of knowledge. Researchers have the freedom to explore ideas without the pressure of immediate application. In industry, research focuses more on solving real-world problems and creating products, so the timeline is usually shorter, and there’s more pressure to deliver practical results.”

The variations between the two environments significantly influence the cultural dynamics.

“Academia encourages deep exploration, independent thinking, and publishing findings to advance knowledge,” Soltanayev said. “Industry research, on the other hand, is more collaborative, with teams working together to quickly turn ideas into products or solutions. While academic research often provides the theoretical groundwork, industry research pushes innovation by applying these ideas in real-world situations.”

The Allure of Industry Labs

So, why are more researchers pursuing careers in industry labs rather than traditional academic institutions, and what are the advantages and disadvantages of each path?

“Many researchers are choosing to work at big companies due to the attractive compensation packages,” Soltanayev said. “Salaries in industry labs are typically much higher than those in academia, and they often come with additional benefits such as health insurance, retirement plans, and bonuses. In particular, stock options or equity can be a major draw, especially in tech companies where shares have the potential to grow significantly in value. These financial incentives can offer long-term security that’s harder to achieve in academia, where researchers may face grant-based funding cycles and lower salaries, especially in the early stages of their careers. The stability and benefits that big companies provide, combined with the opportunity to work on high-impact, well-funded projects, make industry labs an appealing choice for many.”

Industry research is often focused on achieving specific business goals and developing new products, which can limit researchers’ freedom to explore topics purely for the sake of knowledge.

“In contrast, academia offers the ability to pursue long-term, curiosity-driven projects, which can be deeply rewarding for those passionate about fundamental research,” Soltanayev said. “Academia also encourages the development of independent research programs and the ability to mentor and teach the next generation of scientists, which many researchers find fulfilling. That said, the “publish or perish” culture in academia can create pressure to produce papers frequently, which may sometimes limit the freedom to take big risks or explore novel ideas. Securing funding and tenure positions can also be highly competitive, adding to the stress of an academic career.”

The industry provides superior financial incentives, job security, and access to resources for tackling significant real-world challenges. On the other hand, academia offers greater intellectual autonomy and opportunities for self-directed research. Both paths have their own advantages, and the decision depends on the researcher’s personal motivations—whether they prioritize immediate impact and compensation or a deeper exploration of fundamental ideas.

The Future of Collaboration

Soltanayev envisions an even more intertwined future for academia and industry.

“I see the relationship between academia and industry in AI becoming even more collaborative,” Soltanayev said. “In the future, I expect to see more partnerships between universities and companies, where academic research provides the groundwork for industry to build upon, while companies provide the data, computing power, and funding necessary to drive large-scale experiments and applications. Companies will continue to play a major role in shaping AI’s future, particularly in applied research and development. With their vast amounts of data and access to powerful computing resources, they’re uniquely positioned to accelerate progress in machine learning, natural language processing, and computer vision.”

Organizations will maintain their influence on AI research by contributing to open-source projects, sharing data, and creating new tools and frameworks. This cooperative environment will play a critical role in expediting advancements in AI and ensuring its responsible progress. With the boundaries between academia and industry becoming increasingly indistinct, we can anticipate even more remarkable progress in AI, driven by the collaborative relationship between these two influential entities.

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The future of sports safety: Exclusive interview with Andrey Baykov https://dataconomy.ru/2024/10/15/the-future-of-sports-safety-exclusive-interview-with-andrey-baykov/ Tue, 15 Oct 2024 08:04:54 +0000 https://dataconomy.ru/?p=59300 Quanta Sphere LLC is transforming sports safety with cutting-edge technology. In this interview, Andrey Baykov, the founder, explains how the company’s flagship product, AirGuard, is revolutionizing safety in extreme sports through AI and ML. What was your initial vision for Quanta Sphere LLC, and how has it evolved? My vision for Quanta Sphere LLC came from my background in skydiving […]]]>

Quanta Sphere LLC is transforming sports safety with cutting-edge technology. In this interview, Andrey Baykov, the founder, explains how the company’s flagship product, AirGuard, is revolutionizing safety in extreme sports through AI and ML.

The future of sports safety: Exclusive interview with Andrey BaykovWhat was your initial vision for Quanta Sphere LLC, and how has it evolved?
My vision for Quanta Sphere LLC came from my background in skydiving and aviation. I saw firsthand how safety in extreme sports like skydiving was largely dependent on human factors. This was a major vulnerability, as human error or even small lapses in attention could lead to severe risks. I wanted to create a solution that could reduce those risks by introducing technology that automates key safety measures. That’s how AirGuard was born.

Initially, we focused on skydiving, but as we developed AirGuard, we realized the same approach could be applied to other high-risk sports. Now, our vision has expanded. We aim to provide a versatile tool that enhances safety not just in skydiving but in any sport where human lives depend on fast decisions in unpredictable environments.

What problem does AirGuard solve for athletes, and how does it contribute to their safety?
AirGuard solves the problem of monitoring athletes in real time, even when they are out of sight or out of communication range. In skydiving, for example, if something goes wrong, it can be extremely difficult for ground personnel to detect or understand what happened. Often, by the time help is needed, it’s too late.

AirGuard bridges this gap by continuously collecting data from the athlete’s vitals and the surrounding environment. It uses AI and ML to interpret this data, alerting the athlete when something is wrong and providing real-time insights to prevent accidents. The data collected is objective and reliable, offering a clear picture of what happened in critical moments. By doing so, AirGuard minimizes the guesswork and ensures the athlete gets the right support when needed.

How do AI and Machine Learning improve AirGuard’s performance and safety features?
AI and Machine Learning are central to AirGuard’s ability to protect athletes effectively. The device gathers data during jumps or other extreme activities, such as speed, altitude, and biometric readings like heart rate or oxygen levels. AI helps AirGuard process this data in real time, while ML enables it to detect patterns and learn from past experiences.

The beauty of AI and ML is that they evolve. Every jump, every new piece of data improves the model, allowing AirGuard to get better at predicting potentially dangerous situations. For example, if a jumper is descending at an unusual speed or has abnormal vital signs, AirGuard recognizes this pattern based on previous data and alerts them instantly, giving them time to react and avoid a potential accident.

Can you walk us through the three versions of AirGuard: Light, Standard, and Pro?
Absolutely. We designed AirGuard in three versions to cater to different user needs: Light, Standard, and Pro.

The Light version is tailored for beginners or people just starting in extreme sports. It provides essential safety features—monitoring vital signs and environmental factors—to ensure basic protection. The key advantage here is simplicity. It’s designed to be easy to use without overwhelming the user with too much data.

The Standard version is for more experienced athletes who need a deeper understanding of their performance and the conditions they’re facing. This version gives more detailed insights, including real-time analysis and alerts based on AI and ML. It’s ideal for athletes who want to push their limits while still staying safe.

The Pro version is designed for professionals and high-level athletes. It incorporates the most advanced technology, providing real-time data streaming and performance analytics. The Pro version not only offers enhanced safety but also helps athletes optimize their performance by providing granular insights. As you move up from Light to Pro, the devices also become more compact and lighter, integrating the latest technology to ensure they don’t interfere with the athlete’s activity.

What have you learned from early users of AirGuard, and how has it influenced product development?
We’ve been fortunate to have professional skydivers and athletes test AirGuard in real-world environments. Their feedback has been crucial in refining the product. For instance, we learned that the device needed to be more intuitive, especially during intense, high-pressure moments. We adjusted the alert systems to make them clearer and more actionable, ensuring athletes receive the right information at the right time.

Additionally, we improved the durability of the device based on field tests. We reinforced the hardware to withstand the harsh conditions athletes often face—like extreme temperatures, moisture, and impact. The insights from our testers allowed us to make key updates, many of which have been incorporated into the latest versions of AirGuard. This collaborative approach ensures that our product meets the real-world needs of athletes.

What advice would you give to entrepreneurs looking to innovate in the IoT space?
My primary advice is to stay focused on solving real-world problems. In the IoT space, it’s easy to get caught up in the technology and lose sight of its practical applications. The real value comes from how your product can genuinely improve people’s lives.

Also, stay adaptable. The IoT landscape is constantly evolving, and being open to change is essential. Iterate quickly, gather feedback from your users, and don’t hesitate to refine your product based on that feedback. Collaboration is another key element. Surround yourself with experts in various fields because innovation often happens when different perspectives and ideas come together.

Where do you see Quanta Sphere LLC and AirGuard in five years?
In five years, I see Quanta Sphere LLC as a leader in sports technology, not just in skydiving but across various sports where safety is paramount. AirGuard will be an integral part of high-risk sports like mountain biking, scuba diving, and even team sports where real-time data can make a difference in preventing accidents.

We aim to expand into new markets and gather even more data to improve our AI models. As we grow, I also see us partnering with sports organizations and regulators to help set new safety standards globally. Ultimately, our goal is to make advanced safety technology accessible to athletes at all levels, ensuring that they can focus on their performance, knowing that their safety is in good hands.

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Exclusive: Insights on global AI governance, ethics, and regulation from UN and EU leaders https://dataconomy.ru/2024/10/08/exclusive-insights-on-global-ai-governance-ethics-and-regulation-from-un-and-eu-leaders/ Tue, 08 Oct 2024 11:34:21 +0000 https://dataconomy.ru/?p=58921 The hasty progress of artificial intelligence (AI) technology and its growing influence across many areas of life have sparked significant global discussions on governance, ethics, and regulatory frameworks. At the forefront of these discussions is the EU AI Act—a pioneer regulatory framework that aims to set the standard for these topics across Europe. But this […]]]>

The hasty progress of artificial intelligence (AI) technology and its growing influence across many areas of life have sparked significant global discussions on governance, ethics, and regulatory frameworks. At the forefront of these discussions is the EU AI Act—a pioneer regulatory framework that aims to set the standard for these topics across Europe. But this isn’t just another regulatory effort; it represents a broader vision for shaping the future of AI in a way that ensures fairness, inclusivity, and respect for human rights. As AI technologies and their impact continue to accelerate, it’s becoming increasingly clear that engaging with these regulations is crucial—not just for AI developers but for policymakers, businesses, and society at large.

Dataconomy had the opportunity to speak with key EU and UN leaders to explore the global impact of AI governance in greater detail. These interviews revealed how AI regulation and ethics are unfolding on a global scale, with the EU AI Act playing a critical role. During the Digital Enterprise Show (DES) 2024 in Malaga, Wendy Hall, a UN AI Advisory Board member and prominent UK AI strategist; Carme Artigas, Co-Chair of the UN AI Advisory Body on AI Governance and Inclusion; and Dan Nechita, Head of Cabinet for MEP Dragos Tudorache and lead technical negotiator for the EU AI Act on behalf of the European Parliament, shared their exclusive insights with us on how AI governance, ethics, and regulation are being shaped in real-time.

Bridging the global AI divide

Exclusive: Insights on global AI governance, ethics, and regulation from UN and EU leadersWendy Hall, a UN AI Advisory Board member and prominent UK AI strategist, strongly advocates for a globally collaborative approach to AI policy. During our discussion, Hall emphasized that while AI presents vast opportunities, the strategies employed by different nations vary widely. For instance, the UK has taken a more comprehensive, policy-driven approach to AI development. Beginning in 2017, the UK government recognized AI’s potential for economic growth and job creation, positioning the country as a leader in AI governance. At a time when Brexit consumed political focus, the UK still managed to work on AI policy. Hall notes that the UK’s early engagement helped establish its prominence, but she’s quick to point out that other countries like the US and China have followed distinctly different paths.

In the US, the focus has largely been on empowering tech companies like Google and OpenAI to push AI boundaries, leaving governance in the hands of the private sector. Conversely, China has taken a centralized, state-driven approach, with the government maintaining control over AI’s strategic direction. These divergent strategies, Hall explains, highlight the complexity of global AI governance and the need for more cohesive international policies.

Yet, Hall’s primary concern isn’t the divergence between these leading nations but rather the unequal access to AI technologies across the globe. She emphasizes the need for equitable AI development, particularly for countries outside the wealthy West. Regions like the Global South, which often lack the infrastructure and resources to keep pace with AI advancements, risk being left behind. Hall states this divide could deepen existing global inequalities unless capacity-building initiatives are implemented.

“These regions need more than just access to AI technologies—they need the infrastructure, talent, and data to develop AI systems suited to their own needs,” Hall stresses. This could include providing countries in the Global South with access to high-performance computing systems, datasets, and the technical expertise needed to build AI models locally. Hall advocates for global initiatives offering the tools and resources necessary for these countries to participate actively in the AI revolution rather than passive consumers of technology developed elsewhere.

“There’s a risk that AI could deepen global inequalities if we don’t ensure equitable access to the necessary infrastructure and talent”

Elena Poughia with Wendy Hall
Elena Poughia with Wendy Hall at Digital Enterprise Show 2024

A particular concern for Hall is the rapid and unchecked development of generative AI models, such as OpenAI’s GPT-4. While these models offer groundbreaking possibilities, they also pose significant risks in the form of misinformation, disinformation, and ethical misuse. Hall is cautious about the unintended consequences of such powerful technologies, noting that generative AI can produce convincing but entirely false content if not carefully regulated.

She draws attention to the broader implications, explaining that while earlier AI technologies like automation primarily focused on improving efficiency, generative AI directly impacts knowledge creation and dissemination. “We’ve seen this with misinformation online—if the data going in is flawed, the output could be damaging, and at a scale that we’ve never dealt with before,” Hall warns. The stakes are high, particularly when AI technologies influence decisions in critical sectors like healthcare, law, and finance.

For Hall, the solution lies in advocating global partnerships aimed at creating robust ethical standards and governance frameworks. She advocates for establishing international agreements to ensure that AI technologies are developed and deployed responsibly without contributing to societal harm. Hall points to the importance of involving diverse stakeholders, including governments, private companies, and civil society organizations, to establish regulations that balance innovation with public safety.

Hall’s perspective underscores a critical point: AI could exacerbate existing global inequities and introduce new ethical dilemmas without collaboration and shared governance. Hall’s call for capacity building and ethical oversight isn’t just a recommendation—it’s a necessary step to ensure AI is developed to benefit humanity as a whole, not just a select few.

Ensuring inclusive AI governance

Exclusive: Insights on global AI governance, ethics, and regulation from UN and EU leadersCarme Artigas, Co-Chair of the UN AI Advisory Body on AI Governance and Inclusion, brings a critical perspective to the conversation about AI’s global development—one focused on the glaring disparities in how different nations are included in discussions about AI governance. Artigas stresses that the current frameworks governing AI, including initiatives led by the G7, UNESCO, and the OECD, are largely dominated by wealthier, more technologically advanced nations, leaving out key voices from the Global South. “Many countries in the Global South are not even invited to the table,” Artigas points out, referring to the global discussions that shape AI’s future. In her view, this exclusion is a major governance deficit and risks creating a new form of digital colonialism. As AI technologies advance, those countries that lack the resources or influence to participate in international AI policymaking could be left even further behind. For Artigas, this isn’t just a matter of fairness—it’s a fundamental risk to global stability and equality.

Artigas highlights the need for a governance model that goes beyond the traditional frameworks of regulatory bodies. Rather than creating a single new international agency to oversee AI governance, she advocates for leveraging existing institutions. “We don’t need more agencies; we need better coordination between the ones that already exist,” she explains. Organizations such as the ITU (International Telecommunication Union), UNICEF, and WIPO (World Intellectual Property Organization) are already deeply involved in AI-related issues, each within their own sectors. What’s missing is a coordinated approach that brings together these specialized agencies under a unified global governance structure.

“True governance must go beyond mere guidelines and include mechanisms for accountability”

Elena Poughia with Carme Artigas at DES 2024
Elena Poughia with Carme Artigas at DES 2024

Artigas’s vision is one where AI is governed in a way that respects international law and human rights and ensures that all countries—regardless of their technological standing—have equal access to the benefits AI can bring. This includes providing the necessary tools and resources for countries currently excluded from AI advancements to catch up. She notes that the private sector and academia also have a role in helping democratize access to AI technologies.

However, Artigas points out that ethical guidelines alone are not enough. While many companies have developed their internal ethical frameworks, she argues that these are often voluntary and unenforceable. True governance, she asserts, must go beyond mere guidelines and include mechanisms for accountability. Without clear consequences for unethical AI development or deployment, the risks of misuse and harm—particularly for vulnerable populations—remain high.

One of the key issues Artigas raises is the role of AI in exacerbating the digital divide. If not properly regulated, AI could further entrench existing inequalities, with wealthier nations gaining more economic and technological power while poorer nations fall further behind. For her, the goal of AI governance must be to close this divide, not widen it. “AI has the potential to be a great equalizer, but only if we ensure that its benefits are shared equally,” she emphasizes.

Artigas’s focus on inclusivity and coordination in AI governance reflects the growing recognition that AI is a global issue requiring global solutions. Her call for a unified approach—where existing agencies work together to govern AI—underscores the need for a more inclusive, ethical, and accountable system that benefits all of humanity, not just a select few.

Balancing innovation and regulation

Exclusive: Insights on global AI governance, ethics, and regulation from UN and EU leadersDan Nechita, Head of Cabinet for MEP Dragos Tudorache and the lead technical negotiator for the EU AI Act brings a pragmatic yet forward-thinking perspective to the discussion of AI governance. As one of the key figures behind the EU AI Act, Nechita emphasizes the importance of balancing innovation with the need for robust regulation to ensure AI technologies are developed and used safely.

According to Nechita, the EU AI Act is designed to set clear rules for AI systems, particularly those considered high-risk, such as AI used in healthcare, education, law enforcement, and other critical sectors. “This isn’t just about regulating the technology itself,” Nechita explains. “It’s about protecting fundamental rights and ensuring that AI doesn’t exacerbate existing societal problems, like discrimination or privacy violations.”

One of the standout features of the EU AI Act is its emphasis on risk management. Nechita explains that AI systems are classified based on the level of risk they pose, with the highest-risk systems subject to the strictest regulations. This tiered approach allows for flexibility, enabling Europe to maintain its leadership in AI innovation while ensuring that the most sensitive applications are thoroughly regulated. For Nechita, this balance between innovation and regulation is crucial to maintaining Europe’s competitiveness in the global AI landscape.

Yet, Nechita acknowledges that implementing the EU AI Act is a complex and ongoing process. One of the challenges is ensuring that all 27 EU member states, each with their own national priorities and strategies, adhere to a unified regulatory framework. The EU AI Act requires cooperation between governments, industry leaders, and regulatory bodies to ensure its success. “We’re fostering a continuous feedback loop between companies and regulators, ensuring AI systems evolve safely while remaining compliant as new technologies emerge,” Nechita explains. “We’re not just handing companies a set of rules and walking away. We’re asking them to work with us continuously, to test their systems, report issues, and ensure compliance.”

“AI will transform the world, and we must guide it in a direction that benefits everyone”

Exclusive: Insights on global AI governance, ethics, and regulation from UN and EU leaders
Dan Nachita on the stage explaining the EU AI Act’s implications for European enterprises

Nechita also points out that the EU AI Act is not just about creating static regulations. The Act includes provisions for continuous updates and revisions as AI technologies evolve. He argues that this dynamic approach is essential because AI is a fast-moving field, and regulations must keep pace with new developments. This is why the EU AI Act encourages ongoing dialogue between AI developers and regulators, fostering a relationship where both innovation and safety can coexist.

However, Nechita is also mindful of the broader global context. While the EU has taken a proactive stance on AI regulation, other regions, particularly the US and China, have different approaches. In the US, AI regulation is more fragmented, with companies largely self-regulating, while China’s state-controlled AI development prioritizes national interests over individual rights. Nechita acknowledges that achieving global consensus on AI governance will be difficult, but he sees potential for collaboration in areas like AI safety, sustainability, and ethical standards.

Nechita envisions an AI governance model that balances innovation with public safety. He believes the EU AI Act, focusing on risk management, transparency, and continuous collaboration, offers a model for how other regions might approach AI regulation. At the same time, he stresses the need for global cooperation, particularly in addressing AI’s ethical and societal implications.

As the EU AI Act continues to take shape, Nechita remains optimistic about its potential to set a global standard for AI governance: “AI is going to change the world, and we need to make sure it changes for the better,” he concludes. His approach reflects a nuanced understanding of the challenges ahead and a strong belief in the power of regulation to guide AI development in a direction that benefits society.

Dan Nechita is scheduled to speak at the Data Natives 2024 event in Berlin on October 22-23; the event’s theme is “2050: The ‘Good’ AI Symposium.”

A unified vision for the future of AI

Wendy Hall, Carme Artigas, and Dan Nechita’s insights reflect a crucial turning point in AI governance as we watch AI evolve at an unprecedented pace. Their perspectives converge on one undeniable truth: AI isn’t just a technological breakthrough; it’s a force that has to be firmly steered away from benefiting the few at the cost of the many.

The urgent need for global capacity building and ethical controls of AI is also being called for by Wendy Hall, who asks us to bridge the growing gap between the capabilities in this area between developed and developing nations. However, Camre Artigas’s focus on inclusivity and accountability reminds us that the enforcement that precedes any governance should be part and parcel. EU AI Act is a worthy example of balancing innovation with safety and, thus, how other regions may approach AI governance.

Together, these voices paint a holistic picture of what’s needed to shape AI’s future: focus on collaboration, human rights protection, and a strong framework that encourages innovation while protecting public interests. It’s an incredibly tough road ahead but also one with tremendous potential. AI’s future is now, and it’s up to us to make it happen right.

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Gene-editing tech is set to revive dodo and many other extinct species by 2028 https://dataconomy.ru/2024/10/08/dodo-de-extinction-by-gene-editing/ Tue, 08 Oct 2024 08:41:11 +0000 https://dataconomy.ru/?p=58920 Gene-editing breakthroughs are setting the stage for a leap in de-extinction efforts, with scientists aiming to bring back extinct species such as the dodo, wooly mammoth, and Tasmanian tiger by 2028. Colossal Biosciences, a pioneering biotech company, has been at the forefront of this initiative, developing the technology needed to replicate the DNA of extinct […]]]>

Gene-editing breakthroughs are setting the stage for a leap in de-extinction efforts, with scientists aiming to bring back extinct species such as the dodo, wooly mammoth, and Tasmanian tiger by 2028.

Colossal Biosciences, a pioneering biotech company, has been at the forefront of this initiative, developing the technology needed to replicate the DNA of extinct species using close living relatives.

Based in Texas, Colossal Biosciences has raised $235 million to fund its ambitious de-extinction projects, with support from notable figures such as Chris Hemsworth, Paris Hilton, and Tony Robbins. The company, co-founded by tech entrepreneur Ben Lamm and Harvard geneticist George Church, is focused on reviving species by identifying and editing key “core” genes that define these animals.

Dodo and others might revive by 2028

Ben Lamm, Colossal’s CEO, has indicated that the Tasmanian tiger and dodo may reappear before the mammoth due to their shorter gestation periods. While the woolly mammoth requires a 22-month gestation, the Tasmanian tiger’s is just weeks, and the dodo’s is about a month.

This timeline positions Colossal to potentially revive one of these species well before the mammoth’s expected return.

Beyond de-extinction, Colossal is also driving conservation efforts. The company recently established the Colossal Foundation, securing an additional $50 million to protect endangered species such as the vaquita porpoise and the northern white rhino.

The technologies developed for de-extinction are being shared with conservation groups to aid in species preservation and bolster biodiversity.

dodo de extinction by gene editing
Scientists aim to revive extinct species like the dodo, woolly mammoth, and Tasmanian tiger by 2028 using gene-editing technology

How and why reviving extinct species will help us?

Reviving extinct species, often called de-extinction, holds potential benefits for science, ecology, and even humanity’s future.

Many extinct species played critical roles in their ecosystems. Their absence has disrupted natural processes like predation, grazing, and seed dispersal, which can lead to the collapse of ecosystems. Reviving key species could help restore balance to these ecosystems and improve their health.

For instance, the reintroduction of the woolly mammoth or a close genetic relative to the Arctic tundra could help restore ecosystems by trampling down shrubs, allowing grasslands to flourish, which in turn sequesters carbon and slows climate change.

De-extinction can also boost biodiversity, which is crucial for maintaining healthy ecosystems. Biodiversity strengthens resilience to changes, such as climate shifts, disease outbreaks, and habitat loss. Reviving extinct species also provides an opportunity to repopulate endangered or severely depleted ecosystems, enhancing their complexity and stability.


Google’s AlphaFold 3 AI system takes on the mystery of molecules


Restoring the Tasmanian tiger (thylacine) to its natural habitat in Australia could help control populations of invasive species, as it once played a role as a top predator.

The gene-editing technologies developed for de-extinction can benefit the conservation of currently endangered species too. These techniques can be used to genetically strengthen species against diseases or environmental changes or even allow them to adapt to changing climates.

Humans learn by examining and the effort to bring back extinct species could lead to groundbreaking discoveries in genetics, biology, and ecology. By reviving extinct animals, scientists will gain invaluable insights into evolutionary processes, species adaptation, and how ecosystems functioned in the past. This deeper understanding could be applied to help modern species survive in rapidly changing environments.

How about us?

Gene editing in human research is heavily restricted or outright illegal in many countries due to a combination of ethical, safety, and societal concerns. While technology, such as CRISPR, holds enormous potential to treat or even cure genetic diseases, there are several reasons why it remains controversial and regulated.

Why is gene-editing illegal for human research?

Altering the human genome, especially in ways that affect future generations (germline editing), raises profound ethical questions. Many argue that it could lead to unintended consequences, such as “designer babies,” where parents select traits like intelligence, physical appearance, or athletic ability. This could exacerbate inequalities and create ethical dilemmas over what constitutes an “ideal” human.

Gene editing carries the risk of off-target effects, where unintended parts of the genome might be altered. These mistakes could lead to unforeseen health problems, including the potential for new diseases or harmful mutations. The long-term impacts of altering the human genome, especially in future generations, are still largely unknown, posing significant safety concerns.

Germline gene editing affects not just the individual but also future generations, who have no ability to consent to these changes. This creates a major ethical issue, as it could lead to unintended harm or significant genetic alterations in human evolution that cannot be reversed.

Many bioethicists worry that gene editing for non-medical purposes could revive discredited eugenic practices, where selective breeding or genetic engineering is used to favor certain traits, leading to social divisions and discrimination based on genetic “desirability”.

dodo de extinction by gene editing
De-extinction efforts could restore ecosystem balance, bringing back species that played crucial ecological roles

Gene editing, especially in humans, is a complex and rapidly evolving field. Governments and regulatory bodies are still grappling with how to adequately monitor and control its use to prevent unethical practices or unintended consequences. In most places, laws and regulations have been put in place to ensure that gene editing in humans is only conducted under very strict conditions, if at all.

Illegal or unethical gene-editing experiments, such as the case of Chinese scientist He Jiankui, who edited the genomes of twin girls in 2018, have caused public outcry. These incidents undermine trust in the scientific community and lead to fears that unregulated gene editing could harm public health and safety.

So, while gene editing offers great promise for medical advancements, legal restrictions are in place to ensure that any human research adheres to rigorous ethical standards, prioritizes safety, and avoids irreversible harm to future generations or society at large.

The next few years could see the return of creatures long thought to be consigned to history, as science and technology converge to rewrite the future of biodiversity.


Image credits: Emre Çıtak/Ideogram AI

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Redefining media monitoring: Joe Hamman on AI, competitor insights, and the future of brand intelligence https://dataconomy.ru/2024/10/01/redefining-media-monitoring-joe-hamman-on-ai-competitor-insights-and-the-future-of-brand-intelligence/ Tue, 01 Oct 2024 12:02:43 +0000 https://dataconomy.ru/?p=59883 From humble beginnings to being the head of a multi-national media monitoring agency, Joe Hamman learnt from an early age how a strong work ethic, coupled with a clear vision can lead to success. Not precocious by choice, Joe had to jump in and support his family with menial jobs from the age of 13, […]]]>

From humble beginnings to being the head of a multi-national media monitoring agency, Joe Hamman learnt from an early age how a strong work ethic, coupled with a clear vision can lead to success.

Redefining media monitoring: Joe Hamman on AI, competitor insights, and the future of brand intelligenceNot precocious by choice, Joe had to jump in and support his family with menial jobs from the age of 13, waiting tables and even doing a stint as security guard at Ellispark. “These jobs were not without merit, because they taught me the value of hard work and taking control of my own future,” Joe says. Seven years later he was invited by a stockbroker to head into the world of sales. It was not a successful venture, but by chance, Joe met one of Old Mutual’s top financial advisors who took the young man under his wing. “The advisor saw something in me and took me under his wing, teaching me how to negotiate the maze of successful marketing.” This mentorship would be transformative and usher in the second part of his career and soon Joe was headhunted by McGregor BFA, a pre-eminent provider of stock market fundamental research data and news to the financial sector and corporate market.

“That is where I discovered the world of media monitoring, where big companies want to know what the media is reporting on them and what their competitors are up to. I realized that the industry was poorly managed, as companies saw media monitoring as a grudge purchase, with little real value return. Most companies would attempt to go it alone, looking for news about themselves, but with no analysis, deriving no benefit from the info they gleaned,” he says. It was the perfect opportunity to turn the industry on its head and bring a fresh perspective and unique business model to the market.

After working in the industry for just under a decade, Novus Group was started in 2014, a risky move in which Joe put everything on the line, but persevered through hard work and with a sound business plan. The company today employs close to 100 employs and serves more than 700 customers.

Media monitoring to Joe means helping your customer and solving problems – his favourite aspect of the industry. “We help people and companies by finding out what the media is reporting on them in near-real time, and we solve problems by providing advice, suggestions and then developing solutions for their problems,” he explains.

With the media monitoring landscape becoming increasingly sophisticated, Joe thinks it will not be long before media monitoring becomes part of a company’s operational landscape.

“We see the integration of AI and automation transforming media monitoring, giving companies the benefit of gaining deeper insights into public perception. Media monitoring will also become more relevant across different industry sectors, allowing businesses to analyse consumer discussions and trends, and so tailor their strategies to meet demands,” he says.

This also means a growing need for media monitoring to customise solutions in obtaining relevant information in a timely manner. “Media monitoring will find increasingly innovative ways to integrate technology across social media platforms. Cross-platform analysis will thus become vital to organisations wanting to gain a holistic overview of what is being said about their brands,” Joe says.

One of the ways to leverage media monitoring is through competitor media monitoring, he says. “This can be an effective proactive tool for a business looking to gain insight into today’s dynamic market landscape, since it allows a systematic approach to analysing large amounts of data, which allows them to understand competitors’ media presence. In this way, businesses can refine their own approaches and identify opportunities to showcase their strengths. Media monitoring also allows them to identify areas where the business falls short and position services accordingly.  In fact, seeing which media, influencers and platforms a company’s competitors are using, can enhance their own targeting efforts,” Joe says.

Harnessing media monitoring can be advantageous to businesses on many levels, Joe says. “The focus is on creating a measurable impact, instead of just sending out press releases into the void. Media monitoring tools can help business quantify efforts, while providing insight on how to drive future strategies. As platforms continue to evolve and trends change, business must embrace lifelong learning, and by using platforms offering courses that include how to optimize content and master industry-specific digital marketing tools, businesses can take that next step. By understanding search engine optimization, companies will elevate the reach and impact of their content, increasing visibility while ensuring the right message get out and seen. Lastly, networking is a critical skill to have. Leveraging platforms like LinkedIn, attending industry conferences, and joining industry-centric forums all must form part of the modern growth strategy. Relationships forged today can yield invaluable benefits tomorrow,” says Joe.

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Future insights and challenges in data analytics with Aksinia Chumachenko https://dataconomy.ru/2024/09/27/future-insights-and-challenges-in-data-analytics-with-aksinia-chumachenko/ Fri, 27 Sep 2024 12:11:04 +0000 https://dataconomy.ru/?p=58589 The global data analytics market is forecasted to increase by USD 234.4 billion from 2023 to 2028. This rapid increase will accelerate the growth of jobs in the field. To learn more about the trends of data analytics fields, their prospects, and their challenges, we talked to Aksinia Chumachenko, Product Analytics Team Lead at Simpals, […]]]>

The global data analytics market is forecasted to increase by USD 234.4 billion from 2023 to 2028. This rapid increase will accelerate the growth of jobs in the field.

To learn more about the trends of data analytics fields, their prospects, and their challenges, we talked to Aksinia Chumachenko, Product Analytics Team Lead at Simpals, Moldova’s leading digital company. In this interview, Aksinia will share her journey, approach to leadership and mentorship, and vision for the future of this rapidly evolving field.

Your journey from a university student to a Product Analytics Team Lead is inspiring. Could you share the key milestones that have shaped your career in data analytics?

Future insights and challenges in data analytics with Aksinia ChumachenkoMy journey began at NUST MISiS, where I studied Computer Science and Engineering. I studied hard and was a very active student, which made me eligible for an exchange program at Häme University of Applied Sciences (HAMK) in Finland. This experience has led to my first real IT job ― an internship at Renault in 2019. It was my first job as a data analyst. It helped me to become familiar with popular tools such as Excel and SQL and to develop my analytical thinking.

The time I spent at Renault helped me realize that data analytics is something I would be interested in pursuing as a full-time career. After my time at Renault, I joined Sberbank, one of the largest banks in Eastern Europe, as an intern analyst through their highly competitive Sberseasons program. The competition was intense, with over 50 applicants per position. However, three different teams within the bank were interested in hiring me, and I ultimately chose to work with Sberbank CIB, which is responsible for the corporate investment business.

At Sberbank, I worked as an analyst for major B2B clients. This experience helped me to improve my Python skills and get more practical experience working with big data.

In 2020, I transitioned to product analytics at OZON Fintech ― one of the leading marketplaces in Russia. This pivotal role allowed me to double my salary and gain extensive experience working on fintech products. At OZON, I worked with four financial products, and through my data-driven research, we significantly increased key metrics such as usage, number of new customers, returns, and revenue.

In November 2020, BCS Investments, named “Investment Company of the Year” by an authoritative financial online platform, approached me. They were looking to hire their first product analyst and build a new department from scratch. That opportunity fitted with my goals, as I wanted to gain new leadership skills. During my time there, I implemented numerous impactful initiatives. One of the most significant was introducing the A/B testing process from scratch, which improved user experience and product metrics. Thanks to the company-wide implementation of this A/B testing process, we increased the onboarding conversion rate in our app by several percentage points, ultimately impacting the number of customers using the app and, consequently, our revenue.

About a year later, I transitioned to Simpals in Moldova, where I still work as a Product Analytics Team Lead. I manage a team of top-notch data analytics experts and work on one of the most visited websites in Moldova.

Recently, I have been highly involved in giving back to the community. I organized a meetup in Moldova in 2023 and was also a speaker. One of the speakers was a colleague whom I mentored from scratch ― it was a huge pleasure to see how much she’s grown quickly.

I am also a judge in several international hackathons, including the United Nations Big Data Hackathon, where I evaluated 18 different teams based on their solutions’ innovation, quality, and applicability.

Other hackathons to which I was invited as an expert are the MLH Web3Apps Hackathon and MLH Data Hackfest.

As a leader in your field, how do you approach mentoring your team members, and what impact do you hope to have on their careers?

I started mentoring as soon as I had my team. Today, I mentor not just within Simpals but also external organizations such as Women in Tech and Women in Big Data. These are free international programs that help women progress in their careers. As a mentor, I’ve helped several women achieve significant success by leveling up or starting a new career.

Every mentee is different, which is why I create individual development plans based on their goals, strengths, and weaknesses. We also meet regularly for one-on-one meetings to discuss how things are going.

Seeing my impact on colleagues is very rewarding. Moreover, by helping others, I also help myself grow as a professional and a human being.

Aksinia, as the Product Analytics Team Lead at Simpals, a company that has a significant impact on Moldova’s digital ecosystem, what role does data analytics play in the success of digital platforms like 999.md?

999.md is visited by more than 2 million unique users every month, giving us much data to work with. I was responsible for building a team from scratch and leading them to ensure the growth of key metrics and optimize existing processes. Thanks to the adjustments to the key features, we have achieved a 13% revenue increase.

Thanks to our work, the platform can gain more revenue and reduce spending where possible. This is what analytics does: not only does it help to make more money, but it also prevents unnecessary spending, which, for large projects like this, can be significant.

The field of data analytics is constantly evolving. What are the biggest challenges facing product and data analytics today?

Data accumulates fast, and it’s challenging to collect and analyze it. However, even more importantly, the insights generated need to be aligned with the company’s overall strategy and goals. Ask a question: will completing this task drive you to achieve your business goals? Sometimes, data analysts forget to ask themselves this question. But I think it’s crucial to have a business mindset.

Also, many IT professionals find it hard to stay up-to-date with rapidly changing technologies. To stay up-to-date, I regularly attend conferences (sometimes as a speaker). My mentor also helps me constantly grow and explore new things.

You mentioned the importance of aligning data analytics with business strategy. Please give us an example of how this alignment has worked in your role at Simpals.

My team’s task was optimizing the user experience on 999.md. We needed to increase user engagement and conversion rates by making the platform more intuitive and user-friendly. Here is what we did:

  • identified pain points in the user journey;
  • used user segmentation to understand better how different groups use the platform;
  • conducted A/B testing to compare different platform versions and see which changes led to better outcomes.

I discussed earlier how important it is to align data analytics with business goals. The insights we gained allowed us to increase revenue and boost customer satisfaction.

The integration of AI and machine learning into analytics is a hot topic right now. How do you see these technologies shaping the future of data analytics?

AI and machine learning are basically omnipresent. There isn’t a single field where these technologies aren’t used. These technologies also allow us to automate complex data processes. This saves time on ‘manual labor’ and allows us to dedicate more time to problem-solving and creativity.

In the future, we’ll see AI and machine learning becoming even more integral to data analytics, with more sophisticated models and tools that can handle increasingly complex tasks. These technologies work best in synergy with human creativity, not as a replacement. A deep understanding of the data and the business context is still essential for making the most of what AI and machine learning offer.

Given your experience and recognition in the field, including judging international hackathons and the UN Big Data Datathon, how do you see the global data analytics landscape evolving in the coming years?

The role of analysts will gradually change and expand. For example, a trend that I see right now on the market is that analysts must have product management skills, as they need to have a deep understanding of working with data and product knowledge to make decisions.

Another important change is that the new technologies are greatly accelerating the work with data. What used to take days or weeks can now be done in a few hours. For example, Google’s BigQuery cloud data warehouse, which many companies use, is already introducing new tools that make life easier for analysts, such as searching for insights based on a specific table and monitoring data quality.

However, it is important to realize that AI will not replace analysts completely. On the contrary, it will become a powerful tool that will allow you to focus on more complex and strategic tasks. The role of humans in analytics is still very important. Soft skills such as critical thinking and the ability to communicate and negotiate with different people are some crucial things that AI can’t replace.

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Insights from Yuriy Golikov on building a developer community https://dataconomy.ru/2024/08/29/insights-from-yuriy-golikov-on-building-a-developer-community/ Thu, 29 Aug 2024 07:06:46 +0000 https://dataconomy.ru/?p=57427 In this interview, Yuriy Golikov shares his journey from a web developer to creating a supportive developer community. With a background in software development, Yuriy discusses how he brought together like-minded professionals to form a thriving network. He offers insights into the challenges and rewards of building this community and its impact on his career […]]]>

In this interview, Yuriy Golikov shares his journey from a web developer to creating a supportive developer community. With a background in software development, Yuriy discusses how he brought together like-minded professionals to form a thriving network. He offers insights into the challenges and rewards of building this community and its impact on his career and the tech industry.

Insights from Yuriy Golikov on building a developer communityYuriy, tell us a little about your professional path and how you came to create DevBrother.

My path in the technological field began with receiving a master’s degree in computer science. After graduation, I worked as a web developer for several companies, such as DDI Development and Ritarsoft, and took on freelance projects.  These experiences provided me with valuable insights into various aspects of software development.

While freelancing, I organized a coworking space for software engineers to surround myself with like-minded individuals with similar values and work interests. This led to the creation of the Software Engineering community in Ukraine – CoWorkingClub (now TalenBankAI), where I began to grow my network of software engineers.

As my network expanded, people approached me with their software engineering needs. Given my background in the field, it was a natural progression to launch the DevBrother Company. I saw a significant need for high-quality software development services.

Today, my network has grown to over 30,000 developers, and DevBrother now has a team of 100 developers. We are officially established in Poland, the USA, and Ukraine and provide various development services.

Tell us about DevBrother. What makes your company unique?

Here are the things that make DevBrother unique, in my opinion:

  1. Organic Growth: DevBrother has grown organically from a developer community, forming a core tech team and a database of over 30,000 developers.
  2. Unique Service Model: This foundation allows DevBrother to offer a unique combination of outsourcing and outstaffing services.
  3. Industry Expertise: DevBrother stands at the intersection of deep expertise in industries like Healthcare, Blockchain, and AI.
  4. Values-Driven Impact: DevBrother significantly impacts lives across different countries by offering not just highly qualified tech expertise but also a values-driven approach. We foster a supportive environment where each team member’s personal and professional growth is prioritized. Our long-term relationships are built on trust, emotional intelligence, and a commitment to both technical and business excellence. This culture extends to our clients, creating a partnership based on safety, positive emotions, and reliability. Our growth, from 50 to 100 team members in just one year, reflects this strong foundation.
  5. Positive Social Impact: We are dedicated to projects that have a positive social impact, contributing our expertise to initiatives we believe in.

You are also known as a startup advisor and angel investor. How do you help new companies?

I always aim to support startups in their early development stages. My role is to help teams build projects, attract technical talent, and provide strategic consulting. I pay special attention to healthcare and blockchain projects, as these fields have excellent innovation potential.

Also, with some companies not funded enough, we are working as dev partners, having shares in their companies. This way, we support interesting early-stage projects, in fact, by incubating them. We have many qualified resources interested in participating in those projects.

I provide advisory and investment help for some companies, as they have strong backgrounds in their area but need an experienced and skilled dev partner.

What are your main achievements and projects at DevBrother?

Among our most significant achievements is successfully implementing over 50 startups and technical products. We are proud that our company has become a reliable partner for many customers in various industries, providing them with high-quality solutions and support.

Many DevBrother clients started as small companies, but having my team around could grow several times over the years. Due to the signed NDA, I can’t share most of the names, but industry leaders such as PurplePass, Animoca Brands, CryptoBriefing, and many more.

Tell us about your developer community and the TalentBankAI.com platform.

I lead a dynamic developer community of 30,000 people. Our TalentBankAI.com platform facilitates direct interaction between companies and qualified freelancers. We create conditions for seamless cooperation and innovative solutions, which help businesses quickly find the right talent.

What is your vision for the future of technology and the IT industry?

Technology will continue to change our world incredibly. I see excellent prospects in healthcare,  blockchain and especially AI. These industries have great potential for improving people’s quality of life and increasing the efficiency of business processes.

What is most important to you in your work, and why do you continue to develop DevBrother?

DevBrother makes a significant impact on the lives of people in different countries. We bring not only highly qualified tech engineering help but also DevBrother. First, we have the values and corporate culture internally and provide these values to the world. We care about every person on the team and listen to them to develop their personal and professional level; we are a family that trusts and builds our relationships over the years, helping each other on projects and sometimes with different individual situations. We care about the projects that we do long term by being not only executives but also people who provide professional advice during all development processes, sometimes connected to the business side (not the tech side).

The same values we are cultivating in our collaboration with clients. All these bring a feeling of safety to ask questions and positive emotions from the partnership, trust, and reliability over the years.

Emotional intelligence, self-awareness,  self-management, and building strong long-term relationships are basic things we cultivate in DevBrother on all levels.

As a result, DevBrother growth is significant. During only one last year, we have grown from 50 to 100 people in the team, from several stable clients to tens of them.

I believe in the power of a team and the importance of creating an inspiring work environment. This motivates me to continue developing DevBrother and implementing new ideas and technologies.

What are you doing in your free time? What are your hobbies?

In my free time, I enjoy traveling and sports. These activities help me regain strength and find inspiration for new projects. I am also always happy to expand my professional circle of communication and look for new opportunities for cooperation.

I am also a singer, have videos on YouTube (Юрий Голиков) and music on SoundCloud and iTunes. I have been making music since I was 10, and it has been an essential part of my life. Being a musician also has an additional positive impact on DevBrother, as being a musician makes you feel more self-aware and develops your emotional intelligence. It is very important when running and developing companies nowadays.

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AMD: The David to NVIDIA’s Goliath in the AI Chip Arena? https://dataconomy.ru/2024/08/27/amd-nvidia-ai-chip-arena/ Tue, 27 Aug 2024 13:05:19 +0000 https://dataconomy.ru/?p=57226 The semiconductor industry is witnessing a fascinating rivalry as Advanced Micro Devices (AMD) challenges NVIDIA’s dominance in the AI accelerator market. With its Instinct MI300X, AMD is poised to disrupt the status quo, offering a cost-effective and powerful alternative to NVIDIA’s H100. The surge in demand for AI chips, driven by the explosive growth in […]]]>

The semiconductor industry is witnessing a fascinating rivalry as Advanced Micro Devices (AMD) challenges NVIDIA’s dominance in the AI accelerator market. With its Instinct MI300X, AMD is poised to disrupt the status quo, offering a cost-effective and powerful alternative to NVIDIA’s H100. The surge in demand for AI chips, driven by the explosive growth in AI adoption and data center expansion, further intensifies this competition.

In the fast-paced arena of AI chip technology, AMD is making notable progress in challenging NVIDIA’s dominance. While NVIDIA currently commands the lion’s share of the market, estimated at over 80%, AMD is steadily gaining momentum, particularly in the data center sector. This surge is fueled by robust demand for their MI300X AI chip, with projected sales reaching an impressive $4 billion, accounting for roughly 15% of AMD’s anticipated revenue.

When it comes to performance, NVIDIA’s H100 chips remain widely acknowledged for their prowess in AI workloads, especially in the realm of training. However, AMD’s MI300X is proving its mettle in specific AI tasks, particularly inference, where some assert it even outperforms NVIDIA’s flagship H100.

In terms of industry partnerships and adoption, NVIDIA boasts well-established collaborations with major cloud providers and enjoys broad acceptance across diverse sectors. On the other hand, AMD is actively forging partnerships, such as its alliance with TensorWave, to broaden its reach and refine its technology for AI-centric tasks.

The dynamic interplay between these two giants promises an exciting future for the AI chip market. I spoke with Darrick Horton, CEO at TensorWave, to understand why it has put all its AI eggs in the AMD basket.

AMD’s Instinct MI300X: A Game-Changer?

The MI300X boasts a larger memory capacity than the H100, making it advantageous for specific AI tasks, especially those involving large language models. While the H100 generally offers greater raw compute power, the MI300X shows promise in inference tasks and larger batch sizes. 

Although exact prices are not public, the MI300X is reportedly cheaper, potentially offering a better price-to-performance ratio. However, NVIDIA’s CUDA platform enjoys wider adoption and a more mature software ecosystem.

“One of the standout features of the MI300X is its superior memory architecture,” Horton told me. “With up to 192GB of unified HBM3 memory, the MI300X significantly outperforms the H100, allowing for the seamless handling of larger models and datasets directly on the accelerator. This reduces the need for off-chip memory accesses, which can be a bottleneck in AI workloads, leading to improved performance, caching abilities, and lower latency.”

Other considerations that led TensorWave to partner with AMD include energy efficiency and AMD’s software ecosystem.

“The MI300X is designed with energy efficiency in mind, delivering outstanding performance per watt,” Horton said. “This is particularly important as AI workloads scale, enabling enterprises to achieve high performance without escalating energy costs. This efficiency is a critical factor in large-scale deployments, where operational costs can be a significant concern. AMD’s ROCm (Radeon Open Compute) platform continues to mature and offers robust support for AI and HPC workloads. The open-source nature of ROCm provides developers with flexibility and the ability to optimize their applications for the MI300X, something that’s increasingly important as AI models become more sophisticated.”

The MI300X’s hybrid architecture combines CPU and GPU capabilities, which can optimize performance across various workloads, and efficiently scale across multiple accelerators. All of this paints a picture of a compelling alternative to NVIDIA.

Of course, AMD and NVIDIA are taking highly different approaches to building large-scale GPU systems. AMD favors the open standard of PCIe 5.0, offering broader compatibility and potentially lower costs, while NVIDIA relies on its high-bandwidth NVLink interconnect for improved performance in certain scenarios but with potential scalability limitations and higher costs.

A Mission to Democratize AI Access

TensorWave’s pricing model seems aimed at democratizing access to high-performance AI infrastructure, and the reported lower cost of leasing AMD GPUs through the platform can contribute to making advanced AI technologies more accessible to a wider range of organizations.

“When it comes to GPU procurement, it’s far from a simple 1-click checkout,” Horton said. “The process is often delayed by production backlogs, making shipment timing unpredictable. Plus, the upfront costs can be prohibitive. We’ve already built out our data centers with thousands of MI300X GPUs, ready to deploy when you are. But let’s say you manage to get your hardware. Now, you’re faced with the challenge of building, managing, and maintaining that hardware and the entire data center infrastructure. This is a time-consuming and costly process that not everyone is equipped to handle. With our cloud service, those worries disappear.”

While NVIDIA currently holds a dominant position, AMD’s Instinct MI300X and TensorWave’s innovative approach are poised to disrupt the AI accelerator market. 

“NVIDIA has been the dominant force in the AI accelerator market, but we believe it’s time for that to change,” Horton said. “We’re all about giving optionality to the market. We want builders to break free from vendor lock-in and stop being dependent on non-open-source tools where they’re at the mercy of the provider. We believe in choice. We believe in open-source optionality. We believe in democratizing compute. These principles were central when we built and focused our cloud around AMD MI300X accelerators.”

TensorWave believes this is important as more SMBs and big businesses start to leverage AI tools in the same way corporations already have.

“Think about accounting firms, legal offices, and research institutions,” Horton said. “They have vast amounts of historical data. If they can build AI tools that learn from these datasets, the potential for positive business outcomes is enormous. However, to achieve this, you’re going to need to process large datasets (250,000+ tokens), which will require substantial memory and performance from the hardware. And this isn’t just theoretical—enterprises are actively working on long-context solutions right now.”

A Bold Bet in a High Stakes Game

TensorWave also believes AMD will become the new standard as LLMs reach new heights, which is a big driver behind it putting all its chips on AMD (blackjack metaphor intended).

“As AI models continue to grow larger and more memory-intensive, NVIDIA’s solutions struggle to compete with the MI300X in terms of price-to-performance. Take Meta’s Llama 3.1 405B model, for example. That model can run on less than one full MI300X node (8 GPUs), whereas it requires approximately two nodes with the H100B. We’re betting big that the AI community is ready for something better—faster, more cost-effective, open-source, and readily available. 

Doubling down on its investment in AMD, TensorWave is looking towards the future, developing new capabilities to democratize further access to compute power.

“We’re developing scalable caching mechanisms that dramatically enhance the efficiency of handling long contexts,” Horton said. “This allows users to interact with larger chats and documents with significantly reduced latencies, providing smoother and more responsive experiences even in the most demanding AI applications.”

Currently in beta, TensorWave is projecting to roll this out to its users in Q4 2024.

The MI300X’s technical advantages, combined with TensorWave’s focus on democratization and cost-effectiveness, present a compelling alternative for businesses seeking high-performance AI solutions.

Ante Up for a Brighter Future

The “see, raise, and call” between AMD and NVIDIA will undoubtedly drive further advancements in GPU technology and AI applications across the entire industry. As the demand for AI continues to grow, both companies will play crucial roles in shaping the future of this transformative technology.

Whether AMD can ultimately surpass NVIDIA remains to be seen. However, their presence in the market fosters healthy competition, innovation, and ultimately benefits the entire AI ecosystem. The battle for AI supremacy is far from over, and the world watches with anticipation as these two tech titans continue to push the boundaries of what’s possible.

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AI Assistants for software engineers https://dataconomy.ru/2024/08/23/ai-assistants-for-software-engineers/ Fri, 23 Aug 2024 08:10:13 +0000 https://dataconomy.ru/?p=57129 In the rapidly evolving landscape of software development, AI assistants have emerged as game-changing tools, empowering engineers to write code more efficiently than ever before. To gain insights into this transformation, we spoke with Ilia Zadiabin, a mobile developer, about the impact of AI assistants on the software development process in 2024. Ilia Zadiabin is […]]]>

In the rapidly evolving landscape of software development, AI assistants have emerged as game-changing tools, empowering engineers to write code more efficiently than ever before. To gain insights into this transformation, we spoke with Ilia Zadiabin, a mobile developer, about the impact of AI assistants on the software development process in 2024.

AI Assistants for software engineersIlia Zadiabin is a prominent figure in the global tech ecosystem, renowned for his expertise in mobile app development and AI-driven solutions. As the founder of Slai, an innovative AI-powered language learning platform, he gained international recognition by successfully competing with industry giants.

His influence in the software development sphere is further amplified by his articles on TechBullion, a leading tech news platform, where he offers valuable perspectives on cutting-edge development practices and industry trends.

Ilia’s expertise has led to his selection as a judge for several high-profile tech events, including the Business Intelligence Group, the Global Startup Awards Africa, and Geekle’s hackathon. In the healthtech and fintech sectors, his work has set new industry standards, consistently earning praise from users and experts alike.

In general, software developers have looked favorably upon AI assistants, expecting that the new technology can improve productivity and smoothen their workflow. As an expert, could you tell us what exactly AI assistants do?

AI assistants are transforming the code writing process, acting as intelligent companions that enhance productivity and code quality. These tools provide real-time code suggestions and completions, often generating entire functions or code blocks based on context and intent.

A key strength of these AI tools is their ability to suggest alternative solutions to already solved tasks, encouraging developers to consider different approaches and potentially find more efficient or readable solutions. Even when AI suggestions are incorrect, they can be valuable by sparking new ideas or leading developers to better solutions they might not have considered.

By handling routine coding work and offering diverse perspectives, these tools allow developers to focus on higher-level problem-solving and creativity. In essence, AI assistants serve as collaborative partners, augmenting human capabilities in software development.

What AI assistant tools are used in the development workflow? Which features do you believe are required for an AI assistant in case it has to work effectively for software engineers?

AI assistants have become crucial tools in modern software development workflows. Key examples include GitHub Copilot, GitHub Copilot Chat, JetBrains AI, and Google Gemini for Android Studio. These tools offer features like code generation, real-time suggestions, and debugging support.

For more personalized support, developers can use tools like llama code assistant, Continue.dev, and Supermaven. An interesting feature is Claude Projects, which allows using multiple files as context for the AI assistant.

Effective AI assistants for software engineers should offer:

  1. Accurate code generation and completion
  2. Context-awareness across multiple files
  3. Multi-language support
  4. Integration with development workflows

I see. Could you provide more details on how they help improve productivity in your field?

The Microsoft study showed that developers using Copilot completed tasks 55% faster and had a higher task completion rate (78% vs 70%). The Accenture experiment demonstrated an 84% to 107% increase in successful builds with AI assistance.

Moreover, AI tools automate many of the mundane, repetitive tasks, allowing developers to focus on higher-level design and problem-solving, reducing stress and mistakes, and thereby enhancing productivity.

Can you name a good example of a project where an AI assistant has dramatically improved the result?

Research suggests that AI assistants can increase development speed by up to 50%, benefiting most projects. However, AI tools are particularly effective for certain types of tasks, especially those that are large and repetitive.

Writing tests is an excellent example of where AI assistants excel. They can efficiently generate comprehensive test coverage for an entire project – a task that developers often find tedious but is crucial for software quality. AI assistants are also highly effective at writing comments and documentation for technical projects, rarely missing important details.

A concrete example of AI’s impact is Duolingo’s adoption of GitHub Copilot. The language-learning platform reported a 25% increase in developer productivity after implementing the AI tool. This case demonstrates how AI assistants can significantly enhance development efficiency in real-world scenarios, particularly for companies with large codebases and complex software systems.

What problems are encountered while working with AI Assistants?

When working with AI assistants in software development, two main concerns arise. First is the issue of data privacy and potential code leakage. Developers worry about proprietary code being exposed to third parties through cloud-based AI models. Some companies address this by offering on-premises solutions, but for individual developers using public AI services, the risk remains.

The second concern involves AI mistakes and hallucinations, though this is less problematic in software development than in other fields. AI coding assistants typically generate small code snippets, making errors easier to spot and correct. The structured nature of programming languages, with strict syntax rules, helps in quick error detection. Unlike in natural language processing, code provides immediate feedback through compiler errors or runtime issues.

In practice, the hallucination problem common in AI chatbots is less severe in coding contexts. The rigid structure of programming languages naturally constrains the AI, reducing nonsensical outputs. Developers can easily identify and fix AI-generated errors, such as incorrect method names or syntax.

You mentioned earlier that AI assistants can dramatically improve productivity. Do you have any concrete data or research findings to support this claim?

GitHub, a leading platform in the software development space, conducted extensive research on the impact of their AI assistant, GitHub Copilot. Their findings, published in May 2024, provide compelling evidence of the benefits of AI assistants in software development.

Regarding productivity in terms of speed, GitHub’s controlled experiment with 95 professional developers yielded significant results. Developers using Copilot completed a specific coding task 55% faster than those without it. On average, Copilot users finished in 1 hour and 11 minutes, while non-users took 2 hours and 41 minutes. This represents a substantial time saving.

However, as mentioned earlier, productivity extends beyond mere speed. The research demonstrated improvements in various other areas as well. Developers using Copilot showed a higher task completion rate, with 78% finishing the task compared to 70% of non-Copilot users.

In terms of job satisfaction, a majority of users reported feeling more fulfilled with their work, experiencing less frustration when coding, and being able to focus on more satisfying tasks when using Copilot. The AI assistant also proved beneficial for maintaining focus and preserving mental energy. A significant portion of developers stated that Copilot helped them stay in the flow and conserve mental effort during repetitive tasks.

Efficiency in handling repetitive work was another area where Copilot showed strong benefits. An overwhelming majority of developers reported completing such tasks faster with the assistance of Copilot.

Research regarding productivity:

https://dl.acm.org/doi/10.1145/3520312.3534864
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321

Research: quantifying GitHub Copilot’s impact on developer productivity and happiness

https://mit-genai.pubpub.org/pub/v5iixksv/release/2

How do you integrate AI Assistants with other Development tools and platforms?

AI assistants for software development generally fall into two categories: those integrated into commercial development platforms and more personalized AI tools for individual developers.

The first category includes AI-powered features in platforms like Sentry, GitLab, and Eraser.io, as well as server-side code analyzers such as Snyk and SonarQube. These tools use AI to enhance specific workflows within their platforms. For example, Sentry suggests solutions to observed issues, while Snyk analyzes code and provides security-focused suggestions. Due to the unique nature of each product, it’s challenging to generalize about their AI enhancements.

The second category comprises “personal” AI assistants like GitHub Copilot, Supermaven, and Continue. These tools integrate directly into developers’ IDEs, primarily focusing on enhancing code completion. They aim to predict and generate code based on the developer’s intent. Some, like Copilot Chat, can even answer development questions by analyzing the entire project context.

It’s worth noting that some companies hesitate to adopt AI assistants due to concerns about data privacy, as these tools may potentially send codebase information to third parties.

How do you cope with situations when the AI assistant gives the wrong or misleading information?

As a frequent user of AI assistants, I encounter this issue regularly. Fortunately, AI hallucinations or errors in code completion are typically easy to spot and correct. Since these tools usually autocomplete only a few lines of code at a time, experienced developers can quickly identify and fix any mistakes.

For AI features in SaaS solutions, errors are generally less impactful as they often come in the form of suggestions rather than direct code changes. Overall, dealing with AI errors is manageable and, interestingly, gives developers confidence that they won’t be easily replaced by AI.

However, I do monitor trends in developer frustration with specific AI autocomplete tools. Persistent issues often lead to developers switching to alternative solutions or occasionally abstaining from AI assistance altogether.

Is it possible to create your own AI assistant?

Yes, you can create your own AI assistant. There are multiple approaches, ranging from complex to more straightforward.

The most challenging approach involves building an AI assistant from scratch using Python, PyTorch, and TensorFlow. However, this path is typically reserved for large companies and dedicated enthusiasts due to its complexity and resource requirements.

A more accessible approach for most developers is to leverage existing Large Language Models (LLMs) and either use them as-is or fine-tune them for specific needs. This method significantly reduces the technical barriers and development time.

To start using local LLMs for code assistance, you’ll need two main components:

  1. An extension for your Integrated Development Environment (IDE). One popular option is Continue (https://www.continue.dev/), which integrates well with various LLMs.
  2. A way to run LLMs locally. Ollama (https://ollama.com/) is a popular tool for downloading and running various LLM models on your local machine.

Other popular solutions in this space include llama-coder, Cody, and Tabby. These tools offer different features and integration options, allowing you to choose the one that best fits your workflow and requirements.

What place do you think AI assistants will take within the area of software development in a few years?

Even now, the combination of AI + developer is superior in speed to just a developer.

In a few years, I believe AI assistants will be core components of software development. As their functionality improves, they will support more sophisticated coding and hopefully will be integrated with compilers to eliminate possible errors.

My expectation is that every developer will use one of the AI assistants to some degree, and I suggest they do so immediately.

These tools improve not only the efficiency of coding but also enable developers to focus on higher-order tasks. In general, AI assistants are likely to enlarge the role of developers, promoting a collaborative environment in which coding will be more accessible to a wider audience.

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Pioneering computer vision: Aleksandr Timashov, ML developer https://dataconomy.ru/2024/08/23/pioneering-computer-vision-aleksandr-timashov-ml-developer/ Fri, 23 Aug 2024 06:32:28 +0000 https://dataconomy.ru/?p=57121 Aleksandr Timashov is an ML Engineer with over a decade of experience in AI and Machine Learning. He holds a degree in Mathematics from Indiana University and a graduate certificate in Artificial Intelligence from Stanford University. Aleksandr’s career spans multiple industries, including e-commerce, oil & gas, and fintech. In this interview, Aleksandr shares his unique […]]]>

Aleksandr Timashov is an ML Engineer with over a decade of experience in AI and Machine Learning. He holds a degree in Mathematics from Indiana University and a graduate certificate in Artificial Intelligence from Stanford University. Aleksandr’s career spans multiple industries, including e-commerce, oil & gas, and fintech. In this interview, Aleksandr shares his unique experiences of leading groundbreaking projects in Computer Vision and Data Science at the Petronas global energy group (Malaysia).

Hello Aleksandr. Please tell our readers about your background and how you got into Data Science and Machine Learning?

Pioneering computer vision: Aleksandr Timashov, ML developerMy passion for mathematics started early in high school when I participated in national-level olympiads. This love for numbers and problem-solving continued into university, where I was drawn to subjects like linear algebra and probability theory. The transition to Machine Learning felt natural given my mathematical background. It is an exciting field that allows me to apply abstract concepts to solve real-world problems.

When I was offered a Machine Learning position at Petronas, a large Malaysian corporation, I saw it as an incredible opportunity. The scale of the company and the potential to make a significant impact were major factors in my decision. Working at Petronas not only allowed me to improve company processes but also to positively affect the lives of millions of Malaysians. It’s a role that combines my technical skills with meaningful, large-scale impact.

Can you tell us about Petronas, what kind of company is that? And what brought you to Petronas, what were your goals when you started there?

Petronas is a huge state company in Malaysia, and although they mainly operate in the Oil & Gas industry, they do much more than that. The Petronas group of companies includes a bunch of other companies associated with Kuala Lumpur and Malaysia. Let’s say, for example, that KLCC property holding is directly related to Petronas. The company is responsible for the security and management of Kuala Lumpur City Center Twin Towers – the beautiful twin towers in the capital of Malaysia. And the company’s influence is not limited to Malaysia – it has a presence in more than 100 countries all over the globe.

Petronas is involved in various industries – from petrochemicals to logistics to engineering services. The company is also famous for pioneering several digital technology fields, including Cybersecurity, IoT, and, what concerns specifically me – Artificial Intelligence.

My foremost goals in joining Petronas as a Machine Learning and Data Science professional were to gain experience in a company with huge opportunities for improvement and to share my knowledge with as many young talents as possible.

And did you achieve these goals?

Certainly, this was fertile soil for my ambitions! When I joined the company, they were just creating a large Data Science/Machine Learning department – at that time, these technologies were not separated in the company. When I came, there were already several dozen people in the department, yet they were still working on the strategical roadmap for the department. At the same time, the huge benefit of this situation was lots of opportunities to improve and lots of directions to go. I chose Computer Vision as one of my favourite fields of AI. To continue about that time, I will give you an example: one Computer Vision model the company was using when I joined could “weigh” a gigabyte. On the very first day, the day I started working there, I made on the fly a model that was 20 times smaller and much more accurate.

The manager of the department that worked with this model was surprised at how quickly and accurately my model worked. They were very interested and asked me if I could optimise the work of other models. I agreed on the condition that if I do something, then I am responsible for it, and I am provided with the necessary resources. And so I got carte blanche to build the Computer Vision team, to make it an efficient unit that would help Petronas achieve its goals. The people I trained are still a core part of the Computer Vision team at Petronas.

So how did you address this challenge of creating a powerful Computer Vision team from ground zero?

That was actually not one but several challenges. Unlike smaller companies and startups, large companies with established structures and business processes are often reluctant to change. By the time I joined, Petronas already had working processes, and it wasn’t always self-evident how Computer Vision could help make those processes even more efficient. So we had, on the one hand, to persuade various departments within the company to accept a new technology, and on the other hand, to make the technology work for them.

And this leads us to the second challenge – building a team that would implement all these changes. The department was already running when I joined, and I couldn’t start by inflating the staff – I needed to choose and train people who were already there. And I was excited to see how talented people could be, even if they never worked with Computer Vision before! I was able to find people in the company and in the department who were interested in changing how things were being done, people who possess critical thinking and a love for solving complex mathematical problems – and this is not always an easy task! So, it took a lot of time and all my communication skills, but I managed to move people in the company to become imbued with Computer Vision.

Can you tell us about your work with Computer Vision at Petronas?

I led several projects that dramatically advanced the company’s technological capabilities:

Real-time Video Analytics for Security:

We developed an advanced system integrating deep learning algorithms with existing CCTV infrastructure. This project overcame challenges in processing vast amounts of visual data in real time and adapting to various environmental conditions. The resulting system accurately detected security threats, optimising security operations and positioning Petronas as a leader in AI-driven security in Malaysia’s energy sector.

Automated Industrial Plant Inspections:

We combined drone technology with advanced image recognition algorithms to automate plant inspections. This unprecedented project in Malaysia required creating robust models to identify defects in diverse industrial equipment under varying conditions. We developed a custom data pipeline to handle the immense volume of visual data, resulting in significant cost savings and reduced human exposure to hazardous environments.

Engineering Drawing Digitalization:

We tackled the digitalization of Petronas’ extensive engineering drawing collection using a combination of OCR and drawing detection algorithms. A key challenge was mapping drone inspection detections to real-world maps. This project dramatically improved the accessibility and utilisation of critical engineering information, enhancing operational efficiency and decision-making processes.

On these projects, I mentored numerous ML engineers, fostering a culture of innovation within Petronas. My work demonstrated broad expertise in computer vision, deep learning, and industrial IoT, showcasing the ability to adapt cutting-edge technologies to the specific needs of the oil and gas industry and tackle unprecedented challenges in the Malaysian context.

You told us you were implementing these projects in 2020-2022, so it all started amid the Covid-19 times. Did the pandemic and isolation complicate your work?

Well, of course, the pandemic affected our operations, just like everywhere in the world. Essentially, the priorities set before my team were changed, and we began to focus on such tasks as crowd management, face mask detection, etc. You see, as a giant state corporation, Petronas is responsible for many public places including KLCC park, and it’s really cool that our work at the time helped save a lot of lives during COVID.

By the way, it wasn’t just COVID that complicated our work and made it more challenging and interesting. Malaysia is a predominantly Muslim country, and this means people may behave differently and even dress differently from people in the countries in which the majority of ML and Computer Vision models are usually trained. There was a certain bias that we had to overcome to make the same models work in a significantly different environment.

This does sound intriguing! Could you tell us more about it?

For instance, pre-trained models mostly originate in Western countries, where there are not many ladies in headwear covering their heads to various degrees. It was quite problematic to detect women wearing head coverings! We had to reassemble the dataset, retrain the models, etc. This issue is unique to Malaysia.

And secondly, as I have already said, there is culture itself. People in Malaysia are less likely to express their opinions openly. In this regard, I had to demonstrate to my teammates – on purpose – that I could be wrong too. And when they gradually pointed out my mistakes, it encouraged them. In this somewhat roundabout way, I gradually built a more collaborative environment so familiar to Western companies but completely new to Malaysia.

As a person who assembled from scratch a team working on the bleeding edge of modern technology, what advice would you give to aspiring Data Science and Machine Learning specialists who are looking to make a significant impact in their careers?

For aspiring Data Science and Machine Learning specialists, I have three key pieces of advice:

Critically assess if this field truly aligns with your passions. DS and ML are complex and highly competitive, demanding not just skill but genuine enthusiasm to succeed.
If you’re certain this is your path, commit to intensive, continuous learning. As Andrej Karpathy noted, it takes around 10,000 hours of dedicated work to become a true professional in this field.

Focus on joining top companies or research labs where you can collaborate with leading minds in the field. Surrounding yourself with brilliant colleagues will accelerate your growth exponentially. You’ll be exposed to cutting-edge problems, innovative solutions, and a level of expertise that will challenge and inspire you daily.

Remember, if DS and ML are truly your passion, these challenges will be exciting. This enthusiasm, coupled with exposure to top talent, will be key to making a significant impact in your career.

What are some of the current trends and advancements in Computer Vision that you find most exciting and promising?

While Natural Language Processing has seen significant advancements recently, I believe Computer Vision remains highly underestimated and holds immense untapped potential. We are still far from achieving human-level capabilities in visual perception and understanding.

One of the most promising trends in Computer Vision is Self-Supervised Learning. This approach, which can be likened to how children learn by observing the world around them, has shown great potential in reducing the need for large labelled datasets. However, I believe there’s still a crucial element missing in fully replicating human-like visual learning and understanding.

I’m particularly excited about the evolution of Generative AI in CV, especially diffusion models and consistency models. These technologies are revolutionising image generation, manipulation, and understanding. Diffusion models excel in creating diverse, high-quality images, while consistency models enhance our ability to maintain coherence across different visual perspectives.

Despite these advancements, we’re still in the early stages of unlocking CV’s full potential. The field is ripe for innovation, particularly in developing more robust, generalizable models that can approach human-level visual understanding across diverse contexts. This makes it an incredibly exciting time to be working in Computer Vision, with ample opportunities for groundbreaking research and applications.”
This concise version maintains the key points about the current state of CV, your perspective on its potential, and the exciting developments in the field while being more focused and to the point.


Featured image credit: rawpixel.com/Freepik

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Overcoming Challenges, Embracing AI: The Volodymyr Panchenko Story https://dataconomy.ru/2024/08/20/embracing-ai-volodymyr-panchenko/ Tue, 20 Aug 2024 12:45:33 +0000 https://dataconomy.ru/?p=56820 In the heart of Ukraine, a young entrepreneur named Volodymyr (Vlad) Panchenko embarked on a journey that would lead him from the gaming world to the forefront of AI innovation. His story is one of resilience, vision, and an unwavering commitment to empowering small and medium businesses (SMBs) worldwide. Vlad Panchenko’s entrepreneurial spirit ignited early […]]]>

In the heart of Ukraine, a young entrepreneur named Volodymyr (Vlad) Panchenko embarked on a journey that would lead him from the gaming world to the forefront of AI innovation. His story is one of resilience, vision, and an unwavering commitment to empowering small and medium businesses (SMBs) worldwide.

Vlad Panchenko’s entrepreneurial spirit ignited early on. He founded his first company at the age of 16, and by 23, he had already built several successful ventures. His experiences in the gaming industry, where he created virtual item marketplaces and esports platforms, taught him valuable lessons that would shape his approach to building Portal.ai, his most ambitious venture yet.

From In-game Items to AI for SMBs

Portal.ai, founded in 2023, specializes in helping small to medium-sized businesses streamline their operations by centralizing data and functions. The platform integrates data from sources like Shopify, Amazon, Google, Wix, Quickbooks, and Meta, covering key areas such as operations, marketing, finance, and logistics to provide a holistic view of the business. In June 2024, Portal.ai raised $5m in a pre-seed round to help business owners implement its AI-driven operations.

Before Portal.ai, Panchenko founded DMarket, a global cross-game marketplace enabling gamers worldwide to trade and exchange in-game items across any gaming platform, creating an opportunity for over two billion gamers to buy and sell their virtual goods. DMarket was acquired by Mythical Games in 2022.

From in-game items to AI for businesses, Panchenko’s entrepreneurial path has been unique, but the key lessons he learned along the way remain the same.

One of the most important lessons Panchenko learned is the power of great teams. “No great idea will come to life without a great team,” Panchenko told me. “Effective communication, collaboration, and execution are the cornerstones of success.”

Overcoming Challenges and Seeing Beyond the Horizon

Panchenko’s journey hasn’t been without its challenges. He faced skepticism and doubt from those who didn’t share his vision. But instead of discouraging him, this fueled his motivation.

“Throughout my journey, many people laughed at my ideas and told me, ‘It’s not going to work,'” Panchenko said. “Instead of discouraging me, this fueled my motivation. I see potential where others don’t – recognizing future trends and latent demands before they become obvious.”

This ability to see beyond the horizon has been a defining characteristic of Panchenko’s career. He recognized the transformative power of AI early on and saw its potential to revolutionize the way SMBs operate.

“The most important tech trends are the evolution of large language models, the decreasing cost of computing, and a new product vision defined by AI-based products,” Panchenko said. “The unique aspect of this moment is that LLMs can already communicate in many languages. Simultaneously, the methodologies and mathematics behind them are universal.”

Portal.ai: A Vision for the Future of SMBs

Portal.ai is Panchenko’s vision for the future of SMBs. The platform serves as a digital chief of operations, providing businesses with the tools and insights they need to thrive in the digital age. It integrates essential data from marketing, sales, finance, and logistics, offering a 24/7 operational picture and actionable advice.

“Portal.ai stands to transform 400 million small and medium businesses, the backbone of a $60 trillion global economy,” Panchenko said. “We’re offering tools previously accessible only to the top 1% of corporations. Our digital CXOs provide best practice methodologies, allowing businesses to operate with unprecedented efficiency and success.”

Panchenko’s commitment to Portal.ai goes beyond just building a successful company. He sees it as his life’s work, a chance to make a meaningful impact on the world.

“Portal.ai is the evolution of everything I’ve done before,” Panchenko said. “Along with the top-notch team we’ve assembled, this venture is on another level, offering a chance to impact the entire world in a meaningful way.”

Resilience and Responsibility

Of course, many people in the world want to make a meaningful impact on the world. Some never get past the idea stage, many lack the experience to execute the idea at this stage of their career, and some show that their intentions are very different from their actions. 

Others react to difficult situations and apply the lessons to both their personal life and their work. The war in Ukraine had a profound impact on Panchenko, leading him to help DMarket employees relocate. This experience deepened his sense of responsibility and the role of leadership during a crisis.

“That experience deepened my sense of responsibility and the role of leadership during a crisis,” Panchenko said. “It taught me that you should never stop doing good and standing by your beliefs. The most important thing I’ve learned is that no matter how long it takes or what effort is required, staying true to your values and caring for others is paramount.”

The Road Ahead for Portal.ai

As Portal.ai continues to grow and evolve, Panchenko remains focused on his mission to democratize AI and empower SMBs worldwide.

“The next step for Portal.ai is global adoption and scaling, reaching billions of successful customers,” Panchenko said. “We envision a world where every small and medium business can harness the power of AI to achieve unprecedented levels of success. Our mission is to democratize access to top-tier operational methodologies, transforming not just businesses but entire economies.”

Volodymyr Panchenko’s journey is a testament to the power of entrepreneurship, resilience, and a vision for a better future. With Portal.ai, he is leading the charge to democratize AI and unlock the full potential of SMBs worldwide. His story is an inspiration to all those who dare to dream big and make a difference in the world.

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Psychology of a Gen Z Relationship: Loneliness, Fantasies, and the Role of Technology https://dataconomy.ru/2024/08/16/gen-z-relationships-technology/ Fri, 16 Aug 2024 12:24:54 +0000 https://dataconomy.ru/?p=56744 Why do young people maintain a one-sided relationship with bloggers, performers, and webcam models? What is a delusionship and how do public figures encourage fans to have parasocial relationships with them (and no, that doesn’t mean dating while skydiving)? In this article, we discuss these issues with professional psychologist Tatiana Persico and Stan Kos, founder […]]]>

Why do young people maintain a one-sided relationship with bloggers, performers, and webcam models? What is a delusionship and how do public figures encourage fans to have parasocial relationships with them (and no, that doesn’t mean dating while skydiving)? In this article, we discuss these issues with professional psychologist Tatiana Persico and Stan Kos, founder at YouMatch.

Generation Z, often called Zoomers, includes those born from the late 1990s to the early 2010s. They are usually described as tech-savvy, pragmatic, open-minded, individualistic but also socially responsible. Gen Z has grown up in a digital world, surrounded by smartphones, social media, and instant access to information. For them, swiping, tapping, and scrolling are about as second nature as breathing.

“A specific period of time when a person is born and grows up and always influences their personality formation,” Tatiana Persico, YouMatch Chief Psychologist, and relationship expert, said. 

Zoomers are so immersed in the virtual world that they often don’t separate it from reality and perceive their online activities as an important part of their real, offline life. Sometimes digital can even replace real: instead of going to a restaurant people now offer delivery, and instead of meeting someone we call them or chat with them on social media. It saves us time and energy – but at what cost?

“The ease and effortlessness with which we can get things and emotions online create an illusion that you don’t have to work hard to achieve something,” Persico said. “In some ways, it is cool. Gen Z are more adaptive and motivated and lots of them believe that you just have to find the right tool to achieve literally everything.”

Today, celebrities, bloggers, movies, and music stars all have accounts on social media. For many people, it gives the wrong idea that all those public figures are easily accessible and ready to communicate with everyone. Indeed, when you follow a blogger who shares their pictures and personal thoughts online every day, you start to feel like you know them personally.

This creates a sense of closeness, similar to what you would feel when having a real relationship with someone in offline life.

From Fans to Delulu: The Unsettling Trends in Gen Z Relationships

The merging of real and virtual worlds, offline and online, digital and analog, gave rise to several curious (and, sometimes, unsettling) relationship phenomena.

The term “parasocial” is usually used in the context of young audiences feeling they have a relationship with the influencer due to the huge amount of the influencer’s content they consume. The influencer in question, however, is unaware of the fan’s existence other than the general knowledge of having fans.

“Stanning” is the act of being overly obsessed with an artist/person/character/etc. When you stan a musician or another celebrity it means that you’re a big fan. These relationships can be exacerbated by the influencers confirming they have more than a creator-to-viewer relationship.

Delusionship is a new word created to refer to a type of relationship where one person was delusional the whole time, and the relationship never really existed in the first place, it was all in their mind and delusions. A “delulu” is a delusional fan girl/boy who believes they can/will end up with their favorite idol or celebrity and invests an unhealthy amount of time and energy into their idol.

Developing feelings for a webcam model (or even a relationship with such a model) is another common but rarely discussed phenomenon of the digital age. This attachment is facilitated by repeated exposure, familiarity, and easily accessible, mostly positive interactions. Unlike real-life relationships, interactions with webcam models always prioritize the user’s needs, fostering a false sense of emotional and physical intimacy. This can lead to a fantasy-driven relationship where the user projects romantic ideals onto the model. 

Such fixations can harm real-life relationships by creating unrealistic expectations and diverting attention from genuine connections. The relationship remains transactional and lacks the complexity and mutual consideration found in real-life partnerships, often leading to secrecy and feelings of shame. 

From Screen to Soul: Understanding Gen Z’s Virtual Intimacy

The widespread internet connection and the rise of social media have contributed to changing the psychological profile of an entire generation.

One of the basic human needs is the need for love, belonging, and closeness. When you interact with a virtual companion regularly, you start feeling like you’ve known this person for a long time, understanding each other’s values and views on life. Gradually, you start feeling like you’ve found that special someone who fulfills your need for close relationships.

It doesn’t matter that you’ve never met this person offline. When you have an intense online relationship, it may feel like it’s all real.

“For some people, having a fantasy-like one-sided online relationship that is fully under their control may be a way to deal with loneliness,” Persico said. “However, Generation Z is rarely familiar with the true feeling of loneliness. They have many online friends and can interact with people who share their interests and values daily. Zoomers are very curious; they know how to occupy themselves and set ambitious goals.”

So, for Gen Z, having a relationship with a webcam model may be completely normal. Because of the deep integration of digital and online platforms in their everyday routine, the divide between real and digital may be blurred to the point where they can no longer see the difference. 

When a Zoomer chooses a real offline partner, they usually have a clear idea of why they need this relationship and what future they will have as a couple.

“When choosing dating apps, Gen Z pays attention to details others might overlook,” Kos said. “The old-fashioned apps with their focus on appearances, likes, and dislikes are too boring for the new generation which is more into deeper communication based on shared interests, interesting educational activities, and large gatherings.”

Dating apps of the future should evolve to be more profound and genuine, both in their user interfaces and matching algorithms, as well as in the values they promote.

“These platforms should include features that encourage authentic connections and minimize user burnout,” Kos said. “Enhancing user experiences to foster authenticity, self-care, and self-awareness is essential, as is creating marketing campaigns that showcase successful, harmonious relationships formed through the apps.”

When Zoomers start a relationship in real life, it is important for them that the emotions are more vivid than in online communication. That is why connecting with their hearts and souls is more important for them than simple attraction to someone’s face and body.

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Inside the World of Algorithmic FX Trading: Strategies, Challenges, and Future Trends https://dataconomy.ru/2024/08/13/inside-algorithmic-fx-trading/ Tue, 13 Aug 2024 12:45:55 +0000 https://dataconomy.ru/?p=56523 The foreign exchange (FX) market, where currencies are traded against each other, has a rich history dating back centuries. Historically, FX trading was primarily conducted through physical exchanges, with traders relying on their intuition and experience to make decisions. However, the advent of electronic trading in the late 20th century revolutionized the FX market, opening […]]]>

The foreign exchange (FX) market, where currencies are traded against each other, has a rich history dating back centuries. Historically, FX trading was primarily conducted through physical exchanges, with traders relying on their intuition and experience to make decisions. However, the advent of electronic trading in the late 20th century revolutionized the FX market, opening it up to a wider range of participants and increasing trading volumes exponentially.

Today, the FX market is the largest and most liquid financial market in the world, with an average daily turnover exceeding $7.5 trillion in April 2022, according to the Bank for International Settlements (BIS). Its importance lies in its role in facilitating international trade and investment, as well as providing opportunities for profit and serving as an economic indicator.

Data science has emerged as a critical tool for FX traders, enabling them to analyze vast amounts of data and gain valuable insights into market trends, price movements, and potential risks. I spoke with Pavel Grishin, Co-Founder and CTO of NTPro, to understand data science’s role in this lucrative market.

The Rise of Algorithmic FX Trading

One of the most significant applications of data science in FX trading is the development of algorithmic trading strategies. These strategies involve using platforms to execute trades automatically based on pre-defined rules and criteria. Algorithmic trading has become increasingly popular due to its ability to process large amounts of data quickly, identify patterns and trends, and execute trades with precision and speed.

“Proprietary trading firms and investment banks are at the forefront of data science and algorithmic trading adoption in the FX market,” Grishin said. “They utilize sophisticated data analysis to gain a competitive advantage, focusing on areas like market data analysis, client behavior understanding, and technical analysis of exchanges and other market participants. Investment banks, for instance, analyze liquidity providers and implement smart order routing for efficient trade execution, while algorithmic funds use data science to search for market inefficiencies, develop machine learning (ML) models, and  backtesting trading strategies (a process that involves simulating a trading strategy using historical data to evaluate its potential performance and profitability).”

Types of Data-Driven Trading Strategies

There are several types of data-driven trading strategies, each with its unique approach and characteristics.

“Data-driven trading strategies, such as Statistical Arbitrage, and Market Making have evolved with advancements in data science and technology,” Grishin said. “Statistical Arbitrage identifies and exploits statistical dependencies between asset prices, while Market Making involves providing liquidity by quoting both bid and ask prices.  There is also a High Frequency Trading approach, that focuses on executing trades at high speeds to capitalize on small price differences. These strategies and approaches have become increasingly complex, incorporating more data and interconnections, driven by technological advancements that have accelerated execution speeds to microseconds and nanoseconds.”

Collaboration Between Traders, Quants, and Developers

The implementation of complex algorithmic trading strategies requires close collaboration between traders, quants (quantitative analysts), and developers.

“Quants analyze data and identify patterns for strategy development, while developers focus on strategy implementation and optimization,” Grishin said. “Traders, often acting as product owners, are responsible for financial results and system operation in production. Additionally, traditional developers and specialized engineers play crucial roles in building and maintaining the trading infrastructure. The specific division of roles varies between organizations, with banks tending towards specialization and algorithmic funds often favoring cross-functional teams.”

Challenges and the Role of AI and ML in FX Trading

Translating algorithmic trading models into real-time systems presents challenges, mainly due to discrepancies between model predictions and real-world market behavior. These discrepancies can arise from changes in market conditions, insufficient data in model development, or technical limitations.

“To address these challenges, developers prioritize rigorous testing, continuous monitoring, and iterative development,” Grishin said. “Strategies may also incorporate additional settings to adapt to real-world conditions, starting with software implementations and transitioning to hardware acceleration only when necessary.”

Developers in algorithmic trading require a strong understanding of financial instruments, exchange structures, and risk calculation.

“Data-handling skills, including storing, cleaning, processing, and utilizing data in pipelines, are also crucial,” Grishin said. “While standard programming languages like Python and C++ are commonly used, the field’s unique aspect lies in the development of proprietary algorithmic models, often learned through direct participation in specialized companies.”

What Comes Next?

Looking ahead, the future of FX trading will likely be shaped by continued advancements in data science and technology.

“The future of algorithmic trading is likely to be shaped by ongoing competition and regulatory pressures,” Grishin said. “Technologies that enhance reliability and simplify trading systems are expected to gain prominence, while machine learning and artificial intelligence will play an increasing role in real-time trading management. While speed remains a factor, the emphasis may shift towards improving system reliability and adapting to evolving market dynamics.”

While the path ahead may be fraught with challenges, the potential rewards for those who embrace this data-driven approach are immense. The future of FX trading is bright, and data science will undoubtedly be at its forefront, shaping the market’s landscape for years to come.

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Behind the scenes of building digital platforms: Muhammad Huzaifa Malik on software engineering https://dataconomy.ru/2024/07/16/behind-the-scenes-of-building-digital-platforms-muhammad-huzaifa-malik-on-software-engineering/ Tue, 16 Jul 2024 14:18:46 +0000 https://dataconomy.ru/?p=55096 In today’s context of global digitalization, software development has become a subject of major significance affecting every industry across the world. Every company, organization, and institution need a website. Every professional, academic, or social community relies on apps. Digital platforms and ecosystems cover every sphere of social life, whether it’s work, leisure, or interactions with […]]]>

In today’s context of global digitalization, software development has become a subject of major significance affecting every industry across the world. Every company, organization, and institution need a website. Every professional, academic, or social community relies on apps. Digital platforms and ecosystems cover every sphere of social life, whether it’s work, leisure, or interactions with governmental structures. As technologies keep developing exponentially, and their influence on business, infrastructure, and society keeps growing, the craft of software development fascinates minds more and more.

To answer the questions on what lies behind the curtains of software architecture, coding, and all the intricate aspects of technical magic, we interviewed Huzaifa Malik, a software engineering expert in South Asia with extensive experience in websites and apps creation. Malik received an international acclaim as one of the brightest software development talents of his generation after the series of groundbreaking accomplishments in building new-generation apps for BarqApp projects, which brought a change into the industry, and played a role in helping people to get through the COVID lockdown. To find out more about the secret world of software architecture and the role it plays today, let’s dive into Malik’s story.

Hi Huzaifa, thanks for joining us today! Could you tell us more about your work on BarqApp’s projects? How did it all start?

Behind the scenes of building digital platforms Muhammad Huzaifa Malik on software engineering

Imagine a matchmaking service, but for parcels; the one that connects those who need to send a package to anywhere in the world with those who plan traveling to the same destination point and happen to have an extra luggage space. That’s what BarqApp has originally been about. It was quite an innovative approach to the delivery industry: think of it as a dating app but for packages and their carriers. The whole concept was truly inspiring. It was something fresh, revolutionary, and creatively challenging to bring to life. However, when the COVID-19 pandemic struck and international tourism was put on hold, our entire model of service faced some obvious tremendous obstacles.

Most of your competitors did not make it through the pandemic. What did you do to stay on top despite all the COVID challenges?

After losing the option of relying on travel, we had to pivot quickly. The transformation of BarqApp’s form of operation wasn’t just a minor shift; it was a complete overhaul into a full-scale logistics and delivery platform. This meant turning our embryonic delivery app, which was still in the incubator and far from ready, into the backbone of our new service model. The first week was fraught with issues: bugs, crashes, performance lags… One challenge was constantly followed by another, even bigger one. It felt like trying to steer a ship through a storm with a broken compass.

However, the strongest need for having a reliable delivery service to get through the lockdown kept us motivated; we realized the social significance of our mission and knew that simply retreating wasn’t in the cards. I immersed myself in the backend, debugging and optimizing at warp speed. By the end of the second week, not only had we addressed the initial problems, the app started actively facilitating orders and managing deliveries, quickly gaining popularity and growing exponentially.

What would you say became the turning point for BarqApp?

I’d say it was the success of this delivery app. It has become our pivot and cornerstone of all our subsequent accomplishments, which gave us so much inspiration and helped to turn BarqApp into first, a full-fledged logistics service with a wide spectrum of functions, and eventually, into an industry leader. It was a rapid and intense transformation, driven by necessity and executed with a mix of tech savvy and sheer determination. This experience showcased our team’s ability to adapt and come up with non-standard creative solutions under pressure, turning a potential service collapse into a robust platform that played a crucial role for millions during the pandemic.

To most people, your field seems filled with technical mysteries and complex algorithms. Could you shed some light on what’s happening behind the scenes of software engineering?

It feels like being a wizard in a world of technology, where your magic wand is a code. As a Senior Software Developer, I get to use this magic to build truly amazing things, for instance, new-generation apps that can bring groceries right to your doorstep when you can’t go out or enable you to chat with a doctor from your living room. The year I was born no one could even imagine that things like that will be possible one day. And here I am now, making this magic happen in the real world, helping millions of people every day, and making their lives better.
Sometimes, software engineers need to turn into digital detectives, diving into a malfunctioning code to track down a sneaky error and fix it. Then our job reminds us of playing a high-tech version of hide and seek, where we need to look for the hidden symbols that cause some irritating flows, like making the products invisible or creating a glitch on a landing page. And these moments are the ones where the most exciting adventures start. We face challenges, think super-fast, and use our coding spells to turn problems into opportunities and make a positive change. It’s more than just fun: it’s thrilling and makes you feel like you have super-powers!


Featured image credit: Nate Grant/Unsplash

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Google’s AI Aftershock: Expert Explains How to Thrive in the New Search Landscape https://dataconomy.ru/2024/07/08/googles-ai-expert-how-to-new-search/ Mon, 08 Jul 2024 08:15:15 +0000 https://dataconomy.ru/?p=54679 Google recently dropped a bombshell on the search world, shaking the foundation of how we find information online. Forget scrolling through pages of blue links; now, an AI oracle sits atop the search results, dishing out answers plucked from the web’s vast expanse. Google promises this AI genie will grant users’ wishes for quicker, more […]]]>

Google recently dropped a bombshell on the search world, shaking the foundation of how we find information online. Forget scrolling through pages of blue links; now, an AI oracle sits atop the search results, dishing out answers plucked from the web’s vast expanse.

Google promises this AI genie will grant users’ wishes for quicker, more comprehensive answers. But for content creators and publishers, it’s a nightmare scenario. Will anyone bother clicking through to their sites if the information is already laid out on a silver platter? What happens to the carefully crafted marketing funnels, the ad revenue that keeps the lights on, and the lucrative affiliate marketing world?

Google, naturally, assures everyone that this is all for the best. More diverse websites will see traffic, they claim, and links within the AI overviews will be boosted. But let’s be honest: Google needs our content to feed its AI beast. So what does this tectonic shift mean for those whose livelihoods depend on search?

The digital landscape is shifting beneath our feet, and the tremors emanate from Google’s AI-powered search revolution. AI-generated summaries now occupy the coveted top spot in search results, potentially overshadowing the familiar blue links that have guided our online journeys for years. This seismic shift has left many content creators and website owners reeling, their hard-earned organic traffic seemingly hanging in the balance. However, within this upheaval lies a world of opportunity, especially for smaller businesses and niche websites willing to embrace change and adapt their strategies.

I spoke with Anna Lebedeva, a seasoned expert, marketing consultant, founder at The Top Voices, and former Head of Marketing at Semrush – the SEO tools company – about the ramifications of this search engine optimization earthquake.

“Overall, it might potentially lead to a decrease in organic traffic,” Lebedeva acknowledges, “as users may find the information they need directly in the AI-generated summaries without clicking through to individual websites.” 

However, Lebedeva emphasizes that this doesn’t spell disaster. Instead, it’s a call to action, a challenge to rethink and re-strategize.

Strategies for Visibility in the AI Age

To remain visible in this AI-dominated landscape, Lebedeva emphasizes the importance of high-quality content that is informative, engaging, and structured in a way that search engines can easily understand. 

“Implement structured website data to help search engines understand your content and thus increase the chances of getting into AI-driven results,” Lebedeva advises. Essentially, she says, “speak the AI’s language.”

Lebedeva further elaborates, “It is even more important than before to focus on enhancing Local SEO, optimizing Google Business Profile with up-to-date information, and getting into local directories.” For smaller businesses, focusing on local SEO can be a game-changer. By optimizing their online presence for local searches, they can attract a targeted audience and establish themselves as the go-to experts in their community.

Additionally, Lebedeva suggests leveraging long-tail keywords that are less competitive and more specific to your niche. “Regarding keyword optimization, being even more effective with leveraging less competitive long-tail keywords that a small brand can own,” she advises. This allows smaller businesses to rank higher for specific queries that larger competitors might overlook.

Differentiation in a Crowded Digital Landscape

While optimizing for AI is essential, Lebedeva emphasizes that it’s equally important to maintain your brand’s unique identity and voice. “The only way to stand out is to keep your uniqueness and not be afraid of being different, express your expertise, and be creative in all types of communication to get remembered by having a strong brand identity,” she states.

A strong brand identity can set you apart in a world where AI-generated summaries might seem generic and impersonal. Invest in visually appealing content, infuse your communication with personality, and foster a sense of community around your brand. 

“Maybe make your communication more personal and slightly emotional to make it look even more from human to human,” Lebedeva suggests. This human touch can make a difference in a digital landscape dominated by AI.

Community Building as a Competitive Edge

Building a loyal community is another crucial aspect of differentiation. Lebedeva emphasizes the importance of “building your community to support loyalty and keep investing into social media visibility that you more or less own. You can foster a loyal following beyond search engine results by engaging with your audience on social media platforms and creating a sense of belonging.”

The Future of SEO and Content Creation

While the rise of AI-generated summaries may seem daunting, Lebedeva remains optimistic about the future of SEO. She believes that Google will continue valuing original, high-quality content and will likely introduce measures to ensure content creators are fairly compensated. 

“Pay attention to being as local as possible – local search, Google Maps, social media, reviews, and just be creative to get remembered. Build your brand,” Lebedeva advises.

The future of SEO is uncertain, but one thing is clear: it will require adaptability, creativity, and a willingness to embrace new technologies. Smaller businesses and niche websites can survive and thrive in this AI-driven era by focusing on quality content, brand building, community engagement, and local SEO.

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Harnessing AI for better customer experiences: An interview with Sudheer Someshwara https://dataconomy.ru/2024/06/28/harnessing-ai-for-better-customer-experiences-an-interview-with-sudheer-someshwara/ Fri, 28 Jun 2024 07:29:24 +0000 https://dataconomy.ru/?p=54203 In this insightful interview, we talk with Sudheer Someshwara, the Director of Product at Yelp, who has been at the forefront of developing AI/ML products and recommendation software. With a rich career spanning from leading innovative projects at Amazon to founding Viraltag, a successful SaaS B2B marketing platform, Sudheer shares his journey, challenges, and the […]]]>

In this insightful interview, we talk with Sudheer Someshwara, the Director of Product at Yelp, who has been at the forefront of developing AI/ML products and recommendation software. With a rich career spanning from leading innovative projects at Amazon to founding Viraltag, a successful SaaS B2B marketing platform, Sudheer shares his journey, challenges, and the strategies that drive his customer-centric approach. Dive into the conversation to discover how he leverages AI to enhance trust and safety, improve user experiences, and develop data monetization strategies at Yelp while reflecting on the invaluable lessons learned from his entrepreneurial endeavors.

Sudheer, you’ve led several significant projects at Yelp, particularly in recommendation software and AI/ML products. Can you tell us some key challenges and successes you’ve encountered in this space?

At Yelp, leading the Trust & Safety, AI/ML, and Data Monetization groups, one of the primary challenges has been ensuring the integrity of reviews and combating spam and fraud. Ensuring integrity of reviews involved developing robust machine-learning models to detect and recommend the most helpful and reliable reviews based on hundreds of signals of quality, reliability and user activity on the platform. Launching ML models substantially improved our automated spam detection rates and was a significant success in this area. We also built a new team to implement offline customer safety features, such as business COVID safety precaution attributes and consumer alerts regarding health inspection warnings, to help users transact with local business with confidence during the pandemic. These initiatives improved user trust and demonstrated how AI and ML could be leveraged to address complex safety issues in a dynamic environment.

Sudheer Someshwara
Sudheer Someshwara

How do you prioritize customer needs while developing AI/ML solutions at Yelp?

Customer focus is at the core of everything we do. We prioritize customer needs by maintaining a strong feedback loop with our users. Any new ML model or feature we develop is rigorously A/B tested and iterated based on user feedback and impact of these models on key user outcomes. This approach helps ensure that the solutions we create genuinely add value to our customers, whether it’s through improved recommendation systems, enhanced search and ranking systems, or better data monetization solutions.

Your role at Yelp involves managing multiple teams and projects. How do you maintain alignment and ensure effective communication across a large organization?

Effective communication and alignment are critical, especially when leading diverse teams. We run annual planning sessions, quarterly OKRs, and monthly executive operating reviews to align efforts across the entire business and ensure everyone is working towards the same goals. These structured processes help set clear goals, track progress, and make necessary adjustments early on. Additionally, fostering a culture where open communication is encouraged at all levels helps prevent misalignments or issues.

Before Yelp, you founded Viraltag, a SaaS B2B marketing platform. What inspired you to start this venture, and how did you transition from a side project to a full-time business?

Viraltag started as a side project while I was at Amazon. I noticed the growing importance of visual content in social media marketing and saw an opportunity to help businesses manage and distribute their visual assets more effectively. The initial traction was overwhelming – we had about 20,000 users within the first few months. This validation encouraged me to transition from Amazon and focus on Viraltag full-time. We joined AngelPad, an accelerator that provided valuable mentorship and helped us refine our business strategy.

At Viraltag, what strategies did you implement to ensure the company remained customer-focused as it scaled?

Maintaining a customer-first approach was integral to our success at Viraltag. Every decision was made with the customer in mind, from hiring the right people who shared this philosophy to continuously gathering and acting on customer feedback. We also implemented a culture where everyone was involved in customer service regardless of their role. This ensured that we remained attuned to our customers’ needs and built a strong, customer-centric company culture.

How did your experience at Amazon help you when building Viraltag?

My experience at Amazon was invaluable. Working on innovative ad formats and leading teams taught me the importance of scalability and efficiency. At Amazon, I learned to navigate large-scale projects and collaborate across different functions, skills that were directly transferable when scaling Viraltag. Additionally, Amazon’s culture of customer obsession deeply influenced how I approached product development and customer service at Viraltag.

You mentioned taking a step back to gain a fresh perspective on the market while at AngelPad. Can you elaborate on how this approach helped shape Viraltag’s growth strategy?

At AngelPad, we learned to step back and comprehensively assess the market landscape periodically. This approach helped us identify that our initial focus on Pinterest needed to expand. We realized that our users were looking to save time and drive more traffic and sales through multiple social networks. This insight led us to broaden our platform to include Facebook, Twitter, Instagram, and Tumblr, significantly increasing our market potential and user base.

What advice would you give entrepreneurs leveraging AI/ML in their startups?

Firstly, ensure that your AI/ML initiatives align with your business goals and customer needs. It’s easy to get caught up in the excitement of AI/ML, but without clear objectives, these projects can quickly become unfocused. Secondly, invest in a robust data infrastructure and prioritize data quality. High-quality data is the foundation of effective AI/ML models. Lastly, maintain a feedback loop with your users to continually refine and improve your models based on real-world performance.

Reflecting on your journey from engineering roles to leadership positions, what has been the most rewarding aspect?

The most rewarding aspect has been seeing the tangible impact of our work on customers’ lives and businesses. Whether it’s through enhancing trust and safety at Yelp or helping businesses grow through Viraltag, knowing that our solutions are making a difference is incredibly fulfilling. Additionally, witnessing the growth and success of team members I’ve mentored and worked with is gratifying.

What’s next for you in the field of AI and product management?

I’m excited to continue exploring how AI and machine learning can solve complex problems and create value across different domains. At Yelp, we’re constantly innovating and finding ways to enhance our platforms and services. I’m passionate about mentoring the next generation of product leaders and contributing to the broader tech community by sharing knowledge and experiences.

Featured image credit: Unsplash

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Talent magnet: How Hackathons help attract new stars? https://dataconomy.ru/2024/04/19/talent-magnet-how-hackathons-help-attract-new-stars/ Fri, 19 Apr 2024 12:40:14 +0000 https://dataconomy.ru/?p=51297 Hackathons have emerged as dynamic catalysts in the rapidly evolving world of technology, igniting creativity, collaboration, and innovation across diverse industries. What began as exclusive battlegrounds for IT coders has evolved into inclusive gatherings, attracting various talents from banking, retail, pharmaceuticals, and beyond. No longer confined to the realm of programmers, hackathons now attract economists, […]]]>

Hackathons have emerged as dynamic catalysts in the rapidly evolving world of technology, igniting creativity, collaboration, and innovation across diverse industries. What began as exclusive battlegrounds for IT coders has evolved into inclusive gatherings, attracting various talents from banking, retail, pharmaceuticals, and beyond. No longer confined to the realm of programmers, hackathons now attract economists, designers, journalists, and professionals with expertise that transcends conventional boundaries.

Traditionally, companies leverage hackathons to address product-related challenges, from exploring new applications for existing services to integrating cutting-edge technologies like machine learning, bots, and blockchain. However, these events serve a dual purpose; beyond solving immediate problems, they function as talent hubs, offering a fertile ground for HR professionals to unearth gems amidst a sea of innovative thinkers.

Enter the Hackathon Raptors—a non-profit community of talented developers who have taken the initiative to organize socially impactful hackathons and reached remarkable heights. Their journey unveils hackathons’ pivotal role in attracting new talent, fostering innovation, and building a vibrant community of developers.

In the world of tech innovation, Maksim Muravev stands out as a driving force behind the Hackathon Raptors community. His firsthand experiences highlight these events’ unique environment, challenging participants to think creatively and collaborate under pressure, ultimately honing their skills and fostering diverse, impactful solutions. Beyond mere competition, Maksim emphasizes hackathons’ vital role in networking and community building within the tech industry. He values the connections forged during these events, which often evolve into lasting friendships and productive collaborations, reflecting his belief in their power to foster a vibrant community of developers eager to learn, share, and grow together.

How Hackathons help attract new stars

Similarly, Dmitry Brazhenko, an ML engineer at Microsoft Copilot, champions hackathons as pivotal for innovation in machine learning and AI. Drawing from his background as a data analytics tutor and contributions to open-source libraries, Dmitry views hackathons as unique platforms for experimenting with new algorithms and models collaboratively. His work, including developing SharpToken and insightful articles on Habr, demonstrates the practical impact of machine learning innovations from these competitive settings. Dmitry also stresses the importance of hackathons in making technology education more accessible, bridging academic learning and real-world application, democratizing technology education, and fostering a forward-thinking approach to future technologies.

Alim Shogenov, an exceptional software engineer renowned for his groundbreaking work across multiple sectors, including education, finance, and travel technology, offers another perspective on the transformative power of hackathons. His innovative project, “Digital Future of Education”, earned him prestigious accolades for its transformative impact on document management in educational institutions, slashing processing time by 16% while enhancing user accessibility. Alim highlights hackathons’ unique environment, pivotal for rapid innovation in turning concepts into working prototypes. His expertise and interdisciplinary collaboration underscore hackathons’ significant role in fostering personal and professional growth within a supportive community.

Dmitrii Starikov, an incredibly talented web developer with a wealth of experience in creating high-load systems for world-renowned exhibitions, libraries, and archives. He has also made significant contributions to projects of national significance that preserve the world’s cultural and historical heritage. Dmitrii is a firm believer that hackathons provide developers with unique opportunities to push the boundaries of their professionalism and solve real-world problems. Dmitrii is absolutely thrilled about the unique challenge that hackathons present! Participants are given the opportunity to apply their skills in novel ways, which helps to highlight the soft skills gained through participation, such as enhanced communication and effective presentation of ideas. Dmitrii is a big fan of the community aspect of hackathons, where connections with like-minded individuals can lead to potential collaborations and opportunities beyond the event itself.

How Hackathons help attract new stars

Oleg Mikhelson, an outstanding tech expert with decades of experience in technology infrastructure, brings a perspective to the discussion. For Oleg, hackathons are instrumental in driving innovation in systems development, testing, and refining infrastructure solutions under pressure akin to real-world challenges. He values the mentorship aspect of hackathons, seeing them as opportunities for tech professionals to exchange knowledge and mentor up-and-coming talent, fostering a supportive environment where learning and mutual support flourish among enthusiasts. Oleg’s insights underscore the multifaceted benefits of hackathons, from driving technological advancements to building a vibrant, collaborative community that transcends individual events.

But how does one organize a hackathon? Here’s a guide to getting started:

  1. Set your goals: Before diving in, figuring out what you want to achieve is crucial. Whether sparking innovation in a specific industry, tackling a social issue, or simply bringing the developer community closer together, having a clear goal will guide you every step. For the Hackathon Raptors, it’s always been about creating a welcoming space where developers can learn, collaborate, and make a difference.
  2. Pick a theme that speaks to you: Choosing the right theme can make or break your hackathon. It should be broad enough to inspire creativity but focused sufficient to provide direction. The Hackathon Raptors have organized events around themes like AI for Humanity and Web Accessibility, drawing in a diverse crowd of developers passionate about making a positive impact.
  3. Build your dream team: Organizing a hackathon is no small feat—it takes a dedicated team with a variety of skills. From event planning and marketing to technical expertise and community engagement, having the right mix of people is essential. The Hackathon Raptors thrive thanks to their diverse organizing team, bringing together different perspectives and talents to ensure their events run smoothly.
  4. Find support and partnerships: Sponsors and partners can provide the resources needed to make your hackathon a success. This includes everything from funding and technology to mentorship and prizes. The Hackathon Raptors have teamed up with companies and organizations that share their values, ensuring their events are well-supported and aligned with their community’s goals.
  5. Spread the word: Getting the word out is key to attracting participants. Utilize social media, online forums, and good old-fashioned word of mouth to generate buzz around your event. The Hackathon Raptors excel at drumming up excitement, using engaging content and personal stories from past participants to inspire others to join the fun.
  6. Create a collaborative atmosphere: Building an environment that fosters collaboration and innovation is essential for a successful hackathon. Offer resources like workshops, mentorship, and networking opportunities to support participants every step of the way. The Hackathon Raptors strongly emphasize inclusivity and support, ensuring everyone feels valued and welcome.
  7. Celebrate success: At the end of the day, it’s important to celebrate all participants’ hard work and achievements. Hand out prizes, showcase projects, and allow teams to continue working on their ideas. The Hackathon Raptors community is about sustainable development and innovation, encouraging teams to keep pushing forward even after the event.

But it’s not just about organizing—hackathons have birthed remarkable projects. Take the MSQRD app, conceived by just two developers, which aimed to revolutionize modern messaging for mobile devices. Despite requiring originally four team members, they hastily assembled a duo on the eve of the hackathon. MSQRD swiftly gained traction among celebrities flaunting its masks on social media, particularly in Europe and the US.

Intrigued by MSQRD’s technical prowess and user engagement, Facebook struck a deal with the developers, granting them access to its vast user base. With plans for future projects in the entertainment and social sectors, MSQRD now seamlessly integrates its features across platforms like Instagram and WhatsApp.

Hackathons have emerged as transformative forces in the tech world, transcending traditional boundaries to become inclusive platforms for innovation, collaboration, and community building. The stories of Maksim, Dmitry, Alim, Dmitrii, and Oleg exemplify individuals’ diverse perspectives and invaluable contributions to these events. From pushing the boundaries of technology to fostering personal and professional growth, hackathons continue to play a pivotal role in shaping the future of technology and empowering individuals to make a difference. As we look ahead, the Hackathon Raptors and similar communities stand as beacons of inspiration, driving positive change and helping us dream of what’s possible through the power of collaboration and innovation.


Featured image credit: Freepik

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Vitali Zahharov reveals how to be successful designer? A journey from Estonia to California https://dataconomy.ru/2024/04/17/vitali-zahharov-reveals-how-to-be-successful-designer-a-journey-from-estonia-to-california/ Wed, 17 Apr 2024 08:31:33 +0000 https://dataconomy.ru/?p=51086 Many people live according to predetermined schemes, move along beaten paths, and enter a matrix, just to not disturb their comfort zone. But there are others who choose different paths, new experiences, and life journeys. Vitali Zahharov is one of these people – a few years ago, his life changed dramatically, literally in a few […]]]>

Many people live according to predetermined schemes, move along beaten paths, and enter a matrix, just to not disturb their comfort zone. But there are others who choose different paths, new experiences, and life journeys. Vitali Zahharov is one of these people – a few years ago, his life changed dramatically, literally in a few days, and turned into a real adventure.

Vitali Zahharov reveals how to be successful designer? A journey from Estonia to California
Vitali Zahharov

“My journey into the world of design began in 2011. The laptop was always by my side, no matter if I was on vacation with my family or on an outing in nature. My friends often laughed at me that even when I was resting, I was working. But for me, it was a new world that I wanted to explore in-depth.” – Vitali said.

It starts from scratch, from ground zero. He doesn’t even know how to use Photoshop professionally, but with each new day, he learns more and more. He watches online tutorials, observes the work of leading designers and thinks: ‘One day I will do the same! Maybe even better!’

“Many people believe that mastering even just one design program makes them a designer. But this is not so – says Vitali. – It’s like not knowing how to drive and wanting to become a perfect driver with one tour of the city by car. It takes experience and hard work. As well as tenacity: to keep going and not give up.”

Vitali’s profession takes him and his family to Singapore. Today, he remembers this moment with a smile and warmth: “It was a turning point for me that affected my whole life. I started working with people from various fields and communicating with people from all over the world. It gave me a lot of experience. Today, I am already sure – only by working with people from different cultures and background on the project, makes you understand how unique every project is. Everyone is trying add something that he brought with them. And that’s so nice, because it expands the boundaries of your perception.”

Vitali Zahharov’s working day begins early, when there is still no one in the office. These days he drinks coffee and enjoys the peace and quiet. Then he gets down to his daily tasks: emails from clients, meeting with clients and team members, preparing and organizing tasks for fellow designers and so on.

But there is one important rule that Vitaly follows to this day. Lunch!

You should never forget about lunch. If you miss or are late with your lunch, it means that time has stopped listening to you. A timely lunch is a sign that everything is under control.”

The other tip Vitali gives is to create your own mini concepts that can easily be posted on social networks, because the real feedback from the other users will help you understand where your strengths are and where your weaknesses are.

Today Vitali works for as Art Director for MØDDEN, design studio from Los Angeles. He works with big name clients such as Samsung, Toshiba, UAPackaging,  CPK, Thrive Market, Oracle, Syn, and even with a Singapore Government. Vitali puts his soul into every project, without allowing the work to become a routine. He creates useful and beautiful solutions that expand the possibilities of the client. During the development of his career, Vitali is also tryting to help small start-up.

“One day I was highly valued by Silicon Valley’s billionaire!” – remembers Vitali. “Ad that was fantastic, it keeps you motivated doing great things! You just need to remember a few things like:”

  1. Never give up!
  2. Work and study!
  3. Do not blindly agree with the opinions of others. (Even if someone has told you no, do what it takes to make it your yes.)

In the end, the advice from Vitali Zahharov: “Always go to the goal, even if it will be a long way. Plan your tasks for the day, week and month. Write down in your journal what you did today. If something doesn’t work out today, it doesn’t mean you can skip the step towards the goal and move on to the next task.”


Featured image credit: Theme Photos/Unsplash

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How to drive AI and ML advancements on Big Tech with Radhika Kanubaddhi https://dataconomy.ru/2024/02/15/how-to-drive-ai-and-ml-advancements-on-big-tech-with-radhika-kanubaddhi/ Thu, 15 Feb 2024 14:26:57 +0000 https://dataconomy.ru/?p=58135 Radhika Kanubaddhi has significantly impacted the world of artificial intelligence (AI) and machine learning (ML) by working for some of the top technology companies. With a solid background in computer science, Radhika has delivered innovative solutions that have transformed how businesses operate. In this interview, she talks about her work at Epsilon, Microsoft, and Amazon, […]]]>

Radhika Kanubaddhi has significantly impacted the world of artificial intelligence (AI) and machine learning (ML) by working for some of the top technology companies. With a solid background in computer science, Radhika has delivered innovative solutions that have transformed how businesses operate. In this interview, she talks about her work at Epsilon, Microsoft, and Amazon, and shares insights on driving AI and ML advancements in Big Tech.

Could you tell us about your early work at Epsilon and how you contributed to AI and ML advancements there?
At Epsilon, I led a team that worked on developing cutting-edge ML recommendation engines. One of our most notable achievements was implementing a real-time recommendation engine for an airline, which resulted in a $214 million revenue increase within 30 days. Additionally, I piloted an email marketing campaign for a retail client with a 27% increase in orders over eight weeks. I also developed an email recommendation engine, contributing to a 31% lift in client ticket purchases. My work there focused on bridging technical solutions with measurable business outcomes.

How to drive AI and ML advancements on Big Tech with Radhika KanubaddhiHow did your work at Microsoft evolve, and what key projects did you work on there?
At Microsoft, I focused on building and deploying AI solutions, particularly enterprise-grade chatbots. One of my key projects was developing a cloud-native chatbot solution for a hospitality client, utilizing Azure QnA Maker and Azure LUIS. This project generated $1 million in annual revenue by helping the client adopt cloud solutions. My work at Microsoft involved understanding the needs of our clients and guiding them through implementing AI solutions that would enhance their operations. I was fortunate to work on natural language processing (NLP) technologies, which paved the way for more intuitive customer interactions.

What challenges did you face while developing these AI solutions at Microsoft?
Developing AI solutions often comes with challenges, especially when working on large-scale enterprise systems. One challenge was ensuring that the AI technologies were scalable and adaptable to meet clients’ evolving needs. Understanding AI and machine learning fundamentals helped me navigate these complexities. I also collaborated closely with client executives to ensure our solutions met their strategic goals.

What kind of work did you focus on at Amazon, and how does it connect with AI and database technology?
At Amazon, I developed database technology that supports AI applications used across Amazon’s platforms. One of my most significant achievements was the development of a high-efficiency database capable of operating with a single millisecond latency. This is a critical component for AI and machine learning applications, as they require real-time data access and processing capabilities. My work at Amazon centered around optimizing systems for speed and reliability to ensure AI applications function at their best.

As a woman in engineering, have you faced any challenges, and how have you worked to overcome them?
Yes, there have been challenges as a woman in engineering, which is still male-dominated. However, I’ve been fortunate to rely on a solid theoretical foundation in computer science and problem-solving to overcome these challenges. I also dedicate time to teaching high school girls about engineering and computer science to encourage more young women to explore STEM fields. Promoting diversity and inclusion in the tech industry is important, and I try to impact this area positively.

What excites you most about the future of AI, and what are your aspirations in this field?
I’m excited about the advancements in generative AI. AI has immense potential to revolutionize industries and create more intuitive and efficient solutions. Looking ahead, I hope to continue working on cutting-edge AI technologies and contributing to developing solutions that will benefit businesses and society as a whole. I also want to continue mentoring and encouraging more women to enter the AI and technology fields.

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Natural user interface: Is the understanding between the human and the machine already here? https://dataconomy.ru/2024/01/24/natural-user-interface-is-the-understanding-between-the-human-and-the-machine-already-here/ Wed, 24 Jan 2024 13:26:46 +0000 https://dataconomy.ru/?p=47598 In the dynamic landscape of artificial intelligence, the pursuit of seamless integration between humans and technology stands as a paramount goal. The ambition is to render interactions so natural that the utilization of cutting-edge technologies becomes second nature. To explore what the industry is heading towards here, I talked to one of the speakers at […]]]>

In the dynamic landscape of artificial intelligence, the pursuit of seamless integration between humans and technology stands as a paramount goal. The ambition is to render interactions so natural that the utilization of cutting-edge technologies becomes second nature. To explore what the industry is heading towards here, I talked to one of the speakers at our Epic AI Dev Summit,  Or Gorodissky, the Vice President of Research and Development at the D-ID company, the summit’s co-organizer. Or is an expert in Natural User Interface (NUI) technologies and has been developing Generative AI at D-ID since 2018.

Alex: What is the core vision behind the development of Natural User Interface (NUI), and how does it contribute to the broader landscape of AI agents?

Or: The vision behind the development of the Natural User Interface (NUI) is to revolutionize the way people interact with technology. NUI represents a significant leap from the previous interfaces, most notably GUI (Graphical User Interface), emphasizing natural, face-to-face conversations with digital entities. Our goal is to do away with the mouse and keyboard and replace them with an interface that allows you to “speak” with your devices directly, face-to-face, as you would with another human being. This approach humanizes digital interactions, making them more accessible, intuitive, and inclusive. It effectively bridges the gap between human and digital realms, enhancing user engagement and satisfaction across a wide range of business sectors.

Alex: What future advancements in AI and video generation are you most excited about, and how do you foresee the industry preparing for these upcoming changes?Or: The most exciting future advancements in AI and video generation relate to the creation of more immersive, human, and engaging interfaces. With technologies like Apple’s Persona avatar in its VisionPro, D-ID’s real-time interactive Agents, and Runway’s text-to-video generator, the industry is moving towards a more interactive and lifelike mode of communication. This evolution will likely see all companies leveraging these generative AI products to enhance customer interaction. I think that preparing for these changes involves staying updated with technological developments, investing in R&D, and ensuring that these new tools are accessible and adaptable to multiple business needs.

Alex: What are the obstacles faced in creating AI-generated video content, and potential solutions that can be applied universally?

Or: Creating high-quality videos using AI is still considered a  difficult task. Not all of the problems have been solved and developing solutions can take time. Many companies grapple with producing videos that are not only temporally consistent and high-resolution but also created with low latency or high throughput, all while keeping computational costs in check.

It’s a challenge to steer a company in a way that ensures that technical and product roadmaps both innovate and deliver impactful products.  To overcome this, we are focusing on cycles of innovation and improvement, prioritizing impactful efforts and strategically building towards future capabilities. Emphasizing user-centric design and leveraging existing solutions for non-core aspects help streamline the process.

Alex: Integrating AI technologies into existing systems and platforms is often complex. How does D-ID’s technology integrate with existing systems and platforms, and what are the challenges in these integrations?

Or: D-ID’s technology integrates with existing systems and platforms through its advanced API, designed to be flexible and user-friendly. This API allows for seamless integration of our AI capabilities, enabling businesses to personalize their AI experiences and align them with specific needs and audiences. The main challenge in these integrations, we believe, is ensuring compatibility and maintaining the balance between technological sophistication and user experience. Our approach focuses on making these integrations as intuitive and straightforward as possible, providing tools and solutions that tailor our capabilities to each user’s unique requirements.

Alex: Staying ahead in the rapidly advancing field of AI is crucial. What general strategies should companies employ to remain at the forefront of AI technology?

Or: Well, it’s risky to rely solely on technical superiority as everything you build will eventually become a commodity. It may take some time, years if you’re lucky, but you won’t get a lot of sleep if every time a new research paper comes out you’ll question your business strategy.

Instead, try to be laser-focused and user-centric. Double down on the things that bring value and leverage existing solutions when the value doesn’t justify the effort. Technology in and of itself is not a silver bullet. Make sure that both the product and business aspects are constantly addressed to ensure your effort is most effective.

You want your users to stay with you even when the next big open-source solution comes out. Think about that when you choose where to invest your focus.

Alex: Ethical considerations are crucial in AI development. How do you believe the industry should ensure ethical practices in the creation and deployment of AI technologies?

Or: Yes, of course, ethical practices must be a constant touchstone for AI developers. This means ensuring a commitment to transparency, respect for privacy, and adherence to ethical standards. I believe, companies should work closely with privacy experts and ethicists to establish and follow strict guidelines. Regular audits and moderation, along with collaborations with regulatory bodies, can ensure responsible AI development. Additionally, the implementation of tracking systems, watermarks, and content moderation tools can help mitigate misuse. It’s crucial for industry leaders to lead by example, creating a culture of ethical AI use that balances innovation with responsibility and public trust.

Alex: Could you share a memorable success story or a particularly innovative use case of D-ID’s technology in action?

Radio Fórmula, a renowned media entity in Mexico’s Grupo Fórmula network, leveraged D-ID’s technology to create AI-generated newscasters, revolutionizing their news broadcasting approach. This collaboration led to a notable surge in engagement from younger audiences, demonstrating the impactful fusion of traditional media with advanced AI technology. For a detailed exploration of this innovative venture, you can read the full case study on D-ID’s website: Radio Fórmula and D-ID Case Study.


On January 30, 2024, Or will share more of his insights about NUI at our Epic AI Dev Summit, presenting his talk “Crafting AI agents with a natural user interface”. Full agenda and registration here!

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Breaking down the talk: Expert analysis of audio transcription and speaker identification tools https://dataconomy.ru/2023/12/15/breaking-down-the-talk-expert-analysis-of-audio-transcription-and-speaker-identification-tools/ Fri, 15 Dec 2023 14:00:58 +0000 https://dataconomy.ru/?p=57376 In today’s data-driven landscape, audio recordings hold a wealth of untapped knowledge.  From corporate boardrooms to research laboratories, the ability to accurately transcribe spoken words and discern individual speakers is invaluable.  Audio transcription and speaker identification technologies have emerged as powerful tools for extracting meaningful information from these recordings, transforming raw audio data into actionable […]]]>

Andrey G

In today’s data-driven landscape, audio recordings hold a wealth of untapped knowledge.  From corporate boardrooms to research laboratories, the ability to accurately transcribe spoken words and discern individual speakers is invaluable.  Audio transcription and speaker identification technologies have emerged as powerful tools for extracting meaningful information from these recordings, transforming raw audio data into actionable insights.

Guiding us through this intricate field is Andrey Gushchin, a Product Manager and distinguished expert in speech recognition. Possessing extensive experience and a profound comprehension of these technologies, Andrey will illuminate the latest advancements, address existing challenges, and offer insights into future trends in this dynamic realm.

Q1: All right, let’s get into some of the core technologies. Everybody’s talking about ASR and speaker diarization. How do these technologies work together to give us these really powerful tools?

Absolutely. ASR stands for Automatic Speech Recognition, which is basically transcribing spoken language to text. This has increased so much within the past few years, thanks to deep learning, especially with transformer-based models like BERT and GPT. This kind of model has a very remarkable ability to understand the context and nuances of human speech, hence enabling high accuracy in transcription.

On the other hand, speaker diarization is the process of identification and segmentation of audio with regard to who is speaking. It’s a kind of virtual detective that analyzes the audio stream in search of unique vocal characteristics like pitch, rhythm, and timbre to find out the differences between speakers. It’s complex, especially in noisy environments or with overlapping speech, but recent breakthroughs in techniques like i-vectors and x-vectors make it increasingly reliable.

By putting those two technologies together, a user retains the ability not only to transcribe audio recordings but to attach each segment of this transcript to the relevant speaker. This is comparable to detailed minutes of a meeting, including what was said and who said it.

Q2: Transcription requires an extremely high degree of accuracy. How have these tools safeguarded measures that ensure accuracy in the most trying conditions?

Developers always go about pushing these accuracies to the limits with a cocktail of techniques that includes noise reduction, beamforming to preprocess the audio and isolate speech signals against background noises. Advanced speaker diarization models capable of handling overlaps in speech and fine differences in voices, and language models working at refining this transcript for error correction and fluency against the contextual information and patterns in a language.

Another very promising area of study is that of multi-channel audio processing, where information from multiple microphones can be utilized to improve speech separation and speaker identification. In addition, voice activity detection and endpointing enable the identification of the exact start and end of the turns of each speaker, hence increasing its accuracy.

Q3: That’s interesting! Well, privacy is a big worry when it comes to audio data. How do these tools deal with that?

Of course, privacy is at the top of the list for people who make these tools. They’re building privacy into the design from the start, collecting as little data as possible, putting in place tight controls on who can access what, and keeping logs to track anyone who interacts with the data. However, implementing on-site solutions remains the most reliable way to guarantee privacy. Some companies cannot allow their data to leave their perimeter. While cloud-based solutions can also be made private through differential privacy and federated learning, on-site setups ensure that sensitive data never leaves the organization’s control.

Differential privacy adds some noise to the data, making it hard to figure out who’s who. Federated learning lets models learn from data spread out in different places, so sensitive info stays on the user’s device. Adapting such techniques can help cloud solution developers attract customers who value privacy.

Q4: We’ve seen a rise in transcription services . What makes these new tools stand out?

Speaker labeling is the main thing that sets them apart. Old-school transcription services usually give you a basic transcript without saying who’s talking. These new tools go further giving you a detailed record of who said what, which gives you valuable context and insights.

They also focus on making things easy for users, with features like transcribing as you speak, labels for speakers you can change, and smooth connections with other tools to boost productivity.

Q5: Are there any potential uses for this technology besides simple transcription?

Limitless. One day you might search through your audio files, just like accessing information online. You can now easily find that one significant quote or piece of advice. Or identify a tool that offers real-time insights during calls. It highlights important points, summarizes tasks and evaluates the tone pertaining to emotional aspects concerning conversations.

Such technology could be incorporated into voice-activated software programs. Therefore, they would comprehend commands and articulate replies more effectively within particular contexts. Also, it can be utilized in health facilities so as to transcribe alongside analyzing interactions between doctors and their patients. Thus aiding patients’ diagnosis and treatment processes. Education is another area where personalized learning experiences can be created. Learners can use their voice to access learning materials, receive individualized responses.

About the expert: Andrey, a dynamic Technical Project Manager passionate about turning ideas into reality, excels at guiding complex technical projects from concept to launch. Currently making waves at JetBrains, shaping the future of CLion, he has a proven track record of success. He masterminded the launch of the Yandex Monitoring service, optimized a massive internal monitoring system, drove the adoption of an internal “status page as a service,” and orchestrated a seamless migration to an internal CI system, achieving a 10x capacity increase. Andrey thrives in collaborative environments, bridging the gap between business goals and technical execution to deliver solutions that exceed user expectations.


Featured image credit: Sebbi Strauch/Unsplash

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Global DevSlam returns to unite the leading minds of innovators https://dataconomy.ru/2023/10/13/global-devslam-returns-to-unite-the-leading-minds-of-innovators/ Fri, 13 Oct 2023 08:41:49 +0000 https://dataconomy.ru/?p=43172 Global DevSlam is back, setting the stage for a unique gathering of tech aficionados from all corners of the world. Supported by Coders HQ—a standout initiative by the UAE government—and the Python Software Foundation, this year’s event is designed to cultivate innovation and foster meaningful connections. Taking place at the bustling Dubai World Trade Center […]]]>

Global DevSlam is back, setting the stage for a unique gathering of tech aficionados from all corners of the world. Supported by Coders HQ—a standout initiative by the UAE government—and the Python Software Foundation, this year’s event is designed to cultivate innovation and foster meaningful connections.

Taking place at the bustling Dubai World Trade Center from October 16-20 and co-located with GITEX, Global DevSlam is dedicated to offering a diverse platform that encourages widespread participation. The event beckons a rich tapestry of insights from C-level executives, government representatives, and a broad spectrum of international coding professionals.

With a focus on a harmonious blend of deep knowledge sharing and transformative dialogues, the conference aims to provide a practical, engaging, and enlightening experience for all its attendees. As a hub for exploration, learning, and networking, Global DevSlam is set to showcase an array of tech expertise and collaborative endeavors.

During a special pre-event interaction, we delved into the perspective of JJ Asghar, a Developer Advocate at IBM. Hailing from Austin, Texas, JJ brings a robust reservoir of expertise in DevOps and SRE, paired with an intense enthusiasm for the ever-growing realm of AI. He shares diverse insights as he gears up to enlighten the Global DevSlam audience—from his personal aspirations and cultural immersion at the event to in-depth musings on trending industry topics like Cloud Native and Serverless Applications.

JJ’s participation will be a journey of exploration and innovation, contributing tales of unexpected discoveries in serverless applications and insights into the transformative facets of AI. He will join other esteemed speakers in guiding pivotal dialogues on AI, emphasizing the essential role of ethical and responsible practices in technology’s evolution.

We invite you to join us in exploring JJ’s insights and forecasts as they shape the course of what promises to be an unforgettable experience at Global DevSlam:

1. Is this your first time attending Global DevSlam? What are your expectations for the event, and what insights or experiences should participants from around the world anticipate?

JJ Asghar, Developer Advocate, IBM
JJ Asghar

Yep! I’m a Developer Advocate from IBM, living in Austin, Texas. I never once in my life thought I could make it over to the UAE, and speak no less. Thank you for having me! My expectations of the event are not only to learn about the culture in the UAE, but to learn how I can engage in the space from a technology standpoint. I’m passionate about this AI ecosystem, and I want to make the biggest impact I can.

2. What are the highlighted discussion topics at this year’s Global DevSlam, and are there any that resonate with you particularly?

As much as Cloud Native is still “hot” it’s incredible how many people genuinely don’t understand that it’s a journey, not a “goal.” In order to grasp the advantages of Cloud Native, you have to first take stock of what you are trying to accomplish and what you already have invested in your development and infrastructure.

So many times, I’ve helped advise clients and/or companies that “You need to do your homework first” before you buy that OpenShift/Kubernetes cluster. Figure out what you’re trying to do, and maybe you should be looking at your next project, not this one. On top of that, too, I’ve learned to give people/clients/companies permission to realize that Cloud Native is hard. It isn’t like any other way of running your application and requires a real investment of time and effort to be successful. It’s amazing how many people don’t actually say this and give a false sense of security, saying, “just by my thing, and everything will be ‘ok’”.

Our team at IBM helps businesses navigate this process and implement hybrid cloud infrastructure to ensure security and make it as simple as possible for their development teams to manage.  

3. Could you provide some insights into the presentation or talk you’ll be delivering at Global DevSlam?

I’m going to be talking about how we accidentally created a Serverless application, and surprisingly a very common journey for developers to find themselves in the serverless space. I’m a highly experienced DevOps/SRE engineer, so it has a vector of how leveraging DevOps software can get you only so far, and before you know it, you’re building an application to make your life easier. And before you know that, you’ve accidentally created a Serverless app!

4. What industry trends or shifts are currently on your radar, and why do they excite you?

Frankly, I’m very excited about the AI space. With Generative AI all over the world and people are starting to pay real attention to our industry as a whole. That’s actually not what excites me, it’s more the aspect that now as technologies, we have the chance to teach and engage with non-techies in a way to understand what we do day in and day out. Nerd stuff is cool to nerds, but when we get say my parents interested, now I can show it to anyone.

This also ties in with how everyone needs to use this AI space responsibly. Something I genuinely love about IBM watsonx is that we really are the trusted AI platform. We have the datasets and the training of our foundational models there, so you can verify that our models are fair/balanced and built the way real enterprises and privacy-concerned institutions need. 

5. Gen AI is significantly shaping our global landscape. How do you view its influence on individuals, communities, and the broader enterprise world?

Honestly, that’s one reason why I’m so excited about coming to Gitex. Where I’m located in Texas, the AI space is just budding, and the community is small. I’m hoping through the lens of Gitex and AI Everything that, I’ll be able to find influencers to learn from!

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From code to cloud: The story of Vishal Shahane’s impact on AWS and beyond https://dataconomy.ru/2023/09/11/from-code-to-cloud-the-story-of-vishal-shahanes-impact-on-aws-and-beyond/ Mon, 11 Sep 2023 14:29:30 +0000 https://dataconomy.ru/?p=51521 In today’s age of digital technology, cloud computing stands as the backbone of modern technology and its marvels. And in the ever-changing expansive landscape of cloud computing, few names resonate as profoundly as Amazon Web Services (AWS). Among the myriad of minds shaping the future of cloud technology, one stands out: Vishal Shahane, a Senior […]]]>

In today’s age of digital technology, cloud computing stands as the backbone of modern technology and its marvels. And in the ever-changing expansive landscape of cloud computing, few names resonate as profoundly as Amazon Web Services (AWS). Among the myriad of minds shaping the future of cloud technology, one stands out: Vishal Shahane, a Senior Software Engineer whose contributions have left an incredible impression on the industry.

With a career stretching nearly 16 years, Vishal Shahane’s voyage is a testament to the power of curiosity, perseverance, and relentless pursuit of perfection. From his humble upbringing to his impactful leadership roles at AWS, Vishal’s story is one of continuous iterative innovation and commendable achievements.

The man behind the code

Vishal Shahane’s journey in the tech world is nothing short of phenomenal. Armed with a Master’s degree in Electrical and Computer Engineering from Carnegie Mellon University and an MBA from Porto Business School, Vishal’s academic foundation laid the groundwork for his outstanding career.

His voyage into the domain of cloud computing commenced at Tata Consultancy Services, where he honed his crafts in developing services and IT infrastructure for clients in the banking and entertainment sectors. However, it was his tenure at AWS that would push him into the spotlight.

Architecting the future of cloud services

From code to cloud: The story of Vishal Shahane's impact on AWS and beyondAt AWS, Vishal Shahane arose as a trailblazer, spearheading key initiatives within the EC2, Lambda, and S3 departments. Amazon S3 holds over 350 trillion objects, exabytes of data and averages over 100 million requests per second. For comparison, google searches were at 99k per second in 2023. Vishal’s code and services built by him touch these objects stored at S3. His contributions were instrumental in shaping the core functionalities of these foundational cloud services, which are essential to the operations of millions of companies worldwide.

One of Vishal’s most significant endeavours was his role in revolutionising the AWS Lambda architecture. By introducing a groundbreaking design that incorporated lightweight virtual machines, innovative thinking and his patented resource reclamation and dynamic memory reallocation technology. Vishal improved resource utilisation and dependability, propelling Lambda into profitability.

Moreover, Vishal’s commitment to improving service reliability and transparency at AWS EC2 showcases his dedication to excellence. By widening the scope of reliability metrics and simplifying root cause analysis processes, Vishal has set new benchmarks for functional efficiency and client satisfaction.

Inspiring the next generation

Outside his technological prowess, Vishal Shahane is a mentor and advocate for the next generation of software engineers. His devotion to sharing knowledge and promoting inclusive environments highlights his conviction in the power of teamwork and diversity in driving innovation.

Peeking ahead, Vishal’s aspirations lie in growing into leadership roles within the tech industry, continuing to stretch the limitations of what is feasible with technology and encouraging others to do the same.

Unlocking the possibility of cloud computing

On a planet increasingly reliant on digital infrastructure, techies like Vishal Shahane are at the forefront of shaping the future of cloud computing. Through his innovations, leadership, and tireless commitment to excellence, Vishal has not only altered the landscape of cloud services but also motivated a new generation of tech lovers to dream bigger and aim higher.

As we glance at the horizon of cloud computing, one thing is sure: the legacy of Vishal Shahane will continue to radiate brightly, lighting the path ahead for generations to come.

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How technology transforms public administration and investment management – Insights from Asatrian Sergei Tigranovich https://dataconomy.ru/2023/04/04/how-technology-transforms-public-administration-and-investment-management-insights-from-asatrian-sergei-tigranovich/ Tue, 04 Apr 2023 17:14:16 +0000 https://dataconomy.ru/?p=52120 Technology continuously transforms how we approach efficiency and strategy; the public administration and investment sectors are no exception. Asatrian Sergei Tigranovich is a seasoned expert with a strong state and municipal administration background. He brings a unique perspective on how advanced technologies such as data science and artificial intelligence (AI) can enhance decision-making processes, ensure […]]]>

Asatrian Sergei TigranovichTechnology continuously transforms how we approach efficiency and strategy; the public administration and investment sectors are no exception. Asatrian Sergei Tigranovich is a seasoned expert with a strong state and municipal administration background. He brings a unique perspective on how advanced technologies such as data science and artificial intelligence (AI) can enhance decision-making processes, ensure transparency, and promote public trust.

In this exclusive interview, Sergei delves into the potential impacts of these technologies on his field. He also discusses the ethical considerations of data usage and forecasts the transformative power of AI in public engagement and infrastructure planning. We will explore the intersection of technology and public administration through the insightful views of a seasoned expert who stands at the forefront of this evolution. Join us for this intriguing discussion.

  1. Given your extensive background in administration and management, how do you envision specific data science tools, such as predictive analytics, machine learning, and data visualization, and methodologies like data mining and big data analysis, could enhance public administration and investment management?

While my primary field isn’t data science, I firmly believe in the transformative power of these tools for public administration and investment management. By integrating data analytics into our operations, we can significantly enhance our decision-making processes, making them more transparent, efficient, and accurate. Consider predictive analytics—it’s a tool that could revolutionize our ability to forecast tax revenue and economic trends, thereby refining our approach to budget planning and fiscal policies. This potential empowers us to make more informed decisions, confident in the accuracy and reliability of our data-driven insights.

  1. Can you discuss a project or initiative where you integrated technological solutions to improve outcomes in your roles in tax enforcement or investment management?

During my tenure in the Investment Management department, we employed basic data analysis tools to evaluate the feasibility of various investment projects. This method was crucial in pinpointing the initiatives that were most likely to succeed based on a range of economic indicators. Moving forward, adopting more sophisticated data science techniques would enhance our ability to delve deeper into assessing potential risks and benefits, thereby sharpening our investment decisions.”

  1. How important do you think the role of ethical considerations and data privacy is in the public sector, particularly in your areas of expertise?

Ethical considerations and data privacy are not just important, they are paramount, especially in the public sector, where decisions have far-reaching impacts on the community. In my roles, I’ve prioritized ensuring the secure handling of data and upholding strict privacy standards to protect individuals’ information. This approach doesn’t just safeguard personal data—it also strengthens the public’s trust in our governmental institutions. As technology continues to evolve, we must remain vigilant in upholding these ethical standards.

  1. Artificial Intelligence (AI) is becoming increasingly influential in various sectors. How do you see AI impacting investment management and public administration in the near future?

The potential for AI to revolutionize fields like investment management and public administration is immense. In investment management, AI can provide tools for real-time data analysis, risk assessment, and predictive analytics, all of which make the investment process more dynamic and accurate. For public administration, AI offers opportunities to streamline operations, from automating mundane tasks to improving how services are delivered to the public. Additionally, AI’s capabilities in simulations and modeling could be invaluable in urban planning, helping us anticipate and plan for urban development needs more effectively. Embracing AI enables more informed, data-driven decision-making and can be instrumental in identifying and addressing issues before they escalate.


Featured image credit: vecstock/Freepik

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Ahmed Tarek explores the revolutionizing industries and transforming daily life with 5G, IoT, and AI integration https://dataconomy.ru/2023/01/17/ahmed-tarek-explores-the-revolutionizing-industries-and-transforming-daily-life-with-5g-iot-and-ai-integration/ Tue, 17 Jan 2023 12:32:16 +0000 https://dataconomy.ru/?p=51616 Artificial Intelligence (AI), the Internet of Things (IoT), and 5G connectivity make innovation strategies run smoothly and be error-free. Therefore, modern companies have optimized integration approaches, radically enhanced connectivity, and allowed independent thinking and creativity in the workplace. In this article, Ahmed Tarek, a telecommunications expert who has developed data pipelines for several organizations and […]]]>

Artificial Intelligence (AI), the Internet of Things (IoT), and 5G connectivity make innovation strategies run smoothly and be error-free. Therefore, modern companies have optimized integration approaches, radically enhanced connectivity, and allowed independent thinking and creativity in the workplace.

Ahmed TarekIn this article, Ahmed Tarek, a telecommunications expert who has developed data pipelines for several organizations and created infrastructure solutions for various clients, will explore the intricacies of the three pioneering technologies and how they redefine everyday experiences.

5G’s lightning-fast connectivity

5G networks have opened a portal for rapid connections in countless facets. This wireless technology has allowed devices to connect to radio networks with ultra-low latency, ensuring homes and businesses easily connect to the internet to have a reliable bandwidth. As such, 5G users can transmit data at speeds between 10 and 20 gigabytes per second.

In the telecom landscape, 5G systems allow IoT devices to experience real-time data exchange and decision-making. This means that they can communicate properly with monitoring systems in smart homes and automate processes in industrial settings. IoT systems usually have sensors and control commands so consumers can accurately monitor and collect data about them.

AI’s intelligence and automation

AI is a significant innovation in the digital landscape because of its automation powers and exceptional ability to learn and adapt to the challenges trainers present. It is a beneficial tool to engineers because it can quickly analyze data from IoT devices or predictive scenarios and generate reports regarding trends and algorithms. Artificial intelligence is a continuous learning system powered by high-computing capabilities and training models, enforcing its position as a powerful tool in spearheading operations.

Industry transformations

5G, IoT, and AI are collectively remodeling traditional industries because of their new capabilities. For instance, intelligent factories can leverage the synergy from integrating the three technologies to build simulations that help them analyze resource allocation, risk assessments, and processes. They can quickly transmit data between devices throughout their systems, whether dispersed or as a single unit, and optimize their supply chain processes, predictive maintenance, and power consumption.

Smart cities and consumer experiences

In the smart city scenario, urban operations, such as traffic flow, rely on IoT, 5G, and AI synergy to make informed decisions. For example, by studying traffic lights, engineers can use AI to develop predictive analytics and optimize their infrastructure, thus making transportation safer and more sustainable.

Consumers value leisure and convenience, so those with smart homes may utilize IoT devices and AI assistants for personalized experiences. Some key factors they may consider are home security, entertainment, and comfort, which IoT and AI help accomplish with their smooth integration into the home.

Challenges and opportunities

While bringing together the three forces offers lots of chances for different people involved, dealing with its challenges might be easier. Data privacy breaches, cybersecurity risks, and ethical dilemmas are key problems when IoT, AI, and 5G integration take place. Its implementation would consume high energy and potentially result in signal interference. Because tech is not unsusceptible to cyber attacks, security vulnerabilities exist with the interconnectivity between IoT networks on the 5G spectrum bands.

About Ahmed Tarek

Ahmed Tarek has accomplished his career path backed up with a degree in Electrical, Electronics, and Communications Engineering and expertise gained through engineer positions at companies operating globally.

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Global DevSlam will explore the quest to make AI trustworthy and responsible https://dataconomy.ru/2022/10/03/global-devslam-will-explore-the-quest-to-make-ai-trustworthy-and-responsible/ https://dataconomy.ru/2022/10/03/global-devslam-will-explore-the-quest-to-make-ai-trustworthy-and-responsible/#respond Mon, 03 Oct 2022 14:50:17 +0000 https://dataconomy.ru/?p=29837 The biggest meet-up for the developer community, Global DevSlam, is just days away, and the already jam-packed calendar of the event is getting richer with new speakers, sessions, and details. Global DevSlam will bring together over 15,000 attendees from 170 countries, including the world’s greatest coding talents, decision-makers, and visionaries, at the Dubai World Trade […]]]>

The biggest meet-up for the developer community, Global DevSlam, is just days away, and the already jam-packed calendar of the event is getting richer with new speakers, sessions, and details.

Global DevSlam will bring together over 15,000 attendees from 170 countries, including the world’s greatest coding talents, decision-makers, and visionaries, at the Dubai World Trade Center from October 10-13. Global DevSlam will also host the PyCon MEA with the collaboration of the Python Software Foundation.


5 REASONS WHY YOU SHOULD NOT MISS THE GLOBAL DEVSLAM


The conference agenda at Global DevSlam will be jam-packed with industry-leading discussions on Python, artificial intelligence, machine learning, blockchain, DevOps, Javatalks, metaverse, mobile, NFTs, gaming, quantum computing, cloud, Kubernetes, and many more disruptive technologies and many other disruptive trends.

Dr. Seth Dobrin is one of the exciting names that will take the stage at Global DevSlam. He will present how to implement responsible and human-centric AI to benefit more from artificial intelligence. Dobrin, a pioneer in artificial intelligence, data science, and machine learning, answered Dataconomy’s questions before the big event.

1. Can you share your responsible AI perspective with us? 

dr-seth-dobrin
Dr. Seth Dobrin

Trust is essential to human beings: trust encapsulates aspects of humanity that define a responsible attitude that is inclusive and fair: without trust, relationships stall, and transactions fail. Trust and responsibility are vital aspects of our online and offline life, without which normal operations would come to a grinding halt. As technological advances continue at pace, one of the forces behind innovation is the application of artificial intelligence (AI). But AI has not had an easy ride, with issues originating from a lack of trust by design. As a result, ethical issues have blighted the image of AI, with concerns ranging from using AI to manipulate behavior to inherent racial and sex bias. As humans expand our technology repertoire to include AI-enabled systems, these systems must be fair, responsible and inclusive. To help the industry reach this objective and need the Responsible AI Institute has built a schema to score AI systems:

  • Ensure explainable and interpretable AI systems
  • Measure bias and fairness of AI systems
  • Validate systems operations for AI systems
  • Augment robustness, security, and safety of AI systems
  • Deliver accountability of AI systems
  • Enable consumer protection where AI systems are used

2. What does adopting responsible AI approaches promise for enterprises?

In short, better business value and this is backed by several pieces of research from PwC and MITSloan/BCG, whom both demonstrated that businesses placing a priority of responsible implementation of AI systems yield better business value, including better products and services, enhanced brand reputation, and accelerated innovation.

3. To achieve these benefits, what should enterprises consider in implementing responsible and human-centric AI?

Step 1: get a baseline – perform a responsible AI organizational maturity assessment

Step 2: Set clear AI policies, strong AI governance, and appropriate controls – policies, governance, and controls drive innovation as they set clear lanes for your internal teams and expectations for the humans your business interacts with.

Step 3: Maintain an inventory of automated systems – AI is built, acquired, and used widely across most enterprises, but there is not a central management of these assets.  This inventory needs to be built and maintained and should be part of the funding and procurement processes.

Step 4: Assess automated systems – especially where they impact the health, wealth, or livelihood of a human, AI, and automation in general needs to be understood on the six responsible AI dimensions or explainability and interpretability, bias and fairness, systems operations, robustness, security and safety, accountability and consumer protection.

4. What is the difference between responsible AI and trustworthy AI?

Trust is a component of responsibility. When organizations talk about trust, they are generally talking about the technical aspects of an AI system and not necessarily the organizational, process, or workflow aspects.

5. Would it be right to call the human-centered AI field a milestone toward artificial general intelligence, digressing from the vision of more autonomous and godlike AI forms?

We have been talking about AGI for more than 50 years. At that time, we keep saying it was 50 years away; the consensus is it is still 50 years away or more. There are many things we need to achieve technically for AGI to be a reality. On top of the technical aspects, there are many processes, workflow, and societal aspects that need to be considered while we are on that journey – responsibility and the human impact are definitely core considerations.

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PyCon will make its MEA debut with the Global DevSlam https://dataconomy.ru/2022/09/28/pycon-mea-2022-is-counting-down-the-days/ https://dataconomy.ru/2022/09/28/pycon-mea-2022-is-counting-down-the-days/#respond Wed, 28 Sep 2022 09:51:57 +0000 https://dataconomy.ru/?p=29556 Global DevSlam will bring together more than 15,000 participants from 170 countries, including the coding world’s leading talents, decision-makers, and visionaries, at Dubai World Trade Center between October 10-13. The mega event will also feature the popular Python community conference PyCon in the MEA region for the first time. PyCon MEA will be held with […]]]>

Global DevSlam will bring together more than 15,000 participants from 170 countries, including the coding world’s leading talents, decision-makers, and visionaries, at Dubai World Trade Center between October 10-13. The mega event will also feature the popular Python community conference PyCon in the MEA region for the first time.

PyCon MEA will be held with the collaboration of the Python Software Foundation and Global DevSlam. PyCon has held successful Python community events in over 50 countries. The conference will feature 80 speakers and more than 100 hours of interactive learning opportunities.

We recently spoke with the regular keynote speaker of the PyCon events, David Mertz, about what to expect from PyCon MEA and the latest trends in development with Python.

1. Python is very popular in today’s emerging fields, such as data science, analytics, machine learning, and others. This may sound cliché, and you’ve probably answered this question countless times before, but we would love to hear it from you anyway: What makes Python so popular in these and other fields where it is a popular choice, and what advantages does it offer to developers?

David Mertz
David Mertz

I cannot speak for everyone, but for myself—as a member of the Python community for more than 20 years—a large part of what drew me to Python was precisely its community.  Pythonistas, by an large, are friendly, caring, cooperative, but also brilliant, innovative, and dedicated to our technical work. In more narrowly technical terms, Python has benefited greatly from popular scientific computing libraries like NumPy, Pandas, Matplotlib, and more recently PyTorch and TensorFlow.  The language itself is well designed and flexible, but the numeric tooling developed for it has made it the obvious first choice for data science and other scientific fields.

2. The world-renowned PyCon event series is coming to the MEA region for the first time. What should attendees expect from this great event?

I have worked with the organizers of PyCon MEA to move the focus of GITEX at least partially towards developers and the general Python community in its PyCon MEA program.  We have attracted many of the most prominent people in Python communities worldwide as speakers—most of them my friends and long-time colleagues, I can say proudly—which I think will make this event one where developers can share technical thoughts, and not only for managers to share product pitches.

3. In your opinion, what are the prominent trends affecting development with Python, what should Pythonistas keep their eyes on?

Python encompasses so many different software domains, that it is hard to name just one main trend.  I myself have worked largely in numeric and scientific computing, but Python is equally prominent in web applications, in education, in computer science, and in bread-and-butter commercial applications.

In all of these areas, the breadth and quality of supporting libraries are central to Python’s popularity.  Within the language itself, however, I think the growth of type annotations and gradual static typing have made the language more appealing to large application developers who have historically had a resistance to purely dynamic languages.

4. Would you like to add anything else about Python, PyCon MEA 2022, and/or Global DevSlam?

I would love it if PyCon MEA succeeds in reaching out to the developing world, and most especially to Africa (part of the name “Middle East & Africa”, after all) which has seen an amazing and rapid growth in developer communities and conferences throughout the continent, often drawing multi-national and continent-wide audiences to events.

There are many reasons to attend the world’s biggest coding and development networking event, and here are five prominent reasons not to miss the Global DevSlam.

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Chris Latimer tells how to use real-time data to scale and perform better https://dataconomy.ru/2022/04/13/how-to-use-real-time-data-to-scale/ https://dataconomy.ru/2022/04/13/how-to-use-real-time-data-to-scale/#respond Wed, 13 Apr 2022 13:35:25 +0000 https://dataconomy.ru/?p=23128 Real-time data is more critical than ever. We need it for quick decisions and pivot timely. Yet, most businesses can’t do this because they must upgrade their software and hardware to cope with real-time data processing’s demanding performance and scale standards. And when they can’t, we are left with stale data. DataStax recently announced its […]]]>

Real-time data is more critical than ever. We need it for quick decisions and pivot timely. Yet, most businesses can’t do this because they must upgrade their software and hardware to cope with real-time data processing’s demanding performance and scale standards. And when they can’t, we are left with stale data.

DataStax recently announced its Change Data Capture (CDC) feature for Astra DB, which brings data streaming capabilities built on Apache Pulsar to its multi-cloud database built on Apache Cassandra.

The new functionality offers real-time data for use across data lakes, warehouses, search, artificial intelligence, and machine learning by processing database changes in real-time via event streams. It will enable more reactive applications that can benefit from connected real-time data.

Solving today’s problem: How to use real-time data?

To get more details on the matter, we had a chance to talk with Chris Latimer, Vice President of Product Management at DataStax, about their new offering and the current landscape in data.

Chris Latimer tells how to use real-time data to scale and perform better

Can you inform our readers about the current market in real time data streaming?

The demand for real-time data streaming is growing rapidly. Chief data officers and technology leaders have recognized that they need to get serious about their real time data strategy to support the needs of their business. As a result, business leaders are putting more and more pressure on IT organizations to give them faster access to data that’s reliable and complete. As enterprises get better at data science, being able to apply AI to augment data in real time is becoming a critical capability that offers competitive advantages to companies that master these techniques and pose a major threat to companies that can’t.

How does real time data streaming affect user experience?

We’ve grown accustomed to data streaming in the consumer apps that we all use. We can watch the driver’s location when we’re ordering food or when we’re waiting for an online purchase we made to be delivered. While those features have clearly improved our experience, they also provide valuable data that can be used later to create second and third order effects which have less obvious impacts on user experiences.

These effects range from new optimizations that can be made by recording data streams. For example, a food delivery service might analyze driver location, drive times, selected routes and start incentivizing customers to order from restaurants which have a lower overall delivery time, letting drivers complete more deliveries and reducing wait times for consumers.

Likewise, in applications such as retail, capturing clickstream data from the collective audience of shoppers in an e-commerce app can enable retailers to select the best offers to put in front of customers to improve conversions and order size. While consumers are now accustomed to and demanding these types of interactions, many of these improvements are invisible and the end user sees a relevant discount on a product on food or clothing that can get delivered to them quickly

How did Pulsar tech help you build the new CDC feature?

Pulsar is the foundation for these new CDC capabilities. With Pulsar we’re able to offer customers more than just a CDC solution; we’re able to offer a complete streaming solution. This means that customers can send data change streams to a wide range of different destinations such as data warehouses, SaaS platforms or other data stores. They can also build smarter data pipelines by leveraging the serverless function capabilities built into our CDC streaming solution. Better still, changes are recorded so users can replay those change streams to do things like train ML models to create smarter applications.

Chris Latimer tells how to use real-time data to scale and perform better
Chris Latimer tells how to use real-time data to scale and perform better

How will your users benefit from this new feature?

This feature makes it a lot easier for users to build real time applications by listening to change events and providing more responsive experiences. At the same time, it provides the best of both worlds to users that need a massively scalable, high performance, best of breed NoSQL solution while delivering that data throughout the rest of their data ecosystem.

If you compare your CDC solution with others, what are the advantages?

The biggest difference is that DataStax is providing a comprehensive event streaming platform as part of CDC. Other solutions out there provide a raw API that sends changes as they happen. With CDC for Astra DB, we offer customers all the tools needed to quickly connect their change data streams to other platforms with a library of connectors and a full serverless function platform to facilitate smarter real time data pipelines.

We also provide developer friendly libraries so that change streams can power real time applications in Java, Golang, Python, Node.js and other languages.

With the ability to replay change streams, DataStax also offers differentiated capabilities for organizations as they build machine learning algorithms and other data science use cases.

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Ken Jee explains how to build a career as a data scientist https://dataconomy.ru/2022/03/22/ken-jee-explains-how-to-build-a-career-as-a-data-scientist/ https://dataconomy.ru/2022/03/22/ken-jee-explains-how-to-build-a-career-as-a-data-scientist/#respond Tue, 22 Mar 2022 11:47:59 +0000 https://dataconomy.ru/?p=22751 There’s no doubt that data scientists are in high demand right now. Companies are looking for people who can help them make sense of all the data they’re collecting and use it to make better decisions. Being a data scientist is a great way to start or further your career. It’s a field rising, and […]]]>

There’s no doubt that data scientists are in high demand right now. Companies are looking for people who can help them make sense of all the data they’re collecting and use it to make better decisions.

Being a data scientist is a great way to start or further your career. It’s a field rising, and there are many opportunities for those with the right skills. We talked with Ken Jee, Head of Data Science at Scouts Consulting Group, about how to build a career in data science.

With a goal-oriented approach to problem solving, data science evangelist Ken Jee is admired for his work in the field. He is the Head of Data Science at Scouts Consulting Group, and creates and shares content via his podcast, website, YouTube channel, and 365 DataScience course offering; frequently contributes to Kaggle; and is a Z by HP global ambassador. He recently helped design data science challenges featured in “Unlocked,” an interactive film from Z by HP. The film and companion website present data scientists with the opportunity to participate in a series of problem-solving challenges while showcasing the value of data science to non-technical stakeholders, with a compelling narrative. We spoke with Jee about how he built a successful career as a data scientist.

What is your background and how did you get started in data science?

All my life, I played competitive sports, and I played golf in college. One of the ways that I found I could create a competitive edge was by analyzing my data and looking at the efficiencies that could be created by better understanding my game, and allocating time towards practice more effectively. Over time I became interested in the data of professional athletes, particularly golfers, so I started to analyze their performance to predict the outcome of events. I tried to play golf professionally for a bit, but it turns out I am better at analyzing data than playing the game itself.

What advice do you give young people starting out or wanting to get into the field?

If they’re just starting out learning data science, I recommend that they just choose a path and stick to it. A lot of times people get really wrapped up in whether they’re taking the right course and end up spinning their wheels. Their time would be better spent just learning, whatever path they take. I will also say that the best way to land a job and get opportunities is by creating a portfolio by doing data science. Create or find data, whether it’s on Kaggle or from somewhere else, like the “Unlocked” challenge, show your work to the world, get feedback and use that to improve your skills.

“Unlocked” is a short film that presents viewers with a series of data science challenges, that I along with other Z by HP Data Science Global ambassadors helped to design. There are challenges that involve data visualization using environmental data; natural language processing or text analysis using a lot of synthesized blog posts and internet data; signal processing of audio information; and computer vision to analyze pictures, along with accompanying tutorials and sample data sets. We wanted to highlight a variety of things that we thought were very exciting within the domain.

There’s a lot of fun in each of these challenges. We’re just really excited to be able to showcase it in such a high production value way. I also think that the film itself shows the essence of data science. A lot of people’s eyes glaze over when they hear about big data, algorithms and coding. I jump out of bed in the morning happy to do this work because we see the tangible impact of the change that we’re creating, and in “Unlocked,” you’re able to follow along in an exciting story. You also get to directly see how the data that you’re analyzing is integrated into the solutions that the characters are creating.

How has technology opened doors for you in your career?

I would argue that technology built my entire career, particularly machine learning and AI tech. This space has given me plenty to talk about in the content that I create, but it has also helped to perpetuate my content and my brand. If you think about it, the big social media companies including YouTube all leverage the most powerful machine learning models to put the right content in front of the right people. If I produce content, these algorithms find a home for it. This technology has helped me to build a community and grow by just producing content that I’m passionate about. It is a bit meta that machine learning models perpetuate my machine learning and data science content. This brand growth through technology has also opened the door for opportunities like a partnership with Z by HP as a global data science ambassador. This role gives me access to and the ability to provide feedback on the development of their line of workstations specifically tuned to data science applications–complete with a fully loaded software stack of the tools that my colleagues and I rely on to do the work we do. Working with their hardware, I’ve been able to save time and expand my capabilities to produce even more!

What educational background is best suited for a career in data science?

I think you have to be able to code, and have an understanding of math and programming, but you don’t need a formal background in those areas. The idea that someone needs a master’s degree in computer science, data science or math is completely overblown. You need to learn those skills in some way, but rather than looking at degrees or certificates, I evaluate candidates on their ability to problem solve and think.

One of the beautiful things about data scientists is that they come from almost every discipline. I’ve met data scientists from backgrounds in psychology, chemistry, finance, etc. The core of data science is problem solving, and I think that’s also the goal in every single educational discipline. The difference is that data scientists use significantly more math and programming tools, and then there’s a bit of business knowledge or subject area expertise sprinkled in. I think a unique combination of skills is what makes data science such an integral aspect of businesses these days. At this point, every business is a technology company in some respect, and every company should be collecting large volumes of data, whether they plan to use it or not. There’s so much insight to be found in data, and with it, avenues for monetization. The point is to find new opportunities.

What’s an easy way to describe how data science delivers value to businesses?

At a high level, the most relevant metric for data science in the short term is cost savings. If you’re better able to estimate how many resources you’ll use, you can buy a more accurate number of those resources and eventually save money. For example, if you own a restaurant and need a set amount of perishable goods per day, you don’t want to have excess inventory at the end of the week. Data science can be used to very accurately predict the right quantity to buy to satisfy the need and minimize the waste, and this can be on-going and adjusted for new parameters. Appropriate resourcing is immensely important, because if you have too much, you’ll have spoilage, and too little, you’ll have unhappy customers. It’s a simple example but when your sales are more accurate, even by a small percentage, those savings compound. At the same time, the data science models get better, the logic improves, and all these analytics can be used for the benefit of the business and its profitability.  

Is being a data scientist applicable across industries?

You can have success as a data scientist generalist, where you bounce across different subject area expertise and industries, like finance, biomedical, etc.; you just have to be able to pick up those domains relatively quickly. I also think that if you’re looking to break into data science from another field, the easiest path for you would be to do data science in that field. It all sort of depends on the nature of the problems you would like to solve. There are verticals where subject area expertise is more important, maybe even more so than data skills, like for sports and you need to understand a specific problem. But generally, someone could switch between roles.

Any final notes?

I’m a huge believer of setting goals and accountability. A good goal is measurable, you control the outcome, and set a time constraint. Once you’ve set your goal, write it down or tell people about it. Also, never forget that learning is a forever journey.

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In conversation: the Chaos Computer Club, transparency, and data income plans https://dataconomy.ru/2022/01/13/chaos-computer-club-transparency-data-income-plans/ https://dataconomy.ru/2022/01/13/chaos-computer-club-transparency-data-income-plans/#respond Thu, 13 Jan 2022 10:32:21 +0000 https://dataconomy.ru/?p=22477 Towards the end of 2021, I spoke with Julio Santos, the technical cofounder of Fractal – creators of the Fractal Protocol – on some of the essential topics in data sovereignty, privacy, and security.  As with my organization, polypoly, we have concerns over the free access significant companies have over your data and how to […]]]>

Towards the end of 2021, I spoke with Julio Santos, the technical cofounder of Fractal – creators of the Fractal Protocol – on some of the essential topics in data sovereignty, privacy, and security. 

As with my organization, polypoly, we have concerns over the free access significant companies have over your data and how to keep the web open, practical, and accessible for everyone while regaining control of our information. 

You can read part one of this deep dive in full. We discussed, in detail, Facebook, data sovereignty, and the flaws in regulations such as GDPR. And now, we’ll plunge in again for the second part of this conversation, where topics like data income plans and why it is vitally important to redress the balance and know as much about governments and organizations as they know about us.

Here’s a recap of the final statement from part one of the conversation for context.

Dittmar: 

“When it comes to health data, we are not an expert in it. We are an expert in decentralized data systems. But there are experts out there, who maybe would like to use a decentralized solution, but have no clue how to build these kinds of technology. And so our role is to create the underlying infrastructure, and everybody else can sit on top of that and interact with the user. The idea of the polyPod is that it is extendable. Everybody can build features for the polyPod. If the user wants to have it, they can download that feature and use it or not, depending on whether the user likes that feature or trusts the supplier.”

Santos:

Does this mean data never leaves the polypod?

Dittmar:

For example, if you were managing a fleet of shared cars, you would want to know the schedule of citizens tomorrow, when they will leave their homes for work, and so on. 

Therefore, one way to achieve this is to expose them to sensible data, which they are not likely to do. Another way is to send an untrained model to a federated AI platform. And then, during the night, millions of these networks, or millions of pods, will train that model. So that early in the morning, we’ll train the model. For instance, if you can predict the fleet’s timings, costs, and best routes, you will have a better commute to work.

Data sovereignty and trust

Santos:

Agree. And that also means you’re saving a lot of it off the cloud. And you don’t have substantial IP risks, such as hacking, because everything is done locally.

But how can the user trust these events, algorithms, models, and computation tools sent to the edge and sent to their devices? Is there a vetting process for who was involved in that?

Dittmar:

We will bring this to life at the beginning of 2022. It’s like an app store. And basically, everybody can open such a feature repository. An NGO like the Chaos Computer Club can access such a thing, and they can certify their stored features, so if you trust these kinds of NGOs more than us or more than the government, you can go to this depot then download the elements from there. Also, huge companies like Adidas or Nike can build something like that and have all the features of their products stored here. 

We talked about trust and education earlier. Besides educating people, there’s another ingredient needed to make our data economy understandable for non-tech people. One important aspect is that confidence in the virtual world should work like trust in the analog world.

The trust mechanisms we have in the digital world look completely different. First of all, trust usually is zero or one – if you have a certificate for your HTTPS connection, you trust it or not. And the certificate is typically made by somebody you have never heard of. 

It is always global, and trust for normal human beings is always subjective. For example, I’m using our insurance company for a straightforward reason, because my mom said 30 years ago, go there. And I trust my mom when it comes to money. That’s the way we are building trust – it is emotional. So that means my trust, my personal trust in a company, in a future developer, in somebody who wants to use my data, or in another person, is always subjective. 

If I install our feature, whether you build one now or in the future depends highly on my trust, but also when other organizations or friends, who are trusting you, who have had a fantastic experience with your product.

The ranking of features is not dependent on Google Ads anymore; it is based on your trust and your influence sphere. 

That also means that if a government likes our position on physician informatics, they can be sure they’re acting responsibly for securing the IT systems that store that highly sensitive information. They can publish, with full transparency, explaining that they looked at these features and certify them. 

For example, for features that allow citizens to request data from governments (GDPR is relevant here) when it comes to saying “please send me all the data you store about me,” they can show and state clearly that they trust this feature or this company, and show why. That means that if somebody acts incorrectly, such as selling your data to another company without permission, they can say with absolute clarity and evidence they don’t trust them anymore. 

And these will have an immediate impact on the whole ecosystem because it’s something that happens in real-time. It is always good to understand how we think they’re transposing working mechanisms from the real world to the digital world.

My privacy is your privacy

Santos:

I have a question about how your privacy is connected to other people’s privacy. We’ve started to realize that the concept of personal data is sometimes a little bit blurry. Often data that’s about you is also about someone else. So, for example, if you and I are known to spend time together, and I’m sharing my location, but you’re not, then I am violating your privacy. At Fractal, we’re working on the concept of privacy, preserving data sharing, and one of the ways that we can make that work is by grouping users in different cohorts or different unions based on these privacy preferences to make sure that these externalities aren’t randomly placed on people who aren’t ready to accept them.

If you have any thoughts on this idea, I wanted to know that personal data is sometimes a bit blurry, and it applies to more than you, and if polypoly has taken this into account in any way.

Dittmar:

That’s an old-fashioned problem; we had images in the analog world. So when somebody took a picture with the two of us, it’s precisely the same problem. There are no rules for that in place. Implementing the laws exactly as written is a different story, but you can use them as a guide. It is a good idea to find out how that works in the real world here, too. 

We, as tech people, should not try to implement something better than the real world. First of all, we should try to implement something like the real world because it’s easy to understand. Nevertheless, you’re right. It needs to be as simple as “is it my data, your data, or our data?” And then, there’s a fantastic protocol called the Open Digital Rights Language (ODRL). 

That’s about how to model rights for digital assets. So, it was initially made for digital rights management (DRM). You have an acquisition, and this comes with a policy that includes what you are allowed to do and what is forbidden, and what kind of duties are coming with these purchases.

What you just said about these duties is interesting. If you’re sharing your location and this is close to my location, you are only allowed to do so if you fulfill those duties. 

But at the end of the day, something like this scenario (if it is your location and my location simultaneously), we should find a way to control that. Because the way we want to do it is maybe different than others would like to do it. There cannot be a static solution for something like that. It makes people aware that if they share their location, that means as long as we are in a meeting together, they will share my location. 

So your system, taking care of your private sphere, should be aware that I’m close to you and then send a notification before you can share your location. Is it okay? If I’m saying yes, it’s fine. And if I’m saying no, then both of us will get notified on our phones. 

Santos:

I like your point of view – looking at what has already been deployed in the real world. I think there’s a big difference here, which is scale. Like the fact that I have a picture, an analog picture of you and me, my ability to distribute is quite limited compared to having a digital device with the internet in front of me. 

So I think the additional friction that the analog world brings us is possibly even beneficial for many use cases, and perhaps some stuff will need to be tweaked, reinvented for the digital sphere. But yeah, I agree with your point in general. And again, it takes us back to education and making people aware of what is going on. 

I’ve got a question about user compensation, which I believe polypoly isn’t thinking about right now. Our approach with Fractal Protocol is to compensate the users for their data. So first, we offer blockchain token incentives, just for them to provide data, there’s no sharing in that moment, and then we layer revenue on top of that from an actual buy-side. 

I wanted to understand from your perspective, what are the tradeoffs involved in paying users for data?

Rewards and incentives: it’s not all about the money

Dittmar:

There is a Digital Income Plan. But it will take a while before we bring that to life. We spent a lot of time thinking about this mechanism. And if you pay people for access to the data, you’re creating an incentive for, you know, getting naked in some way. 

People who are as privileged as we are can say – I don’t need these few cents. I will keep my privacy. But what is in it for people who are more in a less privileged position? Now, if we are creating our new system for data economy, we should build it from scratch with suitable incentive mechanisms for all. 

What we would like to do instead of paying people for giving access to their data is to pay people for renting out computing power in the context of the data. At least here in Europe, people often have a lot of computational power because they’re spending some money on Playstations, smartphones, and laptops – around €1 trillion is invested in hardware every three years. However, some reports suggest that these devices only use a fraction (daily) of the possible computing power.

Usually, these many different devices are waiting for us to use them for a few minutes or hours. If you’re combining this computing power when those devices would otherwise be dormant, this is an unbelievable asset that can help make all our vision happen. 

If you want to change the economy, that will cost a lot of money. If you can activate 1% of these unused assets, that’s already a billion. Yeah. From our perspective, incentivizing people to share their computing power, generally in the context of their data, but later on also for other things, is a different incentive than getting paid for giving access to data. And it is more socially balanced.

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In conversation: Facebook, data sovereignty, and why GDPR is flawed https://dataconomy.ru/2021/11/25/conversation-facebook-data-sovereignty-gdpr-flawed/ https://dataconomy.ru/2021/11/25/conversation-facebook-data-sovereignty-gdpr-flawed/#respond Thu, 25 Nov 2021 11:26:56 +0000 https://dataconomy.ru/?p=22381 In recent weeks, the topic of data privacy, data security, data sovereignty, and how social media platforms harvest and use our information has reared its head again. The most recent Facebook whistleblower, who divulged how the platform knows it is responsible for helping to create divisions through its algorithm, which uses our data to deliver […]]]>

In recent weeks, the topic of data privacy, data security, data sovereignty, and how social media platforms harvest and use our information has reared its head again. The most recent Facebook whistleblower, who divulged how the platform knows it is responsible for helping to create divisions through its algorithm, which uses our data to deliver that content, ignores this because it is not good for business.

The latest revelation brings back memories of Brittany Kaiser. She caused a global stir when she explained how Cambridge Analytica leveraged our data to help change hearts and minds during Donald Trump’s presidential campaign.

Data sovereignty became more crucial than ever after her revelations, further explained in the documentary The Great Hack. One organization, polypoly, wants to change the way we use our information and give us back control. And it believes that EU citizens can be the first to gain absolute command over their data. 

At the beginning of 2019, I founded polypoly.eu, intending to restore sovereignty over digital data for everyone and thus support European data capital flow to local markets. Rather than being mere data providers, members of Polypoly cooperatives co-own the same underlying technology: the polyPod.

Julio Santos, the technical cofounder of Fractal – creators of the Fractal Protocol, which it says will enable radical markets for data and help keep the web open and accessible for everyone – spoke with me recently to understand how we might be able to regain control of our information.

Beginning with research

Santos:

Let’s talk a bit about polypoly. When did you start it? And what have you built so far? What can people already do with it?

Dittmar:

So the research started around five or six years ago. We did the research upfront and before we founded the company. Therefore, the main message when we launched was, “we know how to fix it right now because we’ve done the research.” 

Ultimately, this is not only a technical problem. The whole data privacy issue is partly a technical problem, but it has to do with economic incentives. It has to do with our laws, so it’s a multi-dimensional predicament. 

One aspect was, of course, how to build a company that others cannot take over or threaten; A system that is so rock solid nobody can harm it. And then, of course, we also built the prototype – the first version of the polyPod and the first version of the polyPedia. With the polyPod, if you download it today, it lets you look behind the scenes of the data economy. The polyPod that is out there today is a front end for the polyPedia. The polyPedia is a system where we store all information about companies acting within the data economy. 

One of the most critical aspects of that ecosystem is trust, and trust is something you have to earn. And so, the first iteration we made cannot harm you at all because none of your data is involved. We will show you that we know what we’re talking about. And then the second version, which is coming very soon, is then about downloading your data from organizations such as Facebook and showing the context in which the data is stored. So, if you’re in Germany, it means you have a contract with Facebook Ireland, and those laws are in charge. We can show clearly that this is your data, stored on their systems, and what that means.

Data sovereignty and GDPR

Santos:

Interesting. So it’s kind of like mapping your data, what they store, who else can see it, your rights, and what regulations are involved. It’s a complete view of what’s going on.

Dittmar:

Correct. In the EU, GDPR is a right, but it’s not easy to administer. And a law or right you have which you cannot execute is no right at all. And so, we have to make it easier for people to understand what’s going on with their data. 

So why can it be harmful that somebody knows your location data? One of the biggest problems we have in the whole data economy is an entirely abstract threat model for people who have not studied computer science. Nobody can understand what it means when somebody knows my location data or what can be done with pictures. For example, if you’re posting an image, the social media platform can use this photo to find out what kind of trademarks you’re using; a Boss t-shirt or furniture from Ikea. That is then giving these people insight into your estimated earnings and brand preferences. Worse still, all this is very intransparent.

Education and cooperatives

Santos:

I agree, education is vital and is the only way that we get to make people aware of what it is that is going on. Because if they’re going to have an impact and have a voice, they first need to understand this landscape, and it’s deliberately opaque. So it’s not exactly easy to understand without help. I have I’ve seen that polypoly is consists of three linked organizations. So you’ve got the cooperative, the enterprise, and the foundation. Can you give us an overview of why these three organizations exist and what the relationship between them is,

Dittmar:

The foundation is there to build co-ops. The company is incorporated as an SCE, and that means this is a Pan-European cooperative. You can only become a member of a co-op if you are a European citizen. This is for a simple reason. If you have foreigners as members, for example, if you have US citizens as a member of European co-op, it can happen that the co-op will be in front of a court in New York City. So the co-ops are acting as a legal fortress for the local citizens. 

That means we have to build, sooner or later, co-ops in other parts of the world. We are currently discussing with people from Canada, the US, and India to build co-ops there. And that’s the role of the foundation – to create these coops everywhere. It’s a kind of incubator for local co-ops. The critical aspect of keeping co-ops local is simple. One is making sure that in the data economy, organizations will pay taxes locally. When your data generates money, the associated taxes are invested in your community and not somewhere else. And secondly, for you as a local citizen, only your law should be applied. International law is untenable for noncitizens. So we have to make sure that everything will happen locally. That’s the reason we have the foundation and the co-ops in all the different countries.

Santos: 

I was looking at your website, and I believe now I was looking at the cooperative website. It’s very Europe-centric, so I was going to ask why? I guess the answer is that it’s what we’re starting with, right? It’s the first course.

Dittmar:

Yes. It is the first one, and it is made for Europeans. Nevertheless, the data economy market is global. So that means we have to build other co-ops, which will be owned by our citizens only in these countries. That means the core is 100% owned by the users, but the local users take care of their rights and opinions. So, if the European users want to go in that direction, but the Americans want to go in that other direction, that’s fine. We are not so arrogant to think what is right in Europe is suitable for every other country. And so that all the local co-ops have are the opportunity to adapt the system to the local culture and law, but the interfaces are still the same. 

A company that wants to use this decentralized data network will find a global network of interfaces or ports that use precisely the same interface but always use a local adaptation. There is no data without economies, so the enterprise is serving the economy. We’re building tools to find an easy way from a centralized data economy to decentralized data. The enterprise is financed by the economy, and the users fund the co-op. So if you imagine we have nowadays, I would guess, some 100 million Facebook users in Europe. If just 1% of these Europeans would join the European co-op and buy one share, that would be powerful; owned by the users, financed by the users, founded by the users, and funded by the users.

Santos:

Understood. You talk a lot about data unions, and that’s what a co-op is in this context, right? You’re already thinking of more than one co-op, a European co-op, and then maybe you have an American co-op. Do you believe that there is room for multiple European cooperatives in which they compete for user attention by saying, “this is how we handle your data; we do things a little bit differently,” and then you aggregate people based on these preferences?

Dittmar:

Competition is an essential part of our economic system. So yes, there should be, and there is competition. I guess the only thing that is very important here is that interoperability is always in place. There is an excellent organization called MyData Global, which builds standards for handling personal data. And there are already a lot of companies that are part of that organization, and all of them have signed an agreement that they will make sure that it is straightforward for the user to transfer data from one potential solution to the next one. That’s a crucial aspect because you never know which answer is the right one. There must be lots of different players trying lots of other ideas, and then the user will decide what the right one for them is.

Portability and interoperability

Santos:

With a commitment to interoperability, you will allow those things to happen. You’re saying, as a company, we believe that we have the solution for this, but we also may not. And perhaps the answer is to ask somebody else, so this interoperability, this portability of data, becomes essential.

Dittmar:

What we would like to be in the future is something like the public water supply for data. So, we are taking care of the pipes in the earth, we are taking care of everything that’s in those pipes is clear water, and then others can use our infrastructure to create a water supply business. For example, when it comes to health data, we are not an expert in it. We are an expert in decentralized data systems. But there are experts out there, who maybe would like to use a decentralized solution, but have no clue how to build these kinds of technology. And so our role is to create the underlying infrastructure, and everybody else can sit on top of that and interact with the user. The idea of the polyPod is that it is extendable. Everybody can build features for the polyPod. If the user wants to have it, they can download that feature and use it or not, depending on whether the user likes that feature or trusts the supplier.

In the next part of the interview, in addition to going deeper on data sovereignty, I speak with Santos about Tim Berners-Lee’s Solid project, data income plans, and why it is vitally important to redress the balance of knowledge to know as much about Mark Zuckerberg as possible his organization knows about us.

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Finance industry moving towards ‘smart products’ integrated into our daily life: An interview with Pavel Perfilov https://dataconomy.ru/2021/11/10/finance-industry-moving-towards-smart-products-integrated-into-our-daily-life-an-interview-with-pavel-perfilov/ Wed, 10 Nov 2021 07:35:18 +0000 https://dataconomy.ru/?p=54209 Pavel Perfilov is a professional working at the intersection of modern technology and finance, with extensive experience in low latency trading, market data management, and pre- and post-trade risk management. Pavel is particularly knowledgeable about the complexities of brokerage and exchange infrastructure due to his extensive experience with various exchanges, which includes everything from paperwork […]]]>

Pavel Perfilov is a professional working at the intersection of modern technology and finance, with extensive experience in low latency trading, market data management, and pre- and post-trade risk management. Pavel is particularly knowledgeable about the complexities of brokerage and exchange infrastructure due to his extensive experience with various exchanges, which includes everything from paperwork to patching and physically installing servers. In this talk, he discusses technology trends in trading and the future of finance in the aftermath of a new wave of technological revolution that has brought AI into our daily lives.   

As an ex-IT director of an electronic brokerage company, can you tell us a bit about the tools and methodologies for performance testing and monitoring of trading systems that you find the most effective at the moment?

Electronic trading services (DMA) are a very demanding sector; clients require ultra-fast speed of processing orders, higher leverage, better location, and attractive fees. All of these add complexity to the process of building robust and reliable trading systems. You can’t simply add extra checks and additional logging because it would add delays, queues, and non-deterministic behavior, which clients do not usually like.

Pavel Perfilov
Pavel Perfilov

One of the solutions for building high-performance systems for monitoring is to build post-trade monitoring systems and systems that capture traffic and process all messages that are being transmitted over the network. This allows it to minimize its impact on the trading system itself and allows it to monitor every bit of information transmitted. This approach doesn’t work very well in risk systems, as there are some extra regulatory requirements that are hard to solve in a way that won’t impact speed. One of the examples is the requirement to record client transactions and the state of his account before and after the transaction. These functional parts, which are making fast pre- and post-trade checks and record keeping, are typically treated as ‘secret sauce’ in organizations. With regards to methodologies and tooling, modern CI/CD pipelines and automated testing, which include performance testing with very high loads, work very well. Sometimes replaying traffic is considered a good tool for finding bugs and issues before production rollout. 

How are evolving technologies like artificial intelligence and machine learning transforming the trading sector?

The brokerage and finance industry is very regulated, so sell-side is simply not allowed to build AI and non-linear neural networks in clients’ order processing. At the same time, in non-regulated areas where the client takes responsibility for their decisions, AI has become a game changer. For example, some brokers give extra tooling to a client to compose a portfolio in the most efficient manner. AI allows them to extract sentiments from the news feeds and label some market moves by the events that happened at the time so that the clients can better understand and learn market behavior patterns. I think we’re just at the beginning of a new era of AI-driven financial products. 

What challenges and opportunities do you see in the integration of AI?

Challenges are not visible yet because the banking sector is about trust and reliability. Unicorn startups are not changing the game (yet) because of a lack of trust, so big clients tend to stay with companies that have a proven track record and a long history. Modern companies attract more young clients because they offer gamified products and services. If unicorn companies managed to be successful in the next 10 years, the structure of the brokerage industry would probably change. 

What’s your take on the future of decentralized finance?

The ideas and concepts behind DeFi are very good. I see that performance and capacity, first of all, have significantly improved, so tens of millions of transactions are not blocked anymore. This made me think that the industry is growing and that it’s great to have more venues where clients can find liquidity. I don’t think it’s a threat to the real financial sector. 

What future applications do you foresee for blockchain technology in financial markets?

First of all, I think most people have realized that this technology is real and that it works. Crypto exchanges and De-Fi are operating, volumes traded are growing, and regulation is also changing, so we are slowly transforming the paradigm of classical financial services. The future is unknown, but as technology proves more and more reliable, low-cost, and efficient in operations, I think it’s inevitable that it will get more credit. I think trust here is the main enabler; the more trust a certain technology has, the more chances it would have to get approved by regulators. If regulators approve it, there will be a rapid growth of the services, but I hope it will be controlled and the quality of the new products won’t impact reliability and trustworthiness. 

What strategies do you employ to continuously innovate and stay ahead in the competitive fintech landscape?

As of now, it’s all about cost and speed. The strategy is to minimize transactional and operational costs. Fee-free brokerage services are sort of a new reality that currently drives the market. This trend pushes brokers to find new areas for service monetization. The strategy is simple here: always try something new, be it AI, ML, blockchain, or something else. If you find a way to build an innovation pipeline in an efficient manner that doesn’t have a high cost or time to market, you’ll be able to collect feedback and get some sense of the potential value of the innovations, which could lead to higher returns. 

How do you envision the finance industry evolving over the next five years? What current practices will become obsolete, and what new trends do you anticipate emerging?

Five years ago, we were not able to imagine 0-fee brokers, AI-driven automated advisory services, and such an amount of ML pipelines in every department and every service. The cost structure has changed significantly, the velocity of the market has changed, and the trend here is faster time-to-market for new smart finance products. Speed and fees are at their physical limits, so it’s not attractive anymore. So the industry would go towards some ‘smart products’ that are smoothly integrated into our daily lives. 

Featured image: Unsplash

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DataRobot CEO calls for ‘a new era of democratization of AI’ https://dataconomy.ru/2021/03/26/datarobot-ceo-calls-new-era-democratization-ai/ https://dataconomy.ru/2021/03/26/datarobot-ceo-calls-new-era-democratization-ai/#respond Fri, 26 Mar 2021 09:05:55 +0000 https://dataconomy.ru/?p=21860 This article was originally published at VentureBeat and has been reproduced with permission. Dan Wright just became CEO of DataRobot, a company valued at more than $2.7 billion that is promising to automate the building, deployment, and management of AI models in a way that makes AI accessible to every organization. Following the release of version […]]]>

This article was originally published at VentureBeat and has been reproduced with permission.

Dan Wright just became CEO of DataRobot, a company valued at more than $2.7 billion that is promising to automate the building, deployment, and management of AI models in a way that makes AI accessible to every organization.

Following the release of version 7.0 of the DataRobot platform, Wright told VentureBeat that the industry requires a new era of democratization of AI that eliminates dependencies on data science teams. He explained that manual machine learning operations (MLOps) processes are simply not able to keep pace with changing business conditions.

This interview has been edited for brevity and clarity.

VentureBeat: Now that you’re the CEO, what is the primary mission?

Dan Wright: What I’m trying to drive is the democratization of AI. In the past, AI has been some kind of buzzword. It’s been mainly experimental. You had data scientists who are working on different data science projects. But a lot of the models that they were working on never actually made it in production or added any value. What we’re doing now is allowing our platform to be used by people who are not data scientists, as well as data scientists, to create business insights and make better decisions on an ongoing basis. That kind of opportunity is limitless right now so we’re really focused on doing that.

VentureBeat: DataRobot just released a version 7.0 update to the platform. What are the highlights?

Dan Wright: We have enhancements to every one of our products within the platform. We can monitor and manage all of your models, regardless of where they live. They can be completely outside of DataRobot and still provide alerts if there’s any sort of accuracy or model drift. Another thing is anomaly detection. One thing that’s happened in the past is a model would get thrown off when there was some sort of anomalous piece of data. Now we’re actually able to tell you this is an anomaly and ask if it should be disregarded. That way you don’t throw off your models.

The other thing that we’ve done is we’ve created what we call our app builder, which makes it much easier for us to build applications on top of the platform for different use cases. We’re going to create an ecosystem of these AI-powered applications. Then there were some additional features around bias and fairness detection. Our philosophy is that we need to alert you if there’s any sort of bias or fairness issues with respect to your model, and then allow you to configure the model as you deem fit based on your own ethics and your own values.

VentureBeat: Most AI models require a lot of manual effort to build and maintain. Are we on the cusp of moving beyond that? Are we looking at the industrialization of AI?

Wright: I think that’s spot on. We have seen a lot of what I refer to as experimental AI, where people are using disjointed point solutions and open source tools. It’s been a little bit of a black box. Those days are over. Now it’s about the industrialization of AI using an end-to-end system all the way from data prep to monitoring and managing all of your models in production. It’s decision intelligence around specific use cases. I think we’re really going to see AI take off and become real, even for people who may have failed in the past.

VentureBeat: How much data science expertise will ultimately be required? Do organizations need a data scientist?

Wright: The whole idea with DataRobot is to automate a lot of the things that data scientists had previously done manually. You don’t need to be a very highly skilled data scientist to create value with AI to drive insights. A business analyst, engineers, and executives can all get models into production and then monitor and manage all those models. It’s really important that you build data science best practices into the platform, and that everything is fully explainable with trust and governance. It’s democratizing AI, but with guardrails to make sure that people don’t get in trouble.

VentureBeat: What impact did the economic downturn brought on by the COVID-19 pandemic have on AI adoption?

Wright: I think there were a couple of ways. One is because there’s been so much volatility a human can’t take in all of this data when it’s changing that rapidly. You need AI to actually understand what’s happening in the future. If you’re a big retailer trying to determine how many jars of peanut butter are needed in a particular store, that’s incredibly complex when you layer in the pandemic and all of a sudden you have stores opening and then closing.

The other thing that we really saw with the pandemic was that there were already AI models being used in production. People woke up and realized they had no idea what was going on with those models. They had no visibility into them. All they knew is that they were very likely to be inaccurate because all the data had completely changed. We’ve seen really broad adoption of our machine learning operations (MLOps), which is the part of our platform that allows you to monitor and manage all of your different models, including a model that’s created manually with Python or any sort of open source tool. If there is any kind of drift, you can actually run challenger models in the background. It’s no longer acceptable to just say I’m going to get a model in production and come back in six months and see if it’s still accurate. You need to be managing it in real time and updating it as the data is changing.

VentureBeat; Will MLOps eventually just become an element of existing IT operations?

Wright: What we’re really starting to see is an end-to-end system. I don’t think it’s going to be so much about just MLOps in the future, I think it’s going to be about monitoring the entire lifecycle of a model and continually updating it as data is changing. What makes what we do really powerful is we don’t just have MLOps. We have MLOps for all of your models, but most importantly we combine that with automated machine learning. We’re constantly running challenger models in the background and updating the models as the data is changing to do continuous learning. That’s what you’re going to see in the future. It’s not going to be about working for six months to get a model into production.

VentureBeat: It seems like MLOps borrows concepts that were originally pioneered by DevOps practitioners. What’s going to be the relationship?

Wright: I think it’s similar but more powerful. The platform automates many of the things that were previously done manually.

VentureBeat: Most AI models are dependent on the quality of the data, and yet the quality of the data in the enterprise is often suspect. Is there some way to address that fundamental problem?

Wright: You need to be able to automate the process to tag and clean your data to apply machine learning in the first place. We acquired Paxata in December of 2019, which was a company focused on data preparation. We’ve now integrated that into our platform. The other thing that’s really important is being able to take the data in from wherever it resides. One thing that we’ve really focused on is being able to plug into any data source, whether it’s saved locally or in any cloud. We have a great partnership with Snowflake, which made its first strategic investment ever in DataRobot. That is a major pain point for a lot of companies. A lot of companies previously tried AI, but they never got past the step of Data Prep. We’re really solving that by automating a lot of the process related to Data Prep.

VentureBeat: Most AI training today occurs in the cloud. Will training of AI models soon be moving all the way out to edge computing platforms?

Wright: We’re already seeing that, and it’s opening up new possibilities. The other thing that we’re seeing is AI is being used now on different types of data sources that were never previously possible. We have the ability now to take not just text data, but also image data, geospatial data, and many other types of data. You can combine them all into one model and generate predictions and decision intelligence. Humans have all of these different senses. Now AI is going to have all of those different senses, and the edge is definitely a direction that this technology is moving.

VentureBeat: Will the algorithms ever get smart enough to tell us not the answer to a question but also the right questions to ask?

Wright: How we look at it is you want the AI to get as smart as possible. That requires that you have as much data as possible and that you’re continually improving your algorithms. But it’s not going to be about just AI or machine intelligence. It’s this combination of human intelligence with machine intelligence. That’s what’s going to create amazing opportunities in every industry in the future. There’s always going to be a human in the loop. I don’t think AI can be too smart so long as you’ve got that human in the loop.

VentureBeat: Is it possible one day AI models created for conflicting purposes ultimately just nullify each other?

Wright: I’ll answer that question in a couple of ways. We are seeing kind of a rush to adopt this technology. Many people have referred to this as a fourth industrial revolution, but there’s always going to be a first mover advantage. With AI, that is even greater because of the feedback loop you get with algorithms that are constantly getting better and better. The leaders when it comes to AI are going to be the big winners over the next decade, and the losers really may never catch up. There is a very large sense of urgency to adopt the technology. But it’s unlikely that people will adopt it exactly at the same rate, but let’s just say for argument’s sake they do. You’ll end up getting a much more efficient market.

VentureBeat: What’s your best AI advice to organizations right now?

Wright: Too few companies are actually asking what should be an obvious question. What value is actually being delivered from my AI? A lot of people have big budgets and have been spending tens of millions of dollars for years with some of the legacy vendors in the space. They’re not getting any value, and they’re not even actually looking to see if they’re getting any value. That’s no longer acceptable. You need to know in real time what is the value that you’re getting from all of the models in production, and where are opportunities to drive more value? This is a race, and whoever is able to get value fastest is likely going to win in the market. The other thing that has flown a little under the radar is this idea of trust. It’s not enough to just use open source tools or a bunch of disjointed solutions to try to experiment with AI. You actually need a system that has trust built into the very foundation so it’s not a black box.

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Where Data Scientist Salaries are Headed in 2021 https://dataconomy.ru/2021/01/12/where-data-scientist-salaries-headed-in-2021/ https://dataconomy.ru/2021/01/12/where-data-scientist-salaries-headed-in-2021/#respond Tue, 12 Jan 2021 14:49:18 +0000 https://dataconomy.ru/?p=21633 For many years now, becoming a data scientist has been the goal for many. Billed as the hottest role of the 21st century, data scientists are among the highest paid in the IT industry and one of the most scarce right now. In fact, according to Glassdoor, the average salary for a data scientist is […]]]>

For many years now, becoming a data scientist has been the goal for many. Billed as the hottest role of the 21st century, data scientists are among the highest paid in the IT industry and one of the most scarce right now.

In fact, according to Glassdoor, the average salary for a data scientist is €93,400.

And data science is part of our daily lives. It is in the online purchases we make, our social media feeds, the music we listen to, and the movie recommendations we’re shown.

As we predicted at the start of the year, companies that do not invest in data science and AI will not compete.

So what does a data scientist do?

The role requires you to collect, structure, store, process, and analyze data so that individuals and organizations can make decisions based on the data’s insights.

It is also a role that fits with the current reality of work. The nature of the job allows you to work at flexible times and remotely. It suits self-employed individuals as much as it does those that are employed.

And it is an exciting space too. The field of data science has grown exponentially in a short period as companies have recognized the importance of getting large amounts of data from websites, devices, and social networks to use them for business purposes. Once the data is available, data scientists use their analytical skills to evaluate data and extract valuable information that companies can use to improve their products, services, and future innovations.

In the future, data is likely to emerge as a game-changer for the world economy, so pursuing a career in data science would be very beneficial for a computer enthusiast, not only because it pays well but also because of its potential impact.

Data science is, of course, not for everyone. Data scientists are often individuals with experience in analytical thinking and analyzing data, matched with complex problem-solving skills and a curiosity to explore a wide range of emerging issues.

They are considered the best in IT and business, making them highly skilled individuals whose job roles straddle the world of computing, statistics, and trend analysis. There is a fast-growing demand for data identification and research in various technological fields such as AI, machine learning, natural language processing, and computer vision that helps ensure the salary and career path of a data scientist is one of the world’s best.

Data Science Job Roles

Data Scientist

The work of a data scientist is as exciting as it is rewarding. Using machine learning, they process raw data and analyze it using various algorithms such as regression, clustering, classification, etc. You can identify insights that are critical to predicting and solving complex business problems.

Salary Range for Data Scientists

As mentioned, according to Glassdoor, the average salary for a data scientist is €93,400 per year. The average salary for a young professional can be as high as €78,220 per year. However, data scientists with 1 to 4 years of experience can earn around €106,009 per year, while the average salary for those with more experience can increase to an average of €135,856 ( per year.

Data Analyst

As the name suggests, a data analyst’s job is to collect, process, and perform statistical data analysis that enables business users to get meaningful insights. For this process, systems created with programming languages ​​such as Python, R, or SAS are utilized. Companies ranging from IT, healthcare, automotive, finance, and insurance employ Data Analysts to efficiently run their businesses.

Data Analyst Salary Range

According to Glassdoor, a junior data analyst earns around €57,636 per year, and experienced senior data analysts can expect to be paid approximately €88,101 per year.

Data Engineers

A data engineer is responsible for building a specific software infrastructure so that data scientists can work. You must have a solid understanding of technologies like Hadoop and big data tools like MapReduce, Hive, and SQL. Half of the job of a data engineer is data wrangling, and it is beneficial if they have a background in software development.

Data Engineer Salary Range

According to Glassdoor, the average salary for a data engineer in the United States is €84,695. Renowned companies such as Amazon, Airbnb, Spotify, Netflix, and IBM value and pay high salaries to data engineers. Entry-level and mid-range data engineers earn an average salary of between €90,571 and €113,436 per year. However, with experience, a data engineer can earn up to €127,623 a year.

Data Scientist salaries per position

The number of job opportunities and the salary level for data scientists is the highest in Switzerland in 2020, followed by the Netherlands, Germany, and the United Kingdom.

The United States is generating the largest number of startup jobs globally, followed by Bangalore in India: the salary of a data scientist in Silicon Valley or Bangalore will likely be higher than in other countries.

Below are the average annual salaries of a data scientist per country:

Switzerland – €95,079
The Netherlands – €56,714
Germany – €52,715
The United Kingdom – €49,222
Spain – €24,742
Italy – €31,111

Experience Data Scientist Salary

Starting salaries for data scientists are very favorable, and there is incremental salary growth with experience.

The salaries of a data scientist depend on skill and years of experience:

A mid-level data scientist’s median salary with about 1-4 years experience is €106,009 per year. If the data scientist is in a managerial position, the average salary increases to €152,324 per year. The median salary for an experienced manager is much higher; about $250,000 per year.

Data Scientist Salary by Company

Some of the highest paying companies in data science are tech giants like Facebook, Amazon, Apple, and service companies like McGuireWoods, Netflix, and Airbnb.

Below is a list of the top companies with the highest salaries:

McGuireWoods – €135,950
Amazon – €135,127
Airbnb – €127,523
Netflix – €121,544
Apple – €118,969
Twitter – €118,846
Walmart – €118,729
Facebook – €117,898
eBay – €117,745

Data Scientist Salary by Skills

There are a few core competencies that will help you shine in your career as a data scientist, and if you want to get an edge over your peers, you should consider honing these skills.

Python is the most crucial and sought-after skill data scientists should be familiar with, followed by R. The average salary in the US for Python programmers is €99,105 per year. If you are well versed in both Data Science and big data, rather than just one of those skills, your salary is likely to increase by at least 25 percent. Machine learning engineers, on average, earn around €92,098 per year. However, machine learning, coupled with Python knowledge, means you can earn up to €120,282 per year.

A Data Scientist with knowledge of artificial intelligence can earn an annual salary of between €82,337 and €123,506. Additional skills in programming and innovative technologies have always been an added value that can improve your employability.

So, what are you waiting for?

Data science is, without any doubt, an area that is going to revolutionize the world in the years to come, and you can have a slice of this very lucrative pie with the right background, mindset, diplomas, skills, experience, and training.

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Ashish Gupta on the cloud’s role in shaping healthcare’s future https://dataconomy.ru/2021/01/04/ashish-gupta-on-the-clouds-role-in-shaping-healthcares-future/ Mon, 04 Jan 2021 10:58:28 +0000 https://dataconomy.ru/?p=59483 Imagine a world where your doctor has immediate access to your entire medical history, no matter where you are. For many, this future is already a reality thanks to cloud computing in healthcare.  But what’s holding most organizations back from making this leap? In this interview, we talk with Ashish Gupta, an expert in cloud […]]]>

Imagine a world where your doctor has immediate access to your entire medical history, no matter where you are. For many, this future is already a reality thanks to cloud computing in healthcare. 

But what’s holding most organizations back from making this leap?

Ashish GuptaIn this interview, we talk with Ashish Gupta, an expert in cloud computing and healthcare technology. He shares his insights on how cloud adoption is transforming healthcare, the challenges organizations face, and the benefits it brings to patient care.

What is the current state of cloud adoption in the U.S. healthcare sector?

Cloud computing is slowly gaining popularity in the U.S. healthcare sector. Many organizations are exploring cloud solutions but face several challenges. A major issue is integrating critical workloads while ensuring everything remains secure and compliant.

Currently, healthcare organizations spend about $70 billion a year just to meet various regulations. Many still rely on older systems, making full cloud adoption difficult. While the cloud has the potential to transform healthcare, there is still a long way to go.

What challenges do healthcare organizations face with cloud adoption?

Healthcare organizations encounter several challenges when adopting cloud technology, with compliance and data security being the most significant. The healthcare industry has strict regulations that organizations must follow, such as the HIPAA (Health Insurance Portability and Accountability Act), complicating the transition to cloud systems.

Additionally, with cyber threats becoming more prevalent, protecting sensitive patient data is crucial. About 70% of organizations cite compliance issues as a major obstacle to their cloud strategies.

What are the key drivers pushing healthcare organizations toward cloud solutions?

Initially, many healthcare organizations considered cloud solutions primarily for financial reasons, aiming to save money by reducing on-premises infrastructure costs. However, the focus has shifted to other advantages, such as agility and improved data integration.

By moving to the cloud, organizations can spend less time managing data centers and more time focusing on patient care. The COVID-19 pandemic accelerated this shift, highlighting the need for flexible solutions that can adapt to changing healthcare demands.

How is cloud computing transforming data management in healthcare?

Cloud computing is fundamentally changing how healthcare organizations manage and analyze data, encouraging a new approach to data handling. Nearly 70% of healthcare organizations want to utilize more cloud technology, yet many hesitate to move critical workloads to the cloud. Organizations need clear migration plans that address security and compliance concerns.

The cloud centralizes data storage and allows for real-time access, helping healthcare providers make better decisions and improve patient care.

In what ways are AI and cloud computing intersecting in healthcare?

The combination of cloud computing and artificial intelligence (AI) creates exciting opportunities for healthcare organizations. The cloud provides the necessary computing power for advanced AI tools, which can assist with predictive analytics and diagnostics.

By utilizing cloud-based AI solutions, healthcare providers can gain valuable insights from large datasets, leading to personalized and effective patient care. This partnership not only enhances efficiency but also helps reduce costs and improve health outcomes.

What benefits does cloud technology bring to patient care?

Cloud technology offers numerous advantages for patient care, making healthcare services more accessible and coordinated. For example, cloud-based Electronic Health Records (EHRs) enable providers to quickly access patient information, thereby improving the quality of care.

Telehealth services also benefit from cloud technology, allowing healthcare providers to conduct remote consultations. By making data more accessible, healthcare organizations can deliver better treatment and adopt a more patient-centered care model.

How does cloud adoption enhance operational efficiency in healthcare?

Cloud platforms streamline clinical workflows and improve how medical devices and systems work together, providing a unified view of patient data. This integration leads to faster diagnoses and more effective treatment plans. Additionally, cloud-connected devices enable remote patient monitoring, which is essential for managing chronic conditions.

By adopting cloud solutions, healthcare organizations can optimize their operations and concentrate on delivering high-quality patient care.

Can you provide examples of successful cloud integration in healthcare?

Certainly! Many healthcare organizations have successfully adopted cloud-based EHR systems, enhancing data accessibility and facilitating coordinated care. Remote patient monitoring is another effective example, allowing healthcare providers to detect health issues early.

The rise of cloud-based telemedicine platforms during the pandemic has also been remarkable, enabling virtual consultations and integrating patient data from various sources. This approach leads to better continuity of care and higher patient satisfaction.

What role does scalability play in cloud adoption for healthcare?

Scalability is one of the most significant benefits of cloud adoption. Healthcare organizations can easily increase or decrease their resources based on demand. For instance, they can quickly ramp up capacity during flu season or a pandemic. This flexibility allows providers to respond better to patient needs.

Scalable cloud solutions also help organizations manage costs more effectively by only paying for the resources they use.

What advice would you give to healthcare organizations hesitant to adopt cloud technologies?

For organizations hesitant to adopt cloud technologies, starting with a clear plan is essential. It can be helpful to begin with less critical workloads to build confidence and experience.

Conducting a thorough risk assessment and involving key personnel in the decision-making process are also crucial steps. Partnering with experts who understand the unique challenges of healthcare can facilitate a smoother transition.

By taking a cautious approach, organizations can address concerns while enjoying the benefits of cloud technologies.

How can healthcare providers ensure compliance with regulations when adopting cloud solutions?

To ensure compliance, organizations should integrate strong data governance and security measures from the start. A solid cloud strategy must prioritize these aspects.

Working with cloud providers that specialize in healthcare can offer guidance for navigating compliance challenges. Additionally, ongoing training for staff on compliance protocols is vital to maintaining a secure cloud environment.

What is your vision for the future of cloud computing in healthcare?

I envision a bright future for cloud computing in healthcare. I believe cloud technologies will enhance patient care, improve data sharing, and support innovations like AI and machine learning.

As the industry evolves, hybrid and multi-cloud strategies will likely become more popular, offering additional flexibility. Overall, I think cloud computing will play a crucial role in personalized medicine, better health management, and fostering collaboration among healthcare providers.

How are healthcare organizations using cloud technology to support telehealth services?

Cloud technology makes telehealth possible by allowing providers to deliver care remotely. This is especially important for patients in rural or underserved areas, ensuring they have access to quality healthcare through cloud-based platforms.

With telehealth on the rise, organizations can use cloud solutions for virtual consultations, remote monitoring, and seamless communication between patients and providers. This approach ultimately enhances the patient experience.

What security measures should be considered when transitioning to the cloud in healthcare?

When transitioning to the cloud, organizations should prioritize security measures such as encryption, access controls, and constant monitoring. Many cloud solutions offer advanced security features that can enhance data protection, often surpassing traditional on-premises data centers.

Additionally, healthcare organizations should implement a comprehensive security framework that includes regular audits and incident response plans to address potential data breaches and cyber threats.

How can cloud technology improve data sharing among healthcare providers?

Cloud platforms enable easy data sharing across different healthcare systems, which is crucial for coordinated care. Improved interoperability allows providers to collaborate effectively, leading to better patient outcomes.

By breaking down data silos, cloud technology facilitates access to important information in real-time, ensuring that all providers involved in a patient’s care have the necessary data to make informed decisions. And I believe that will be the game-changer that alters the healthcare landscape of tomorrow.

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This NY based AI Startup Wants Amy & Andrew to Take Care of Meeting Schedules. Would You Sign Up? https://dataconomy.ru/2019/11/14/this-ny-based-ai-startup-wants-amy-andrew-to-take-care-of-meeting-schedules-would-you-sign-up/ https://dataconomy.ru/2019/11/14/this-ny-based-ai-startup-wants-amy-andrew-to-take-care-of-meeting-schedules-would-you-sign-up/#respond Thu, 14 Nov 2019 10:41:51 +0000 https://dataconomy.ru/?p=20986 Here is a look at an AI startup that raised  $44.3 million in venture capital funding and built a product that has a vision to not only scheduling a “time” for meetings but also take care of every little detail that comes along. Find out how intelligent these  AI assistants could be for you.   […]]]>

Here is a look at an AI startup that raised  $44.3 million in venture capital funding and built a product that has a vision to not only scheduling a “time” for meetings but also take care of every little detail that comes along. Find out how intelligent these  AI assistants could be for you.  

Emails will be dead. We have heard this statement in the past and many tech startups are trying to get closer to it with the help of AI. Be it someone as huge as Slack, or other newbies in the tech world that have the vision to either integrate with Slack or get funded by it eventually. It might be your end goal as an AI startup to replace emails, but studies say that there are over 3.930 billion email users, predicted to reach 4.371 billion by 2023 which is a growth of 3% yearly. 

This means that emails are far from being dead, but the way we communicate through emails is going to change. And Artificial Intelligence has a major role to play in this. 

One of the entrepreneurs who wants to transform the way emails reach you is Dennis R Mortensen, founder of x.ai. He is not only obsessed with the future of work and intelligent agents intertwined with AI, but his vision for the long term is also to ‘kill the inbox’.  When I asked him to elaborate on what he means by this, he tells me how emails and inboxes are completely different.

“I love email. It’s one of the last free and fully democratized communications platforms out there, but the inbox (which is different) is many times a bucket of chores and anything we can do to remove those chores – the better it is. We do the meeting scheduling email back and forth, so you do not have to.”

Dennis is the man behind these two:  Amy and Andrew – the artificially intelligent assistants who schedule meetings for you and there is a high probability that someone receiving emails would never know they are not humans. It has happened to me!  

Why Amy+Andrew as AI assistants?

Rakesh Chada and Marcos Jimenez, data scientists at x.ai. explain how this works,  “We’ve built a virtual assistant (it goes by the name of Amy or Andrew) who can be cc’d into your typical request to meet with people over email. Amy will “understand” the handover and just take it from there with your guests, following up with them to nail the time and location details for the meeting. Just like a human assistant would.” Under the hood this means that Amy must automatically extract meeting-related pieces of information from your email and mashing that up with your calendar and overall preferences, proceed to get your guests to agree to a time that works for you and them, plus gather whatever other details are needed for the meeting (phone conference number, meeting room, address, google hangout link, etc …). That’s the premise of the product. 

To me, this seems like a dream which might be achievable too, but with it comes quite a few bottlenecks, which Dennis agrees to as well but is determined that it is a matter of time that the product will become scalable. Keep reading. 

This NY based AI Startup Wants Amy & Andrew to Take Care of Meeting Schedules. Would You Sign Up?

Dennis never had the ambition of creating an AI company though. All he wanted to do was solve a pain that many didn’t know how to get rid of and that gave birth to his meeting scheduling software company. “I think as an entrepreneur one should never really fall in love with the technology. The more important thing to obsess over is the pain that one’s customer might be in and how you could solve it.”

He knows customers are not willing to pay 40,000 euros a year to have an assistant sit in their front office and fiddle with their calendars. But he also knows customers feel this pain and his massive co-ordination network via the assistants could be the onset of what cloud storage was at a decade ago. 

It is a no-brainer that an AI assistant scheduling meetings could save time across geographies. Imagine a scenario sitting around at 10:00 pm somewhere in the United States trying to reschedule a meeting with someone in Europe. Why wait for someone to reply in nine hours? “This should have been an instant reschedule turned into an 18-hour moment in between,” says Mortensen. It is not just individuals but also companies that have seen value in it. 

Kevin Groat, Sr. Global Technical Enablement Manager at VMWare says, “The recent enhancements (my favorite is being able to instantly schedule with coworkers) are elevating Amy and Andrew to true Enterprise-class virtual citizens, who haven’t forgotten their SMB roots. This, along with the ease of incremental adoption, automation and price point make x.ai an easy choice to streamline inefficient, legacy scheduling practices, “

Founded in 2014 by Dennis and two others Alex Poon and Marcos Jimenez Belenguer, the AI meeting scheduler has to date raised $44.3 million in venture capital.  With the conviction that any company would be interested in bundling up his software with the other must-haves that employees receive from enterprises, Mortensen is aiming at x.ai’s market penetration to rapidly pick speed. 

Pricing decisions and other challenges with AI

Changing the behavior of consumers is the biggest challenge for x.ai. You need to be convinced that you don’t need employees to waste their time on meetings but rather have a tool with just $8 per month for those employees. “There needs to be a slight change of behavior, but I think it’s happening as we speak, so time will solve that. I just want to make sure that it happens fast enough,” says Dennis.

The pricing of the software went from $8 as the lowest offer early this year to now being free. When I spoke to Dennis previously this year, he mentioned that after continuous testing for 14 to 15 months, he had zeroed on $8 pricing as a fair value where he believed the New York-based company had entered the territory where everybody sees x.ai as just another software that every organization uses similar to G-Suite and not as an add-on service that may not find many takers.  However, the company also launched a free version on September 9 this year and Dennis says this has led to the fastest user growth ever. “By lowering costs per user and increasing the utility of our system via network effects, we are able to offer x.ai for free,” he says. 

The second challenge is mistakes that Amy or Andrew might make while fixing meetings for different users.

“No prediction in the world, whatever you want to predict will forever be a 100% correct all the time. No meeting scheduling agent, no movie recommendations on Netflix —  there’ll always be some error.”

Sometimes it doesn’t matter much, and sometimes it’s a complete catastrophe. “Our accuracy is above 95%. Sometimes it is not enough so we are are still working on it with the design approach. We can have our customers kind of help us to verify little details.  Like once you ask Amy to do something, she’ll read it back to you.”

Ammom Brown, the Growth head of x.ai says that the company had a huge amount of feedback on how often and how many times Amy and Andrew should reach out to an unresponsive guest. In some cases it would seem to be a bit overzealous, in other cases, they would be too timid. “Years of tweaking the logic got us no closer to a happy compromise, so we made some big changes recently to help you manage to follow up emails on a case by case basis, ” and this continues. 

To outpace rivals, x.ai is working on two aspects. High accuracy on the host’s end as in the customer where anything which the user asks Amy to do, she understands accurately every time. And second on the guest end, where anything that the guest wants to do, they could do without really any fail or feeling annoyed. There has been a lot of debate about how AI assistants are not the best user experience and don’t feel “human” enough.

Channel integrations and road ahead

Direct integrations with x.ai at this point include MS Office, MS Team, G Suite, Zoom and few others through Zapier. With nearly 20 employees working on making x.ai more accurate, the startup will expand via two interesting integrations besides generic integrations such as powerful platforms that allow a ton of vendors to stick to each other. 

One by channel integrations; say WeChat in China or Facebook messenger or WhatsApp or wherever people talk about meeting up. The second will be into other things that come along by fixing a meeting. Example conference systems like Uber, Hangouts, Zoom, and other resource systems such that Amy can book conference rooms. At present, it offers the ability to book conference rooms in G Suite and MS office. Dennis wants to make sure that anything that comes along with setting up meetings like iterations should be done by Amy. 

Amy is now able to able to login to a person’s Zoom account, pull out a URL, attach that to a meeting. At the same time, if the consumer wants to book a meeting room for this call, Amy is be able to look for the resources to find the meeting room and book at the same time. 

The company does not want to limit itself to one language or just setting up meetings. “We do want to be multilingual so that this can exist in German for German speakers or in Spanish or in some version of Chinese, says Dennis. He sees his company solely focused on getting that event on the calendar and making it as fast as possible. 

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Why we need to open up AI black boxes right now https://dataconomy.ru/2019/05/23/why-we-need-to-open-up-ai-black-boxes-right-now/ https://dataconomy.ru/2019/05/23/why-we-need-to-open-up-ai-black-boxes-right-now/#comments Thu, 23 May 2019 13:49:45 +0000 https://dataconomy.ru/?p=20781 AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets? Think of tech companies creating elaborate AI hiring tools, only to […]]]>

AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets?

Think of tech companies creating elaborate AI hiring tools, only to realise the technology was learning in the male-dominated industry to favour resumes of men over women. As much as this seems to be a facepalm situation, this happens a lot and seems not so easy to solve in an imperfect world, where even the most intelligent people have biases.

“Data scientists are capable of creating any sort of powerful AI weapons”,

Romeo Kienzler, head of IoT at IBM and frequent speaker at Data Natives.

“And no, I’m not talking about autonomous drones shooting innocent people. I’m talking about things like credit risk score assessment system not giving a young family a loan for building a home because of their skin color.”

These ethical questions rang alarm bells at government institutions. The UK government set up a Centre for Data Ethics and Innovation and last month the Algorithmic Accountability act was proposed in Washington. The European Union created an expert group on artificial intelligence last year, to establish an Ethics guidelines for Trustworthy Artificial Intelligence.

IBM had a role in creating these guidelines, which are crucial according to Matthias Biniok, lead Watson Architect DACH at IBM, who designed CIMON, the smiling robot assisting astronauts in space. “Only by embedding ethical principles into AI applications and processes can we build systems that people can trust,” he tells.

“A study by IBM’s Institute of Business Value found that 82% of enterprises are now at least considering AI adoption, but 55% have security and privacy concerns about the use of data.”

Matthias Biniok, lead Watson Architect DACH at IBM

AI can tilt us to the next level – but only if we tilt it first.

“Artificial intelligence is a great trigger to discuss the bias that we have as humans, but also to analyse the bias that was already inducted into machines,” Biniok tells. “Loans are a good example: today it is often not clear for a customer why a bank loan is granted or not -even the bank employee might not know why an existing system recommended against granting a loan.”

It is essential for the future of AI to open up the black boxes and get insight into the models.

“The issue of transparency in AI occurs because of the fact that even if a model has great accuracy, it does not guarantee that it will continue to work well in production”

Thomas Schaeck, IBM’s Data and AI distinguished engineer, a trusted portal architect and leader in portal integration standards.

An explainable AI model should give insight into the features on which decision making is based, to be able to address the problem.

IBM research, therefore, proposed AI factsheets, to better document how an AI system was created, tested, trained, deployed and evaluated. This should be audited throughout their lifecycle. It would also include suggestions on how a system should be operated and used. “Standardizing and publishing this information is key to building trust in AI,” says Schaeck.

Schaeck advises business owners to take a holistic view of the data science and machine learning life cycle if they are looking to invest in AI. Choose your platform wisely, is his advice. One that allows teams to gain insights and take a significant amount of models into tightly controlled, scalable production environments. “A platform, in which model outputs and inputs are recorded and can be continuously monitored and analysed for aspects like performance, fairness, etc,” he tells.

IBM’s Fairness 360 toolkit, Watson Studio, Watson Machine Learning and Watson Open Scale can help you out with this. The open-source Fairness 360 toolkit can be applied to every AI model before it goes into production. The toolkit has all the state of the art bias detection and mitigation algorithms. Watson Studio allows teams to visualize and understand data and create, train and evaluate models. In Watson Machine Learning, these models can be managed, recorded and analyzed. And as it is essential to keep on monitoring AI during its lifecycle, IBM Open Scale connects to Watson Machine Learning and the resulting input and output log data, in order to continuously monitor and analyze in-production models.

Yes, it can all be frightening. As a business owner, you don’t want to end up wasting a lot of time and resources creating a Frankenstein AI.

But it is good to keep in mind that just as our human biases are responsible for creating unfair AI, we also have the power to create AI which mitigates, or even transcends human biases. After all, tech is what we make of it.

If you would like to know more about the latest breakthroughs in AI, Cloud & Quantum Computing and get your hands on experimenting with blockchain, Kubernetes, istio, serverless architecture or cognitive application development in an environment supported by IBM experts, then join the Data & Developers Experience event that is going to take place on June 11-12 at Bikini Berlin. Register here, it’s free.

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IBM Watson IoT’s Chief Data Scientist Advocates For Ethical Deep Learning and Building The AI Humans Really Need https://dataconomy.ru/2018/11/16/ibm-watson-ai-interview/ https://dataconomy.ru/2018/11/16/ibm-watson-ai-interview/#comments Fri, 16 Nov 2018 11:54:59 +0000 https://dataconomy.ru/?p=20511 In terms of pioneering data-based technology, IBM are the gold standard. Indeed, IBM has held the record for receiving the most patents every year for the past 25 years and has developed countless revolutionary technologies, from SQL to the world’s fastest supercomputer. It should come as no surprise that IBM hopped on the Data Scientist […]]]>

In terms of pioneering data-based technology, IBM are the gold standard. Indeed, IBM has held the record for receiving the most patents every year for the past 25 years and has developed countless revolutionary technologies, from SQL to the world’s fastest supercomputer.

It should come as no surprise that IBM hopped on the Data Scientist bandwagon before most- IBM Switzerland hired their first Data Scientist back in 2011. That Data Scientist was Romeo Kienzler; a deep learning enthusiast and specialist in computer networking and distributed systems.

Over his 7 years with IBM, Romeo has become IBM Watson IoT’s Chief Data Scientist, and witnessed copious developments at the cutting edge of data science, AI, and deep learning. He’ll be sharing his insights into the future of AI, machine learning and more in his keynote at Data Natives 2018. For a taste of what to expect, we picked Romeo’s brain about the opportunities and dangers of AI, what the public needs to know about AI, and what it takes to build AI humans really need.

Tell us a little bit about your background and what you’re working on.

I grew up half-Indian in Black Forest, Germany. I studied Computer Networking with a focus on distributed systems. Then I completed my M.Sc. in Bioinformatics at the Swiss Federal Institute of Technology. I’ve been passionate about Data Science ever since.

I was the first Data Scientist working for IBM Switzerland back in 2011. Since the first breakthrough of Deep Learning, I’m just fascinated by the performance of those magic black boxes. We’re seeing a huge shift from traditional machine learning to Deep Learning models. They simply outperform everything we’ve seen before.

What about past work- which accomplishments are you most proud of?

My LSTM based Anomaly Detector. If you search for Anomaly Detection in IoT you find my publications on the first page. This system is used by many of our manufacturing clients.

You’re also a Coursera instructor- what’s your perspective on how MOOCs are changing the tech sphere (and beyond)? What opportunities are being opened up here?

Education is now in the hands of everyone- regardless of ethnic or financial background. You don’t need rich parents or a grant to become a leading AI expert. A laptop with an Internet connection paired with the willingness to learn is sufficient. Through Coursera I’ve nearly trained 100.000 data scientists on AI, Deep Learning, Advanced Machine Learning and Signal Processing.

Your keynote talk at Data Natives addresses the “AI humans really need”- briefly, what AI technologies do humans need?

We definitely don’t need AI trying to sell us stuff we don’t need. We need AI which we can trust. It needs to be in our hands and controlled by humans- not by multinationals.

In terms of regaining control of our data: how do we go about that, in your opinion? Is it a case of international/national regulation, self-regulation, increased data literacy and activism on a personal level, or a combination of all three?

I’d say it is a multidimensional problem. The root of the problem is the very low privacy awareness- especially outside the European Union. That lack of awareness stems mainly from data illiteracy. It’s hard to imagine the tremendous amount of data organisations are able to collect. Only through being actively involved in such projects allowed me to really get the picture.

Although GDPR was a step into the right direction, this problem can only be solved at a user level. Zero Trust and Zero Knowledge algorithms are the ones to be looked at. At the moment where you have to trust a government or corporation, you are doomed. When data is only used to train AI models, data leakage can’t be proven at all. But on the other hand, it’s a prisoner’s dilemma. The one protecting his data is serving humanity but scarifying himself. Have you ever tried to hail a Berlin taxi without an online maps application handy? *laughing* I’m partially doing it, I’m using TOR, DuckDuckGo, Signal, TOX, OpenStreetMaps and some random fake identities on YouTube. But who actually wants to do that? Sometimes I get a bit tired of this as well. And don’t forget that all those services are for free because we are paying with our personal data.

Intriguingly, your abstract also states: “Even conservatively minded folks will notice that things can get out of control. Actually they are already. Or aren’t they?” What to tell us a little more about that? How “out of control” is AI currently?

I’m talking about racist AIs- AI’s that tries to attack our privacy and further increase the gap between rich and poor. Actually, it’s not the AI that is out of control- it’s our data. AI is just the tool. Like the combustion engine for the oil. So we have to regain control of our data. Otherwise we are doomed.

On the subject of racist AIs, and AIs that seem to perpetuate unconscious biases in the data: this is a problem which has (very publicly) tripped up a lot of major players in the tech space, and crucially, has had untold/intangible impacts on the people whose lives were affected by these algorithms. But what can be done to minimize the bias of algorithms trained on data generated by biased humans?

This is where the IBM AI Fairness 360 toolkit kicks in. It allows not only for detecting bias on various dimensions, but also allows for fixing it. Here is a huge set of well-established algorithms to mitigate bias. It is important that this crucial part of model unbiasing is fully auditable. That’s why IBM has open sourced the whole toolkit.

There seems to be a huge gulf between what’s actually happening with ML/AI today and broader public understanding of AI. What’s the one thing you wish everyone knew about AI?

AI needs data. If you have enough data, an AI can learn anything. This is stated by the universal function approximator theorem. Even a single hidden layer neural network can represent any mathematical function. Those functions are highly sought-after, since the allow us to control and predict nearly everything, ranging from a detailed psychological profile of an internet user to a complete model of the physical world. The individuals and corporations that own these models have power in their hands similar to a nuclear bomb. So let’s create clean energy from AI.

Similarly, there seems to be an astonishing binary in sentiment towards AI. In terms of these competing, hyperbolic viewpoints: is AI here to steal our jobs and autonomy, or will it merely free us up to perform more fulfilling work?

This is in our hands. We need to prevent people from building nuclear AI bombs, and instead make sure we are creating nuclear AI powerplants that serve humanity. It’s the responsibility of each and every AI engineer.

What AI-based projects have really caught your eye recently?

It’s definitely Watson Debater, an AI system that can logically reason based on publicly available knowledge, take a particular point of view and defend and argue about it.

Can you tell us a little more about Watson Debater, and how it works?

Debater is making heavy use of Deep Learning based Natural Language Processing. It picks out factual claims from various sources and classifies whether they support or oppose a particular point of view. Then a short speech is made, which consists of what- according to the algorithm- are the best arguments and evidence for each side.

Which technologies and methodologies do you think are going to play a crucial role in the future development of AI?

From a neural network perspective, we are very good at creating new deep learning architectures inspired by the latest findings in neuroscience. But for training an AI, we still rely on variants of Gradient Descent: a non-deterministic and compute-intensive neural network training process which is hard to get right. So the next generation of AI will be trained with some sort of biologically inspired attention mechanism– so more like a human brain, if you will.

Second, Deep Learning models are just blowing our minds– but not the minds of stakeholders, because we can’t explain them. It’s in the nature of stacked non-linear feature space transformations (which is what a Deep Learning neural network is actually doing) that they are not explainable. If we can’t explain them, stakeholders don’t trust them. So all around ethical assessments and bias detection/mitigation is a very hot topic, and IBM is a leader here.


Romeo Kienzler will be giving a keynote presentation at Data Natives– the data-driven conference of the future, hosted in Dataconomy’s hometown of Berlin. On the 22nd & 23rd November, 110 speakers and 1,600 attendees will come together to explore the tech of tomorrow, including AI, big data, blockchain, and more. As well as two days of inspiring talks, Data Natives will also bring informative workshops, satellite events, art installations and food to our data-driven community, promising an immersive experience in the tech of tomorrow.

Additionally, Romeo will be giving an Introduction to Deep Learning with Keras and TensorFlow as part of the IBM MiniTheatre programme; full details here.

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Food Delivery Via Drones: A Reality in Iceland https://dataconomy.ru/2018/05/08/food-delivery-via-drones-a-reality-in-iceland/ https://dataconomy.ru/2018/05/08/food-delivery-via-drones-a-reality-in-iceland/#respond Tue, 08 May 2018 13:20:52 +0000 https://dataconomy.ru/?p=19832 Goldman Sachs predicts that a $100+ billion market opportunity for drones will exist until 2020 – and that’s not just for military usage. The aviation sector is clearly an attractive market capable of drawing in young entrepreneurial teams from across the world, such as the German startup AIRTEAM – an aerial intelligence platform that uses […]]]>

Goldman Sachs predicts that a $100+ billion market opportunity for drones will exist until 2020 – and that’s not just for military usage.

The aviation sector is clearly an attractive market capable of drawing in young entrepreneurial teams from across the world, such as the German startup AIRTEAM – an aerial intelligence platform that uses drone and satellite data in order to rapidly measure, map and inspect properties – and San Francisco-based Skycart – a German startup building the UAE‘s first ever autonomous drone delivery service.

At CUBE Tech Fair, some of the world’s most forward-thinking drone experts will present user cases. We talked with one of the experts, Yariv Bash, Co-Founder and CEO of Flytrex. Flytrex is ushering in the next generation of on-demand delivery, enabling any business, from SMBs to e-commerce giants, to integrate same-day autonomous drone delivery into their offering

But what really makes Flytrex so special? And how are drones going to develop in the near future? Maren Lesche interviewed Bash to gain some insights into his motivations and the industry as a whole.

Maren: Nice to meet you, Yariv. Tell me a bit about yourself. How did you become interested in the tech industry? Did you have an idol you looked up to?

Yariv Bash: My grandfather played a crucial role in my interest in technology and aviation. When I was a child, he would buy me do-it-yourself engineering kits, and we would sit together for hours assembling engines and working on projects. He instilled a love of technology and engineering in me and played a crucial role in my career path.

How did you come up with the idea for Flytrex?

Using our respective expertise, Amit, my co-founder, and I actually initially began selling “black boxes” for drones – similar to the black boxes in commercial airlines. These boxes provided real-time geo-trafficking and flight path sharing. When we realized that drones are going to be the future of on-demand delivery, with consumer expectations for faster and faster deliveries constantly rising, we saw an opportunity. Drones could offer the perfect solution. With this in mind, we shifted direction and began focusing on developing the ideal autonomous drone delivery service that would enable any retailer to provide on-demand deliveries and compete with the e-commerce giants.

Why drones? What makes them innovative?

Widespread commercial drone use was once considered science fiction. However, as technology has improved, we have pushed the boundaries of what is perceived as possible. Drones are set to become a core component of countless industries.

In terms of on-demand delivery, drones are able to tackle several challenges simultaneously. Drones are faster and can facilitate same-hour delivery: consumers can receive items in a matter of minutes. In addition to drastically cutting delivery times, they also slash costs, as they are far more efficient to operate than delivery bikes or other last-mile services.

Moreover, as drones are 100% electric, they substantially reduce emissions. And if that isn’t enough, they are also far safer than traditional delivery options – reducing road congestion without being dependent on error-prone drivers. Our advanced navigation systems mean drones can maneuver around large obstacles such as cityscapes. As the tech improves, drone safety will also improve.

What does a drone delivery look like today?

Drone delivery today is just beginning. There are specific regions that have approved autonomous drone deliveries, and these projects are vital in demonstrating the widespread viability of commercial drones – from a regulatory perspective as well as acceptance by the general public.

In Reykjavik, Iceland, our drones fly at roughly 70 meters in the air, meaning they are out of sight to any pedestrian, and don’t add to noise pollution. The drone system allows direct delivery between two parts of the city separated by a large river, saving energy and human resources normally allocated to the circuitous ground route over a river bridge located in the south of the city.

Our system requires an employee of AHA – our partner in Iceland and a local leader in e-commerce – to simply load a package onto a drone and click a button on the Flytrex platform. The drone automatically flies off to a designated landing area close to customers. Under the current model, another AHA staff member collects the package from the drone at the gathering point, and delivers it to the customer in that neighborhood. Flytrex is improving upon this system daily and has plans to begin lowering packages via a cable directly to consumers’ backyards, making drone deliveries even more efficient. In this
model, Flytrex’s user-friendly software allows customers to track their delivery drone through an easy-to-use app.

What are some of the challenges the drone industry is expecting to face in the future?

The primary challenge is proving to regulators that widespread adoption of drone deliveries is possible. This challenge, however, also presents an important opportunity: ultimately, it will be the regulators’ stamp of approval that will help usher in the era of commercial drones.

That said, general awareness poses another challenge. People have an inherent fear of the unknown, and drones represent something that has never been done before. Getting society to accept drones as a viable form of delivery will be key to ensuring their broader implementation.

Managing drone traffic will also be an obstacle. As commercial drones become more prominent, an important milestone will be a universal air traffic management system that will keep track of all the drones in the air, and ensure they are on-course and functioning correctly.

If you could give one piece of advice to other startups, what would that be?

The sky is not the limit – think big, validate your idea, and push forward like crazy.

Thank you for a great interview Yariv!

You can hear Yariv speak at the 11:15 am panel on May 16th on the Cube Stage together with Mohammed Johmani, CEO of SPACE and Kay Wackwitz, CEO of Drone Industry Insights. If you want to attend, please use the code CTFVIP2018 for free access to the conference.

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Getting Your Feet Muddy with Data Science: A Conversation with Ross Taylor https://dataconomy.ru/2018/03/15/getting-your-feet-muddy-with-data-science-a-conversation-with-ross-taylor/ https://dataconomy.ru/2018/03/15/getting-your-feet-muddy-with-data-science-a-conversation-with-ross-taylor/#respond Thu, 15 Mar 2018 13:00:02 +0000 https://dataconomy.ru/?p=19489 As companies digitize and data occupies a more central place in our lives, corporations are struggling to find enough talented people to meet the business challenges they face. This has created exceptional opportunities for individuals who are up for tackling those challenges. Ross Taylor of ALPIMA started his career as an economist working for the UK Treasury […]]]>

Getting Your Feet Muddy with Data Science: A Conversation with Ross Taylor

As companies digitize and data occupies a more central place in our lives, corporations are struggling to find enough talented people to meet the business challenges they face. This has created exceptional opportunities for individuals who are up for tackling those challenges.

Ross Taylor of ALPIMA started his career as an economist working for the UK Treasury on financial services during the Eurozone crisis. He is now among the growing number of people using their own initiative to satisfy the need for data scientists. Ross now works on the cutting-edge of predictive finance and risk modeling, building data-driven asset allocations for some of the world’s largest and most prestigious hedge funds and asset managers. Insightive.tv’s Robin Block sat down with Ross to discuss his motivations, the common challenges faced in data science and what it take to break into the field.

Robin: How did you enter the field of data science, and what advice would you give someone trying to do the same?

Ross: My advice would be to get started on a side project or get involved in open-source software. I switched from using primarily R to Python three years ago, and a side project basically drove this development. The project was to build a library for time-series methods in Python, which became PyFlux. Ultimately, I got to present PyFlux at multiple conferences in London and San Francisco, and the library was voted one of the most popular Python libraries of 2017.

The key problem with open-source is that it is essentially unpaid labor. With that said, it presents great opportunities to create innovative solutions and to advance careers. Additionally, when you do open-source work, it means having many eyes looking at your code. This not only incentivizes you to push yourself, but it also gives you the opportunity to gain feedback and different perspectives. It is how you get your feet muddy and learn from doing. The barrier to entry in open-source is pretty low — you just have to be motivated.

For project ideas, I would recommend focusing on an area of interest, seeing what’s available in the Python space and trying to fill a gap in the data science toolkit. This may mean contributing to an existing project or starting a brand new project.

What mistakes did you make entering the field, and what have you learned through your experience?

There is an inherent trade off you face when choosing software solutions for your company. If you are too old fashioned, you will get left behind; if you are too close to the bleeding-edge, you will often have less community support and less stable software. You are also making a bet when adopting new software that it will become established. The key is to have a core stack that has widespread adoption/stability — software such as Docker, PostgresSQL, Python 3 — and new solutions where it makes sense. When I first started, I had an unhealthy bias towards trying the latest and greatest thing!

My other initial bias was favoring model-complexity in machine learning. Machine learning is a Swiss Army knife — different tools should be used for different problems. The most complex tool is not necessarily the best tool for the job. This is especially true in finance. Because of the low signal to noise ratio in the data, and non-stationarity, you often find simpler approaches are the most viable, and actually perform surprisingly well compared to more complex methods such as tree-based models, neural networks and so on. Fundamentally, however, you need to have a reason to use the tool you choose. Your model complexity should be tailored to your objective; for example, predictive accuracy or whether you can reasonably productionize the model.

What are the big changes occurring in data science, and which currently interest you?

The big push in the past six months has been towards decentralized applications. People are now beginning to question whether it is good to have data and services reliant on few big providers  – in cloud computing, databases, etc. – and are instead looking for trustless solutions that utilize technologies such as IPFS and Ethereum. It is an open question whether the Dapp model will work, as scalability problems remain and there is an efficiency sacrifice by moving away from centralization, but it is a big theme now.

For AI and machine learning, the big revolution in the last two years has been the growth in software. TensorFlow and PyTorch are part of a new paradigm known as “differentiable programming,” which utilizes a technique known as automatic differentiation to allow for quick construction of deep learning models. When you write software, you are now able to integrate advanced learning algorithms directly. I think the next change is going to be the maturation of the “machine-learning-as-a-service” business model. Examples of this model include AWS Sagemaker and Google Cloud AutoML. It is still early days, but this could allow for greater penetration of data science into more verticals and help solve the skill shortage problem.

What do you most admire about your company and data science companies more generally?

The best thing about working at ALPIMA is the ability to try new ideas. I am currently in a position that allows me to look at ways of applying new technology to make finance more efficient and more data-driven. This is a really rewarding liberty, especially as it enables the company’s clients to build some of the most impressive products in the industry.

In data science more generally, the emphasis needs to be not only the companies, but also the open-source community, which has really driven the development of much of the toolkit (for example, pandas and scikit-learn for Python). This is an encouraging achievement, as it shows people from all over the world can collaborate to make cutting-edge software, without coordination from a single company or set of companies.

This also ties into my own motivation for work, which is to have the broadest impact possible. That means making software applications that are widely used and provide substantial utility. That doesn’t mean I have one master project in mind, but I am driven by the problems I want to solve and try to use data to solve those problems.

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Women in Data Science: “We built a conference to become a movement” https://dataconomy.ru/2018/03/04/women-data-science-built-conference-become-movement/ https://dataconomy.ru/2018/03/04/women-data-science-built-conference-become-movement/#comments Sun, 04 Mar 2018 18:44:29 +0000 https://dataconomy.ru/?p=19425 2018 has brought a new focus to the careers, excellence and issues of working women, and this extends into the fields of tech and data science. As we approach the Women in Data Science (WiDS) conference on March 5, 2018, it is important to take a moment and reflect on where women are now in […]]]>

2018 has brought a new focus to the careers, excellence and issues of working women, and this extends into the fields of tech and data science. As we approach the Women in Data Science (WiDS) conference on March 5, 2018, it is important to take a moment and reflect on where women are now in data science, and where they need to go. Dataconomy’s MD and WiDS ambassador Berlin, Elena Poughia, sat down with Ann Rosenberg, Senior VP of SAP Next-Gen, and Alexa Gorman, Global VP of the SAP.io fund to discuss this topic.

Both Rosenberg and Gorman are leaders at SAP who are empowering each other and other women in tech and data science. As leaders, they recognize the importance of their unique philosophies and mindsets.

“When I moved to the United States a couple of years ago after many years working in my global role from Denmark, and I began to have my team more closely around me, I began to see how women around me were treated,” said Rosenberg. “It was a very interesting observation because it’s not like I haven’t had my challenges as a woman. But I never thought about it in the mindset that it was because I was a woman. I originally thought when I was challenged by men, they were just smarter than me. My philosophy is that you should be who you are and you should treat everybody like a human being… with respect.”

Gorman promotes the empowerment of her teams in order to allow ideas to flourish. “I believe strongly in empowering teams to take decisions, we have a small team here that can make decisions and try new things”, said Gorman. “You have to weigh out the risks – and empowerment is really important to me.”

Both women are mission driven and have managed to make a difference through their work. Rosenberg has been leading the SAP University Alliances and from 2012 to 2017 the number of educational institutions in the SAP University Alliances program increased from 1,300 in 70 countries to 3,360 in 112 countries. In 2016 Rosenberg introduced SAP Next-Gen as a purpose driven innovation community connecting customers to academia, startups, accelerators, and purpose driven partners. “Today there are 72 SAP Next-Gen Labs and hubs at campuses, accelerators and SAP locations, and a new lab opens every second week in locations all over the world,” shares Rosenberg.

Women in Data Science: "We built a conference to become a movement"

In data science and tech, the need for women is clear and apparent. Luckily, the field is still being explored, so women have the opportunity to become pioneers.

“In areas like AI that having more data scientists who are female will ensure that the algorithms and the data sets that AI is being trained on will better reflect the diversity in the world in general,” Gorman said. “Women are still underrepresented. 26%, according to a Forbes article of all data science jobs in the US are held by a woman, which is not representative of the population. A white male data scientist’s certain, potentially unconscious biases will be reflected in his AI solution. Women have a great role to play, and we can do a lot to get rid of biases or identify biases in AI.”

Rosenberg adds “The incredible conference Stanford puts together is something that we wanted everybody to be allowed to be part of and that is where together with the mission Stanford has, women in data science, and our mission the #sheinnovates program got developed. We built a conference to become a movement. In 2017 my team hosted 24 satellite events in 20 countries, and this year we are hosting 30+ events in 22 countries. You have women in those cities who can come to those events and be inspired by local leaders, local role models, local women, and you can listen to the incredible speakers of women in data science.”

When these women describe their individual programs, their voices noticeably get more excited. You can tell that they care about their work in empowering women to really want to achieve their goals. Both Rosenberg and Gorman are passionate about it, and it is the same passion that got them to where they are.

“We ran an accelerator program in San Francisco last year for women founded startups,” said Gorman. “It was really successful. We’re also running the program again this year. Actually, applications are open in New York for women-founded startups. At SAP.iO, we found that the quality of women-led startups is exceptional. It’s why we engage as much as possible with the community – through hosting and participating in events across the globe. We are very committed to promoting and supporting female entrepreneurship in general.”

Gorman adds, “There are so many innovations that are changing the world like space tourism and cryptocurrencies. Those are all fields not yet dominated by either gender. They present opportunities for women to position themselves or to take leadership roles. At SAP, in the blockchain space, we have amazing female colleagues who are true thought leaders. That’s the great thing about tech – there’s so new many fields and innovations that are popping up. We just need to want it.”

Mentorship is an important theme for both Rosenberg and Gorman. Each of them praises their mentors at SAP, male and female. “Building on the tradition of mentorship, we have launched the SAP Next-Gen Advisors initiative to connect experts in the SAP ecosystem to the next generation social entrepreneurs, developers, data scientists, makers, and tech founders to foster innovation with purpose linked to the 17 UN Global Goals while adopting the latest SAP technologies,” said Rosenberg.

“For me, it’s kind of like what I always have had, like my mother, who has been my mentor, my role model, the person who has given me the strength and trust and the belief in myself. That is what I want to give women around the world by building up the #sheinnovates program. It’s very much a go-getter kind of program. There are a lot of things we’re doing with the program, educating people, making sure people have a mentor and making sure people have access to technology,” said Rosenberg.

“I had some great male and female mentors along my journey from within and outside SAP,” adds Gorman. We have an incredible executive board member, Adaire Fox-Martin, and Chief Strategy Officer Deepak Krishnamurthy, who are exactly the kind of role models that I look for. They embody the organization’s culture and values. I am grateful for all my mentors and all the life-changing advice they gave along the way. We tend to get buried in the day-to-day activities and not think about the tough or uncomfortable questions, such as: where’s the next step? where do you want to grow?  and how do you plan to get there?’ What really helped me in my career is refocusing. If you don’t know where you want to go, you definitely won’t get there. If you know where you want to go, you’re at least going to get somewhere close.”

Of course, they’re willing to mentor other women and offer their own advice.

“If you want to change things, the first thing you need to do is understand what the change means for you and how you can contribute,” said Rosenberg. “We did a super cool exercise a few months ago and we were asked to pretend we were time travelling and make a postcard from 2030. Then you go back in time to 2018 and you explain to people what the world looks like. It was really cool because by doing that, you are actually committing yourself to what you would like the future to look like. That means you actually made a life mission for yourself. I have been doing this exercise with other people and it has had an impact on people because we as people only think two, three years ahead maximum, or half a year ahead. But 2030? If you are looking at 2030 you have to ask what you want it to look like and how you are going to contribute. “

“The advice I would like to give to female entrepreneurs is: be bold, take risks and keep a sky-is-the -limit mindset. Never take no for an answer,” said Gorman.

 

Women in Data Science: "We built a conference to become a movement"

 

Alexa Gorman is the Global VP of the SAP.iO Fund & Foundry, working at SAP since 1999. She has held leadership roles in marketing, business development and strategy throughout her career at SAP, working with companies like Google, PayPal, and UPS. At SAP.iO, she works with investments in startups in Europe to accelerate their businesses.

 

Women in Data Science: "We built a conference to become a movement"

Ann Rosenberg is the Senior VP of SAP Next-Gen, an extension of SAP University Alliances and SAP Young Thinkers, which aim to connect young people with tech careers. She works with UN Women Global Innovation Coalition for Change which includes 25 companies, and hosts the UN Women Innovation, Technology, and Entrepreneurship Industry Forum at the SAP Leonardo Center in New York including speaking with UN Women at Davos, Mobile World Congress and South by Southwest. She is also the SAP woman executive sponsor of #sheinnovates, and the SAP Women in Data Science Ambassador. She was recognized as a Top Blockchain Influencer in 2018 by Women In Tech – Hot Topics and Tech Influencers.

The Women in Data Science Conference will be held on March 5, 2018 at Stanford University. You can register for the event here.

Dataconomy and Data Natives is a partner for corresponding events in Berlin on March 5. You can still register for the breakfast and the panel!

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Machine Learning for Connecting Organizations: A Conversation with Roger Gorman https://dataconomy.ru/2018/02/22/machine-learning-connecting-organizations-conversation-roger-gorman/ https://dataconomy.ru/2018/02/22/machine-learning-connecting-organizations-conversation-roger-gorman/#comments Thu, 22 Feb 2018 12:00:05 +0000 https://dataconomy.ru/?p=19258 In the 1990s, anthropologist Robin Dunbar hypothesized that the maximum number of people an individual can personally know and remember is 150 — Dunbar’s number. The specific figure is debated, but it is common sense that the human cognitive capacity to understand relationships at scale has a limit. The larger a company, the more moving […]]]>

Machine Learning for Connecting Organizations: A Conversation with Roger Gorman

In the 1990s, anthropologist Robin Dunbar hypothesized that the maximum number of people an individual can personally know and remember is 150 — Dunbar’s number. The specific figure is debated, but it is common sense that the human cognitive capacity to understand relationships at scale has a limit. The larger a company, the more moving parts, the more projects — the more people.

Roger Gorman is an entrepreneur guided by a desire to end the isolation and fragmentation that occurs within large organizational structures. He is the Founder and CEO of ProFinda, a SaaS company that enables connectivity through the use of machine learning. Insightive.tv’s Robin Block sat down with Roger to understand how ProFinda is changing corporate culture and how it operates internally.

Robin: Why is machine learning important to the future of corporate culture?

Roger: The benefit of large organizations is that they generate a competitive advantage through increasing the number of people working on projects. The problem is that once an organization reaches a certain size, the people within it lose touch with each other and it becomes difficult to source the best talent for any given project. There is no hub that understands the skills of everyone in the company or even the extent and detail of all the projects taking place. It creates a black hole within the organization that is at odds with the fundamental purpose of driving growth.

Machine learning provides the capability to build a people and knowledge platform that intelligently connects everyone and everything — helping people find the right insight or expert at the right time. Our platform is built on Ruby and employs algorithms similar to those used by Google and dating websites. It leverages a Knowledge graph that is cross-referenced with a Network graph and Attitude graph to offer an incredible view of the entire organization. It then hooks into a matrix focused on reward, recognition and motivation – because we are dealing with people.

What really makes our system usable, however, is that it links to a network of tens-of-thousands of terms to accurately pick apart syntax and context within questioning to allow for effective communication using natural speech patterns.

I have had basically five jobs in my life, and I have been fired from four of them — probably why I had to set up my own business! When I reflect on each of those experiences, they are all horrible examples of outdated and large corporate structures in which I was running around trying to be useful, but ultimately coming at odds with expectations. Our product is designed to ‘shape the future of work’ through improving that environment across the board.

How have you attracted staff when there is a talent shortage in technology and data science?

There is clearly a gap between supply and demand for good technologists. Since talent supply is so low, workers are empowered to choose how they want to live their lives. This is a problem that the Googles of the world have historically thrown money at. However, there is a point at which more money becomes less important than having a job that is actually enjoyable.

We offer competitive salaries, but we also offer people the ability to work on groundbreaking and interesting projects in an environment that lets them work how they want and feel appreciated for their contributions. My main barometer is that everyone at ProFinda is actually excited to come in on Monday morning.

What are your end goals for the use of machine learning and the future of ProFinda?

We are building a business that will have a new and genuine impact on society. Beyond the corporate story, we have a social plan for our platform to bring people together across charities, social causes and interest groups. For example, we’re leveraging our platform to reinvent global collaboration on climate change. Presently this is siloed across different functions — the UN, The World Economic Forum, C40 and so many other disparate groups. We aim to integrate all these “tribes” and their efforts into one single intelligent ProFinda-powered platform.

In the long-term, our goal is to change how organizations and society function and communicate. However, just as a wish list, I would be happy going to my grave knowing that this company has had a positive impact on the lives of those who work here. There is a family/team culture at ProFinda of which I am deeply proud. Perhaps I can fire myself in a decade or so once we’re really humming, to complete the story!

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“The Mindset is Often the Hardest Thing to Change” – An Interview with Karol Przystalski https://dataconomy.ru/2017/12/18/mindset-often-hardest-thing-change-interview-karol-przystalski/ https://dataconomy.ru/2017/12/18/mindset-often-hardest-thing-change-interview-karol-przystalski/#respond Mon, 18 Dec 2017 12:53:07 +0000 https://dataconomy.ru/?p=19006 Karol Przystalski is the CTO and founder of Codete, a company specializing in IT consulting and implementation of innovative solutions for businesses. Karol obtained his Ph.D. in Computer Science from the Institute of Fundamental Technological Research, Polish Academy of Sciences, and has worked as a research assistant at Jagiellonian University in Krakow. Karol currently leads and […]]]>

"The Mindset is Often the Hardest Thing to Change" – An Interview with Karol PrzystalskiKarol Przystalski is the CTO and founder of Codete, a company specializing in IT consulting and implementation of innovative solutions for businesses. Karol obtained his Ph.D. in Computer Science from the Institute of Fundamental Technological Research, Polish Academy of Sciences, and has worked as a research assistant at Jagiellonian University in Krakow. Karol currently leads and mentors teams across various departments of Codete, and he has built a company research lab focused on machine learning methods and big data solutions.


What area of expertise do you see becoming more important for Codete in the coming years?

Definitely AI. We see that some of our customers are transforming from traditional businesses to businesses that are actually driven by AI. This is good for us, because we specialize in this area. We can help them with research and a lot of really cool stuff they may not think of on their own. From a data science perspective, it’s more ambitious to go deep into the details than to go through the standard e-commerce motions.

How as your role evolved as CTO of Codete?

In the past, I was just advising when it comes to technical points of view on a given project. Now I’m more involved in the research lab. My Ph.D. in computer science also includes an emphasis on AI (specifically HealthTech and diagnosing skin cancer), so I’m comfortable giving advice in this kind of setting. We are very often building AI-driven prototypes and concepts for companies that know the problem, but not necessarily the solution. I give advice on how to build the product and am happy about this direction.

How do you approach making data work more effectively for companies?

Each case is different, depending on what kind of direction you would like to go in the first place. There are obviously some emerging trends that are very often working well, such as AI, but that’s not your only option. If you are a new company, you also have to think about how you are going to obtain data.

What are some of the greatest digitilization challenges that your customers face?

Many times our customers know what they want to solve, but don’t have a technical understanding of how to solve it. They don’t have a core team with this knowledge. Additionally, the mindset is often the hardest thing to change for companies. I hear that big companies who already have access to data struggle to understand how they should best utilize it – basically a lack of knowledge or experience with these topics. That’s where we come in.

How do you see the AI market changing for devices?

When it comes to AI and mobile, we see companies like Apple releasing products that aim to show developers that AI is also applicable natively on mobile. We are currently building a platform like this that enables you to use any kind of framework for machine learning on any kind of platform or device. This is tricky, but we are hoping it will be a game-changer. Your product, for example, doesn’t need to be written in Python or Java; you can select your platform if you have a more flexible AI – the kind that we are working on.

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Taking a look at “Hyper-personalization” https://dataconomy.ru/2017/11/12/taking-look-hyper-personalization/ https://dataconomy.ru/2017/11/12/taking-look-hyper-personalization/#respond Sun, 12 Nov 2017 17:11:39 +0000 https://dataconomy.ru/?p=18839 The sudden evolution of the phone from a single-function device for making calls to a multifunctional pocket computer is one that’s already changed how we conduct business and live our lives a great deal. Now more than ever before, however, our behavior is also influencing this evolution to create products and services that respond to […]]]>

The sudden evolution of the phone from a single-function device for making calls to a multifunctional pocket computer is one that’s already changed how we conduct business and live our lives a great deal. Now more than ever before, however, our behavior is also influencing this evolution to create products and services that respond to our unique needs.  As the computing power of our handheld windows to the world continues to increase, so to will our ability to instruct them to do what we need them to do. This is where business leaders like Predict.io co-founder Silvan Rath see new opportunities for phones to streamline an array of daily tasks for consumers and provide better insights for businesses. Here are some of his thoughts about phone sensors, the retail market, and what lies ahead in an era of ever more personalized computers.

What was your motivation to start Predict.io? Have things unfolded as you expected?

The market has a way of breaking things that don’t change. So we did. We initially started with a SaaS model that helps cities and transport vendors to understand mobility behavior. But then retailers, QSRs and other verticals started picking up on the value that sits in location data. Hence, we started offering what they need. What they needed was an integration-free means to target customers of their competition.

Explain the advantages of ‘hyperlocal targeting’ when it comes to retail.

Any business with brick & mortar stores desperately needs to understand the offline behavior of consumers. We enable clients to not only get information about what users do when they are online, but about all of their favorite physical stores. There is tremendous value in being able to target your competitor’s clientele.

What is one disadvantage?

Our technology is the adtech equivalent of a laser beam. You can shine very brightly into extremely dense segments. On the downside, this doesn’t give you the reach of a torch – which would be the adtech equivalent of classical segmentation. Currently, the entire market is looking to find methods that convert as well as find methods for retargeting. Targeting a competitor’s visitors can deliver the desired results for many smart retailers.

Where do you see areas of untapped potential when it comes to utilizing smartphones as tools?

The device manufacturers are currently investing heavily in on-device hardware. One example includes specialized chipsets that can run Machine Learning processes very efficiently. This is the gateway to the very near future of hyper-personalizationion. We will see a wave of companies going extinct because they misssed the chance to invest in personalization. I was born in 1980. And even I have no tolerance for untargeted ads – be it online or by post. Imagine the latest generation growing up with voice assistants which will understand all your preferences.

Taking a look at "Hyper-personalization"

What do you ultimately hope to provide for an end user when you develop these new sensor-based tools?

There are many benefits of personalization beyond targeted advertising. Why do I need to buy a train ticket at the train station? Your phone already knows where you went. Why would I need to build a list of bookmarks of my favorite restaurants? My phone already knows where I regularly go. Why do I need to switch on the light every time I enter a room? The room knows I am there. The list goes on and on…

How does work on hospitality tools differ from developing a tool for banking and insurance?

The underlying challenge is the same. You need to deeply understand your customer or prospect. You need to be non-invasive but helpful. The data points that matter to each industry, however, differ widely. A restaurant would be very interested in what other places you frequent, whereas a bank would like to know if your credit card and your phone are located in the same place in order to help prevent fraud. Throughout all of these processes, we also find it important to regulate what we monitor. We are not pulling up insurance data for risk analysis. We also don’t pursue details like sexual orientation, race and other deeply personal data that could be inferred from location information.We feel it is not fair game to use location data for such purposes. The new European GDPR does provide boundaries in this area, but it doesn’t regulate everything.  That’s why we also self-regulate.

As more and more devices and digital tools do and decide things for us automatically, what types of actions do you believe people will want to continue to do, even if they theoretically could be automated?

Let’s talk about cars for a second. I don’t want to drive in traffic. But I do want to drive on a scenic road. Also, in home automation, current types automation are far from fool proof. Personally, I think there should always be a choice. The same way I can choose to cook or go out for dinner. Automation, after all, is a service.

 

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INVESTING, FAST AND SLOW – PART 3: FROM SATELLITES TO STOCKS https://dataconomy.ru/2017/09/29/investing-fast-slow-part-3-satellites-stocks/ https://dataconomy.ru/2017/09/29/investing-fast-slow-part-3-satellites-stocks/#respond Fri, 29 Sep 2017 11:33:05 +0000 https://dataconomy.ru/?p=18432 Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Previously, I sat down to talk about investing and data with co-founders of new investment management firms representing the high-frequency and low-frequency extremes: Walnut Algorithms and […]]]>

Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Previously, I sat down to talk about investing and data with co-founders of new investment management firms representing the high-frequency and low-frequency extremes: Walnut Algorithms and Global Systematic Investors. This time, I am speaking with Alexandru Agachi, COO of Empiric Capital, a data-driven asset manager whose stockholding horizon averages one month.

YOUR FOUNDERS HAVE A SATELLITE COMMUNICATIONS BACKGROUND. HOW COME?

Alexandru Agachi: Well, they are radio frequency engineers by training, so their core skillset is signal processing. These are people who spent their lives designing hardware and software that ultimately aims to deal with increasing the information-to-noise ratio in the tools that they use. In their case, these used to be antennas and satellite communication systems on airplanes and in the maritime industry as well as in defense systems. That whole field gives you exposure to a wide range of signal processing and pattern recognition techniques that can then be extrapolated to working with financial time series and financial data.

GIVEN THIS HIGHLY TECHNICAL BACKGROUND, WHY FOCUS ON STOCKS AND NOT, SAY, FINANCIAL DERIVATIVES?

AA: Our founders were creating this investment solution for themselves first, which is a reason you’d start with equities – the S&P500, the STOXX 600, large companies, liquid stocks, low turnover. They give you enough breadth that you can apply quantitative solutions and the idea behind everything we’ve done to date was also to do something original, because obviously, we’re not the first quant fund. When you’re asking that question, I imagine you’re looking at futures, at CTAs, and there are so many firms doing this very well. So we wanted to start everything from scratch. We started with proprietary pattern recognition models rather than factor models. It was not easy but it ultimately paid off because we ended up with much more original research than otherwise, and it’s starting to show.

IN THE FORM OF HIGH RETURNS?

AA: Well, in the form of quality of returns and therefore investor interest, I would say. In the form of being able to add something to investors’ portfolios, something that diversifies them, something that is not that correlated to everything else they already have in their portfolios. If you look at the CTA space, the top 20 CTAs tend to have an intra category correlation of around 70% from what I see, which is why you keep hearing more and more that the CTA space has been commoditized.

AREN’T THE SIGNAL PROCESSING TECHNIQUES YOU USE ALSO BECOMING COMMODITIZED?

AA: Absolutely. I think that you see a democratization of finance in general, including quantitative finance, but the barrier to entry is still very, very high. We estimate, for example, that we spent eight, nine years just building our back-test machine and we think that that’s the first thing a quant team should do because, without that, you’re blind.

BUT THE PATTERNS IN STOCK RETURNS THAT YOU EXPLOIT ARE LIABLE TO CHANGE AS A RESULT OF TRADING BY QUANT FIRMS SUCH AS YOURS…

AA: I think you’re absolutely right. So that’s one of the fundamental truths in investing. The markets are dynamic. They keep evolving, and that is part of why it’s such a challenging industry to be in, and we often say in the quant space that you judge a quantitative firm by its research program, not by where it is today. This also proves to be one of the main challenges for scientists and engineers who come into finance from deterministic universes – finance is a probabilistic universe and the structure of the data itself regularly changes, unlike in many other fields. So that’s why you obviously need to believe in your research program, in your ability to keep abreast with these changes in the market. I think these new strategies are certainly getting more and more popular, but we’re still far away from a place where the quant hedge fund space, which is now at about 930 billion dollars, changes the overall S&P500 market structure. It’s still tiny compared to the overall investment base of the S&P500.

DID YOU HAVE TO WORRY ABOUT OVERFITTING YOUR MODELS TO THE DATA?

AA: Well, to us it was simple because we started by spending nine months doing just theoretical research – experience based, but nonetheless theoretical, without any data. Once we came up with our first pattern recognition algos, we started testing them with market data and then refining and optimizing them. But we didn’t have to worry about overfitting because we didn’t start by doing data mining. So it was a very clean approach. Now, obviously, a lot of quant teams look for patterns in the data. Then, of course,  overfitting becomes increasingly important and you have to look into how they approach the problem and what patterns they find and how they come up with them in order to know just how worried you should be about overfitting. When we compare our backtest outputs with actual performance, it’s of course not perfectly matching, but it’s extremely close over the almost two years since we started live trading. So that’s why we have very good confidence that what gets output by our backtest machine is very close to what will happen in reality.

CAN YOU TALK ABOUT YOUR ACTUAL STOCK SELECTION PROCESS?

AA: We approach the problem entirely from an automated stock-picking perspective. So far, we have about 60 stocks on the long side at any point in time and then we have about a dozen stocks on the short side and that also is what drives our net exposure to the market. The idea is that the long arm is independent from the short arm, so each one of them scans our investment universe of 1,100 stocks every day and only invests where and if it sees an opportunity. And then the stocks in our portfolio every day compete among themselves and with the stocks outside of our portfolio.

TELL ME ABOUT THE DATA YOU USE

AA: The only data that we use in our investment system right now is end-of- day price data. It’s the most available and transparent data out there and the reason we’ve done this is because when you’re working in investment management, ultimately you’re trying to predict two things: returns and covariances. So that’s why we started working with hard data, which is the daily price data for all the instruments in our investment universe. The way we view it, you start with hard data and then you enter the realm of soft data, which includes accounting and sentiment data. What we’re going to start doing towards the end of this year, I expect, is to add these fundamental quant models and sentiment models, but these will be in our system as filters – for example, to tilt the portfolio or to eliminate certain stocks from a risk management perspective. That’s how we view the data world: you start with hard data, and then on top of it you add the soft data and you keep building up.

YOU SOUND CAUTIOUS ABOUT ALTERNATIVE DATA SOURCES…

AA: What you hear a lot with this soft data that get sold to funds is that there’s a very fast alpha decay with it. And you can easily notice a flurry of alternative data companies trying to sell data to investment firms, yet very few of the latter – none to my knowledge – have managed to build highly successful investment systems with alternative data at their core yet. But it will certainly be a key area of research for the industry in the years to come. Like the application of machine learning to investing, it is not an easy problem, but with enough time and work it can certainly be implemented successfully. Ultimately, to generate sustainable alpha you can look at data that no one else has or you can look at the same data as everyone else but in a different way, and these are the two ways that we explore in our quantitative investment system.

WHY DID YOU SETTLE ON PYTHON AS YOUR DEVELOPMENT TOOL?

AA: It was a very easy decision. If you go to things like C++ or Java, you’ll have software engineers specialized in those languages and they’re going to be perfectly fluent and comfortable with them, but they’re not at all as widespread as Python in academic departments. What we find is that you can go to a mathematics department, a statistics one, a machine learning one and people tend to be comfortable with Python and so are software programmers. If you go for other languages, you’re going to start having problems in terms of harmony inside the team. And as several of our team members have academic backgrounds rooted in research, and we also have academic partnerships in place, a versatile language that crosses from academia to industry was the right choice for us when we started.

IN ORDER TO ATTRACT INVESTORS, DON’T YOU HAVE TO SELL THEM A BLACK BOX?

AA: The human brain is by far the blackest darkest box out there, I think a lot of people overlook this. It’s such a fallacy to think that because you meet a fund manager once a year, which is a typical industry model, you know how they think – and especially how they think under stressful, on-the-job situations, and in a consistent manner over time, which is what really interests you when you judge a human versus a model. It’s important to put this in the context of the research done for the past couple of decades by experts, whether psychologists or behavioral economists, who specialize in studying the use of models versus experts. As the Nobel Prize Daniel Kahneman concluded after decades of research in this area, “Hundreds of studies have shown that wherever we have sufficient information to build a model, it will perform better than most people.” I always frame the debate in that framework. And what happens in the quantitative space, is that people who are comfortable with quant teams and with quant investment solutions always judge the research team, the people behind the models. And this is the right way.

WHAT’S YOUR TAKE ON THE FUTURE OF HUMAN ANALYSTS?

AA: Any part of investing where you have large amounts of publicly available data will be automated. Whether that’s going to be driven by a higher alpha from quantitative systems or whether it’s going to be driven by cost pressures at the industry level, I see automation coming. Automation is expanding from the two ends of the value-add spectrum in investing. – It is coming from the low- level end – passive, smart beta, and factor models, where quantitative systems have become the norm in recent years. And it is coming from the high- level end, with strategies exhibiting very high Sharpe ratios and uncorrelated return streams. If you look at the risk/ return profile of the top quantitative products in the world, and what they accomplished over often two decades of investing, they are unbeaten in the investment universe to my knowledge. And this is why the majority of them are closed to outside investors at this point. However, there are two caveats to that. One is data. Any subfield of investing where you lack large amounts of reliable data cannot be automated, and quants typically don’t even try to do that; without reliable data, quants do not see. The other one is the concentrated style of investing, which again is not suited to statistical frameworks and quantitative systems. Quantitative funds make small gains on large numbers of investments. The concentrated style where you have a ten-to-one leverage on one investment hypothesis and then it really works or really doesn’t work, it’s not something that quants do.

AND HOW WOULD YOU DESCRIBE THE EVOLUTION OF THE QUANT SPACE?

AA: It’s a fast-growing field that’s still in its infancy. I meet our peers all over the world and so many times you’d meet people who have been building firms for 20 years now with amazing academic reputations and they tell you, “Every week we still learn something new.” In the quant space we all feel that we’re still in the early days of quantitative investing. And what a lot of people don’t realize is that quantitative firms have come up with a whole new way of building an investment firm from the ground up. When you visit these quantitative firms, they are more akin to technology firms than to traditional investment firms. They’ve reinvented everything: the way they source the data, the way they do pre-trade compliance, post-trade compliance, risk management. It’s a whole new way of approaching investment, much leaner and in some ways much healthier. Many of these firms are bringing real teamwork to investing. The hedge fund space used to be dominated by the star manager model. When you look at quantitative firms, it’s completely different: even if you wanted to measure someone’s impact specifically, it would be very difficult because it’s all teamwork and incremental improvements every single day.

DO DATA SCIENTISTS NEED A FINANCE BACKGROUND TO SUCCEED AT AN INVESTMENT FIRM?
AA: Many quantitative firms have a bias for hiring people entirely from outside of finance. You hire scientists and engineers, with diverse experiences in machine learning, astrophysics, healthcare. Working with data is what they all have in common. They never worked in investing before, and once you hire them, they need to understand the problem set and the domain knowledge in finance, this is part of the challenge, of course. But once they do, it is amazing how quickly they start thriving with financial data.

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Investing, fast and slow – Part 2: Investment for Data Scientists 101 https://dataconomy.ru/2017/04/12/investing-fast-slow-investment-data-scientists-101/ https://dataconomy.ru/2017/04/12/investing-fast-slow-investment-data-scientists-101/#respond Wed, 12 Apr 2017 07:30:59 +0000 https://dataconomy.ru/?p=17677 Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down to talk with their founders […]]]>

Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down to talk with their founders about investing, data, and the challenges of starting up. Part 1, my interview with Guillaume Vidal, co-founder and CEO of Walnut Algorithms ran last week. Below is my talk with Dr. Bernd Hanke, co-founder and co-Chief Investment Officer of Global Systematic Investors.

What is the origin of Global Systematic Investors?

Bernd Hanke: It came from all of our backgrounds. I did a PhD in finance and then worked for two systematic asset managers. In other words, managers who use systematic factors in order to do individual stock selection, quantitatively rather than using human judgment. Obviously, human judgment goes into the model when you select factors to forecast stock returns, but once you’ve built your model, the human element is reduced to a necessary minimum in order to try to remain disciplined. So that was my background. Both of my partners used to be in a portfolio management role at Dimensional Fund Advisors and one of them has always been very research-oriented. They both come from the same mindset, the same type of background, which is using systematic factors in order to forecast asset returns, in our case, stock returns.  

How has your strategy evolved over time and how do you expect it to evolve in the future?

BH:  We’ve worked on the strategy for quite some time, building the model, selecting the factors, working on the portfolio construction, on basically how you capture the systematic factors in an optimal, risk-controlled manner that is robust and makes intuitive sense. We developed the model over several years and we will keep enhancing the model as we continue to do more research. We are not making large changes frequently, but we gradually improve the model all the time, as new academic research becomes available, as we try to enhance some of these academic ideas, and as we do our own research.

There is a commonly held view that in today’s markets, investment strategies are increasingly short-lived, and so they stop working quickly. You don’t share this view?

BH:  We are using a very low frequency model, so the factors we are using have a fairly long payoff horizon. I think when you talk about factors having a relatively short half-life in terms of usability, that is mostly true for higher frequency factors. If you back-test them, they sometimes look like there’s almost no risk associated, just a very high return, and then obviously as soon as people find out about these factors that almost look too good to be true, the effects can go away very quickly. Instead, we are looking at longer-term factors with a payoff horizon of several months or sometimes even a year. We recognize that there’s risk associated with these factors, but they have been shown to be working over long periods of time. In the US you can go back to the 1920’s studying these factors because the data is available. In other regions, there’s less data, but you have consistent findings. So as long as you are prepared to bear the risk and you diversify across these long-term factors, they can be exploited over long periods of time.

What kind of long-term factors are we talking about?

Our process is based on a value and a diversification component.  When people hear “value”, they usually think about a book-to-price ratio. That’s probably the most well-known value factor. Thousands of academics have found that the value effect exists and it does persist over time. It has its drawdowns, of course, the tech bubble being one of them, and value actually worked very poorly, but then value came back strongly after the tech bubble had burst. We’ve broadened the definition of value. We also use cash flow and earnings-related factors, and we are using a factor related to net cash distributions that firms make to shareholders.

We are also using a diversification factor. We are targeting a portfolio that is more diversified across company sizes and across sectors than a market weighted index.

And the advantage of being more diversified is lower volatility?

BH:  Not necessarily. Stock-level diversification actually increases volatility because you’re capturing a size effect. You’re investing in smaller companies than a market-weighted index would. But smaller companies are more risky than larger companies. So if you tilt more towards smaller stocks you actually increase the risk, but you also increase returns. On the sector side, the picture is quite different. By diversifying more across sectors than the market-weighted index does, you get both lower risk and higher returns.   

Does the fact that your factors are longer-term and riskier mean that it could take you longer to convince an outside observer that your strategy is working?

BH:  Yeah, that’s true. That’s one of the luxuries that high frequency funds have given that their factors have such a short payoff horizon. They only need relatively short periods of live performance in order to demonstrate that the model works, whereas someone who uses a lower frequency model needs a longer period to evaluate those factors.

So what are the benefits of going with such a slow-trading strategy compared to a fast-trading strategy?

BH:  One big advantage is of course that these long-term factors have a much higher capacity in terms of assets that you are able to manage with these factors. It is more robust, in the sense that even if liquidity decreased and transaction costs increased, it wouldn’t really hurt the performance of that fund very much because the turnover is so low. Whereas for high-turnover, short-term strategies, transaction costs and liquidity are obviously key, and even slight changes in the liquidity environment of the market can completely destroy the performance of these strategies. Another advantage related to that is that with lower frequency factors you can also go into small capitalization stocks more. You can tilt more towards small cap because you’re not incurring much turnover even though small cap is more costly to trade. And in small cap there are often more return opportunities than in large cap, presumably because small cap stocks are less efficiently priced than large cap stocks.  

Once you settled on your investment strategy, was it obvious to you how you would monetize it, that you would go for the fund structure that you have today?

BH:  The fund we have now is a UCITS fund. We were looking at different legal structures that one could have. It also depends a little bit on who you want to approach as a client or who you might be in contact with as a potential client. If you’re talking to a very large client for example, they might not even want a fund. They might want a separate account or they may have an existing account already and then they appoint you as the portfolio manager for that account. So then the client basically determines the structure of the fund. If it’s a commingled fund as ours, then there are a couple of options available. Some are probably more appealing to just UK investors and some are more global in nature. The UCITS structure is fairly global in nature. It tends to work for most investors except for US investors who have their own structures that differ from UCITS.

What would be your advice to people who think they have a successful investment strategy and are thinking about setting up their own fund?

BH: Well, my advice would be, find an investor first. Ideally, a mix of investors. So if one investor backs out, then you have someone else to put in. That’s obviously easier said than done. But I think that this is quite important.  

How dependent is your strategy on getting timely and accurate data?

BH: For us, timeliness is not as crucial as for high frequency strategies. Obviously, we want to have the latest information as soon as possible, but if there was a day or perhaps even a week delay in some information coming in, it wouldn’t kill our strategy.  

But data accuracy is very important. Current data that we get is usually quite accurate. The same cannot necessarily be said about the historical data that we use in back tests. In the US, data is fairly clean, but not for some other countries. All of the major data vendors claim that there is no survivorship bias in the data. But it’s hard to check, and accuracy is often somewhat questionable for some of the non-US data sources in particular. We’re not managing any emerging markets funds, but even in developed markets going back, there tend to be many problems even for standard data types such as market data and accounting data.

And the data sources that you are using now are mostly standard accounting data?

BH:  Yes. There are some adjustments that we could make and that we would like to make. For example, one fairly obvious adjustment would be to use more sector-specific data. If you are just thinking about a simple value factor which some people measure as book-to-price, it’s basically looking at the accounting value of a company relative to the market value of the company. You could call the accounting value the intrinsic value of the company. You could measure that differently for different industries. For example, if you think about the oil and gas industry, you might want to look at the reserves that these companies have in the ground rather than just using a standard book value. For metals and mining companies, you could do something similar. Other industries also use other sector-specific data items that could be relevant for investors. Most accounting data sources now incorporate quite a lot of sector-specific data items. One issue is that the history is usually not very long. So if you want to run a long back test using sector-specific data, that is usually not feasible because that type of data has typically only been collected over the last few years.

What role do you see for data science and data scientists in investment management now and going forward?

BH: Right now there is a huge demand for data scientists. That, however, is mostly in the hedge fund area. It is much less for long-only funds. We are managing a long-only fund. There are some quantitative asset managers, that manage both long-only funds and hedge funds, and they might be using a similar investment process for both. So these managers may hire data scientists even to work on the long-only portfolios, but it’s mostly systematic hedge funds and it’s mostly the higher frequency hedge funds. Different people refer to “high frequency” in very different ways, but what I would call “high frequency” would be factors with a payoff horizon of at most a couple of days, maybe even intraday factors. So those types of hedge funds seem to be the ones hiring the most data scientists at the moment.  Also, new service providers keep popping up that employ data scientists and they then sell services to hedge funds, such as trading strategies or new types of data sets.

How valuable are these non-standard or “alternative” data sources?

BH:  The data is there and we now have the computational power to exploit it. So I think it will become more useful, but it’s a gradual process. Everybody talks about big data, but I think right now only a small minority of funds have successfully employed non-standard or unstructured data sources (commonly labeled “Big Data”) in their strategies in a meaningful manner. For some types of non-standard data, I think there there’s an obvious case for using it. For example, credit card payment data can help you see whether there are particular trends that some companies might be benefitting from in the future, or looking at the structure of the sales and trying to use that in forecasting, and so on. And there are other data types where it’s probably more doubtful whether the data is useful or not. There is some tendency at the moment, I think, to be over-enthusiastic in the industry about new data without necessarily thinking carefully enough about formulating the right questions to investigate using the data and doing thoughtful data analysis.

Where do you see investing heading, in terms of passive versus active strategies?

BH:  One trend is away from traditional active. Most institutional investors have come to the conclusion that traditional fundamental active long-only managers have underperformed. So, many institutional investors have moved to passive for their long-only allocation, or if not passive, then to what is often referred to as “semi-passive” or “smart beta” strategies. These are mostly one-factor strategies, where the assets, often in an ETF, are managed according to one factor such as a value factor. For example, fundamental indexing uses a value factor composite and that is the only factor. There are other strategies, such as minimum risk and momentum. Everything that is not a market weighted strategy is active, strictly speaking, but often investors refer to strategies that use fixed rules that are made publicly available to every investor as semi-passive.

And then at the other end of the spectrum, you have hedge funds, and it used to be the case that systematic or quantitative fund managers, both long-only as well as long/short managers, mostly used similar factors. That became very apparent in August 2007 during the “quant liquidity crunch”. Basically what happened was that most quantitative investors were betting on the same or very similar factors, and once more and more quant investors had to liquidate their positions, that caused the factors to move against them in an extreme manner. So most quant factors had huge drawdowns at the beginning of August 2007. Then after 2007-2008, hedge funds attempted to move away from these standard factors to more proprietary factors as well as to non-standard data sources, and at the same time more and more data became available. I think systematic strategies used by many hedge funds now are actually more different than they used to be in 2007. However, the opposite might be true for many smart beta strategies. So, hedge funds are often trying to limit their portfolios’ exposures to standard factors used by the smart beta industry. Whether they are able to do this successfully remains to be seen. If there is going to be another quant crisis, that might be the acid test.

So that’s been a fairly significant change over the last 10 years.  If you had a crystal ball, what would be your prediction of how things will be different 10 years from now?

BH:  One prediction I would make is that smart beta is not going to remain as simplistic as it often is at the moment. Most likely, it will be developed into something that we had before 2007 in quant strategies. People will probably combine fairly well-known smart beta factors like value, momentum, low risk into multi-factor strategies rather than offering them separately for each factor and so that then investors have to combine the strategies themselves to diversify across factors. It is more efficient if the investment manager combines factors at the portfolio level because these factors, to the extent that they have low correlation, often partially offset each other. This means that trades based on different factors can be netted against each other and this saves trading costs. That is happening to some degree already. Several managers have started offering multi-factor smart beta portfolios.

On the hedge fund side, I think the prediction is going to be more difficult. It remains to be seen how successful artificial intelligence and machine learning strategies turn out to be, and it also remains to be seen to what extent new data types are exploitable in terms of predicting subsequent stock returns and risk. My suspicion is that there are going to be many disappointments. Some new data types will be worthwhile but many probably won’t be. Similarly for machine learning and artificial intelligence. It is likely that only a small subset of today’s tools turn out to be useful.   

Do you see fintech companies making headway in investment management, either as asset managers or as suppliers to the industry?

BH:  Oh, definitely, on all sides. Robo-advisors being one of the big ones, I guess, that could change a lot how the asset management industry operates. And it’s in all areas, also other service providers, portfolio analytics providers and so on. There’s a lot of development in this area currently, which is probably a good thing. In terms of data vendors, for example, there is still a strong oligopoly consisting of Thomson Reuters, FactSet, Bloomberg and S&P who sometimes charge inflated prices for their data. And the data often isn’t particularly clean. Even worse are some of the index providers like MSCI, FTSE and S&P. They are offering very simple data at exorbitant prices. They are not really charging clients for the data. Instead they are charging them for usage of their brand name, for example, for the right to use the MSCI name in their marketing material. Now there are more and more fintech companies that are offering the same service, except for the brand name, at much lower cost to the client.

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Investing, fast and slow – Part 1: The Present and the Future of AI in Investment https://dataconomy.ru/2017/04/05/investing-fast-slow-ai-investment/ https://dataconomy.ru/2017/04/05/investing-fast-slow-ai-investment/#respond Wed, 05 Apr 2017 07:30:58 +0000 https://dataconomy.ru/?p=17673 Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down to talk with their founders […]]]>

Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down to talk with their founders about investing, data, and the challenges of starting up. Below is my talk with Guillaume Vidal, co-founder and CEO of Walnut Algorithms. Stop by next week for Part 2, my interview with Dr. Bernd Hanke, co-founder and co-Chief Investment Officer of Global Systematic Investors.

Why did you call the company “Walnut Algorithms”?

Guillaume Vidal:  Because walnuts look like small brains, and from a startup perspective, it was fun, like Apple and Blackberry. It also shows that we were a bit like a walnut tree created out of intelligent algorithms and we felt that it’s important to put “algorithms” on the back of that. So we thought that mixing “walnut” and “algorithms” was fun and it was a good image.

And how would you summarize what you do?

GV: We apply the latest advances in artificial intelligence to systematic investment strategies.

Was that the idea from the beginning, or did you pivot at some point?

GV: I think it came quite naturally. The six co-founders’ backgrounds in artificial intelligence, investment management and finance made us think there’s definitely something to do there. We looked at a lot of AI startups and many of them wonder what they should be doing with AI, as we realized. One of the best AI startups in France is called Snips and even they had a hard time coming up with a product. We focused from the outset on financial services and investment management and that for us was very amenable to AI. We did take a bit of time to find the right business model, which for us now is actually managing capital and advising capital depending on the regulatory definition. But in the beginning it was really a bit naïve saying, “okay, we want to apply AI”. We want to do what DeepMind did with their reinforcement learning or AlphaGo. They are incredibly powerful algorithms and we want to apply that to investment management, and these are algorithms that are four, five years old at most. They are made possible by improvement not only in the AI but also in access to data, coding languages with the right libraries, as well as computing power via Google Cloud or Amazon cloud. It’s a sort of a mix of things that allows this. I would say the most difficult, maybe the luckiest part for us was to be able to have that combination of skills. I think that the biggest barrier to entry is actually that combination of AI, computer science, quantitative finance and business skills.

What financial instruments do you focus on?

GV:  We focus on liquid equity index futures in the US and Europe, because we need both liquidity and low trading costs, you can go long or short without extra cost.

How did you look for your business model?

GV: We started by saying that AI for finance works. It will work. There is no doubt about that. The question is, who is going to make it work? And it will be very, very valuable. If it’s just research, if it’s just one big sale kind of product or if it’s research fees from selling into hedge funds, or you partner with a hedge fund, would you be absorbed by a hedge fund, would you provide signals, would you do consulting work, would you create your own hedge fund, all of those were potential business models that we looked at. When we applied to Startup Bootcamp and when we went through the selection stages we were actually telling them that we didn’t really have a business model and they were fine with it.  Now we are starting with separate managed accounts. This is quite standard in finance. A lot of CTAs do that. The fund structure might be something at a later stage, as it involves heavier compliance and regulatory issues, and is costlier and time consuming.

So what is innovative about generating trading signals with machine learning?

GV: Traditional systematic strategies are rule-based. A systematic strategy that you would code, for example, on Quantopian, you would say, “oh, if these three moving averages cross and I’ll have my yearly pivot at this level, or if my relative strength index is above a particular threshold, then I buy or sell”. And these are fixed rules. What we’re building, is a machine that does not have fixed rules,they are more flexible. A machine learning algo can continuously evolve and actually look at market configurations, classifying buy or sell signals with confidence levels. It’s a bit like a trading floor where you have a number of traders, and in our case it’s a number of robo-traders which are individual AI algorithms, and we have a portfolio manager which is the cash allocator which uses those underlying signals provided by the different AI algos and optimizes the capital to allocate to those individual signals based on the risk constraints and the exposition constraints, long and short and per instrument, per geography et cetera.

It seems that your clients have to be sophisticated enough to appreciate what you do but not so sophisticated that they can do it themselves.

GV: There’s more than 80,000 funds worldwide. Of course a portion of that is interested in it and the people we talk to are even hedge funds themselves. But sometimes they just have a global macro strategy or a credit strategy or some other form of non-systematic strategies. I would say that internal quant teams sometimes are not necessarily staffed enough to do what we’ve been doing.

We coded everything from A to Z with 12, now reaching 15 people soon, all scientists, and we have to code the entire infrastructure and we have to do research, we have to do all of that. A number of those more traditional funds, sometimes they will hire one PhD and say, “let’s let him work on one problem and let’s let him try to enhance one of our systems with machine learning”. It doesn’t necessarily work because maybe you need a collaborative and creative culture, often found in startups, rather than just having one PhD doing some data science on the side working in collaboration with one of their quants. We really work in a tight group, brainstorming all the time, bringing computer scientists, mathematicians, AI scientists, all these skills together to think what actually works, what should work, how should we code this, how should we design this. It requires an innovation mindset.

Established hedge funds have been running with their own systems for 20 years maybe and they have their strategies, long-term systematic or long-term trend-following or whatever, and coming up with something completely new, hiring new people, bringing in internal research in-house is difficult. Some try it. I would say the most sophisticated succeed and these are hedge funds like Renaissance Technology, Two Sigma, Winton. It’s very opaque, we don’t necessarily know exactly who’s doing what, but probably they have some of that.

And these hedge funds’ algorithms will interact with yours in the markets. Do you have a line of defense against that?

GV:  I think there are two main things. One is that for now we’re a lot smaller, and we don’t necessarily focus on the same asset classes. The larger ones have to be in very deep, very liquid markets. These funds have very different investment strategies on multiple timeframes so they can invest from high frequency to yearly trend following, very probably. When you have 60 billion under management, you have no choice but to actually scale to every asset. As we have very minimal assets under management to begin with, we create intraday strategies on specific assets.

The second part is this. When you look at all the systematic trend following CTAs, they typically have an 80 to 90 percent correlation, because they’d be following the same trends on the same weekly and monthly basis. When you start using more complex machine learning strategies, there are many, many ways to actually do machine learning. And we think in modules, so we have our data gathering, data cleaning, feature engineering, entry points, exit policies, we have allocation, and we have market impact – and all these for us are machine learning enhanceable, and machine learning automatable, and there are so many ways to do it, so you end up with a very different system than they have. We come up with some new ways of investing, some signals that we come up with are not the signals that everyone has. It’s not a golden goose. It’s not like you created a machine that just makes money. It has a risk adjusted return, it has drawdowns, it has inherent risk, but from the portfolio management strategy, it does outperform some of the other absolute return strategies, and it is uncorrelated to them. That’s the part that people are interested in.

Do you worry about overfitting your models, so they work for the time periods you used for model development, but not afterward?

GV:  Trying to minimize overfitting as much as possible is really at the core of what we do. There are many ways. First of all, there is data dimensionality, so this is why we are intraday, and we try to have as many data points as possible. When we do our classification, we try to minimize feature vectors so that’s really about trying to reduce the input dimension, and using human expert knowledge is important in that regard. We also do a lot of robustness tests, we designed robustness modules. And we paper trade as well, before it goes live. But there’s always overfitting in a sense.  Because you use historical data and you fit your models on historical data, overfitting is there. Some is useful as you have to make sure the algos are actually fitted to the current market regime, but they have to generalise.

Do your algorithms recognize when the regime has changed or do you need humans for that?

GV:  Yes, we automate that. We try very hard to automate that at multiple levels of the decision making, in the allocation portion, in the entry signal portion. So maybe the underlying algo itself understands that the market has changed and gives us higher or lower confidence on its signal. But on the allocation as well, maybe you say, “that particular algo sent me that particular signal but I’m going to discard it because they are not in the right regime”. So at multiple levels we can actually take into account regime changes. There is no human intervention unless there’s something very critical, a big financial crisis or a big flash-crash, and we might decide, the algo probably won’t work right now and we should shut everything down.

Do you see investment management becoming dominated by AI in the future?

GV:  It’s difficult to see the future, but portfolio managers or heads of hedge funds will probably switch from being traders, economists, business guys into data scientists, mathematicians, into people who are capable of using data, understanding data and managing teams of scientists and teams of engineers. Since AI is becoming more accessible, data is becoming more accessible, computing power is becoming more accessible, you’re probably going to have firms like us coming and disrupting the larger hedge funds out there and they will have to, in a sense, defend their position against those players, or buy them out, or find a way to innovate themselves, because currently they are not really doing it.

Do you think that somewhere down the road, AI for investments could become commoditized?

GV:  AI is not automatic, AI is not a monolith. It’s not one big “I do AI”. I don’t see it becoming something completely commoditized. It’s not like “I have an AI algo and I’ll plug it into data and then it works.” It’s a lot more complicated than that. You have to do a lot of feature engineering, you have to have trading experience, market experience, there are many different parameters and many different ways of doing it. Maybe you’ll have some form of commoditization, for example Quantopian managed to commoditize in a sense the way of writing a systematic algorithm in a platform and it has attracted a lot of people. But maybe someone who uses a different platform will have an edge over people who are all using the same platform with access to the same features and the same data.

This brings us back to the ideal team composition for AI trading.  

GV: You need people with trading experience, data scientists, computer scientists. The infrastructure, code optimization, the execution, for all that you need strong IT people. Data science and AI is more or less the same for us, but there is a difference between an AI practitioner and an AI researcher. So a data scientist knows how to code, how to use machine learning libraries, but a researcher can understand the real underlying principles of a neural network and maybe he will work on getting a better cost function and these kinds of things that are not a data scientist’s job.

And what happens when these people with their different backgrounds disagree on how to move forward?

GV:  That’s huge. I think that’s what makes us what we are, having a team of people who are open minded and capable of just debating all day long, and the best idea wins. It’s creativity management. It’s trying to get all these people to disagree in the beginning and agree in the end. And also to agree on what to prioritize, because we always have a pipeline of ideas that could take a thousand people a hundred years to implement, but we have to decide, what’s the low-hanging fruit? What can we do right now to improve the results as much as possible?  And then you also have the more technical guys who say, “okay, I can code this”, or “it’s too long to code this”, or “how should we code this?”

How do you feel about non-traditional data sources, big data?

GV:  We make a distinction between AI and big data, and people tend not to. AI is a way to let you make sense of big data. But we focus on the improvements that AI made.  When, for example, Google came up with AlphaGo or the Atari games, these are really algorithmic improvements. It’s small data sets or fairly limited data sets, but the improvement is really in the AI itself. We focus on strong AI rather than on alternative data sources. One of the reasons is data dimensionality issues that I mentioned. We’re looking for statistically robust strategies.

There is a lot of demand for data scientists in other industries. How do you attract data scientists to work in finance?

GV:  First of all we market ourselves as a technology company, and all the successful firms and funds that do that, do the same. If you look at the marketing of Two Sigma, Winton, or Renaissance Tech, they really say “we are a technology company, a research company, that happens to be trading”, and this is very important to attract the right people. If you are just another hedge fund, mainly because of the crisis and because of the reputation of the hedge fund industry, people don’t really want to work there. But the work in-house is actually quite interesting. You’re working on very complex datasets. You’re researching, and there’s a very straightforward application. The results are right there, black and white. When you optimize code, do some data science on new data sets, new strategy, new markets, new instruments and do that work day to day, it’s actually quite interesting, maybe even more interesting than doing that in a media company.  On the long-term perspective, let’s say five to ten years’ vision, we would like to expand to other areas. Renaissance Tech, which is a New York-based hedge fund, is considered one of the best theoretical physics labs in the world, and similarly we would like Walnut to be one of the best AI labs in the world.

 

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“The future of humanity is to direct one’s own evolution” – An Interview with Amal Graafstra https://dataconomy.ru/2016/12/09/amal-graafstra-biohacking-dangerous-things/ https://dataconomy.ru/2016/12/09/amal-graafstra-biohacking-dangerous-things/#respond Fri, 09 Dec 2016 09:00:19 +0000 https://dataconomy.ru/?p=16982 Amal Graafstra is a double RFID implantee, author of the book RFID Toys, and TEDx speaker. His interest in biohacking, RFID, and NFC began in 2005 as a simple solution to a problem. He wanted easy access to his office. He explored biometric options only to find they were too expensive, klunky, and vulnerable to […]]]>

amal-graafstraAmal Graafstra is a double RFID implantee, author of the book RFID Toys, and TEDx speaker. His interest in biohacking, RFID, and NFC began in 2005 as a simple solution to a problem. He wanted easy access to his office. He explored biometric options only to find they were too expensive, klunky, and vulnerable to vandalism. Graafstra has been active in the DIY RFID implant scene since the mid-2000s. His custom gadgetry company Dangerous Things’ mission states that “biohacking is the forefront of a new kind of evolution.”


Why did you start Dangerous Things?

In 2005 I was really frustrated with my key situation. I was really upset that I had to carry all these keys around and it seemed pretty lame, I wanted the door to just know that it was me, I didn’t want to use some kind of iris scanner, fingerprinting, I looked at these technologies and they seemed robust particularly around door solutions, you know you have a sensor like that, it’s pretty expensive and you put it outside and a kid whacks it with a stick…it doesn’t work.

RFID technology seems to be the logical solution but you’re exchanging a metal key for a plastic card, the promise of RFID is that you have one card that works with all your doors but in reality access control manufacturers want to build-in proprietariness so you have to buy tags from them so you could have something that technically works for all doors but then you end up with a bunch of tags, like a key ring with keys so it’s so stupid. So I’m like you know what, I’m gonna build my own and I’m gonna get an implant. Then I don’t have to worry about carrying a card. So I did some research I looked into it and decided not to get a pet implant for a couple of reasons and then did find the right kind of chip that I wanted, and the right kind of glass and talked to some doctors who were clients of mine, I said ‘hey what about putting that in here?’ And they were like ‘yeah that’s cool’. So I bought the tags, in 5 minutes it was in, done deal.

That was pretty much it but then in 2008, 2009 maker revolutionary hit, people started making their own electronics again, and really getting into it and RFIDs are a big part of hobby electronics and a lot of people got interested in implants and it was too difficult to manage without a structure around it so I started dangerous things and now we sell safe materials and we partner with professional body piercers to allow our customers to go there and get a safe implant.

Do you come from an engineering background?

No. I like technology. I can code but not well, I can build hardware but not well. I know a little bit about business but not much. I’m a jack of all trades kind of person.

So this chip, you provide the hardware and then other people can provide the software?

You can build a lock mechanism pretty easily or you can buy one, Samsung makes a door lock and a bunch of other companies make door locks, but then if you want to get into something a little more complex like starting your car or something like that you have to know a little bit of electronics to modify the car. When it comes to stuff like payments it’s more of a partnering and people issue than a technical one. You’d have to have the right permissions, the right security on your devices etc.

What about the core mission of the company?

We haven’t really developed a core mission statement, but it really comes down to the idea of upgrading your body directly, either by hardware installs or tinkering with the genes. That kind of thing is really the next form of evolution and we believe that the future of humanity is to direct one’s own evolution. Evolution is two things, it’s random mutation and selection. Random mutation 99.99% of the time is bad. A baby get born with some defect and the baby dies. That’s selection. But we’ve been acting out on that selective process and surgically save the baby’s life. However we haven’t changed the problem which is the bad gene, so being able to guide evolution in a non random way is really what’s interesting there.

Do these devices collect data?

The devices are not connected, actually the only time they are even powered is when they’re in the range of the reader. So there’s no data collected. In the future when we’ll have a safe power storage cell then we can talk about collecting bio-data and aggregating that data, doing more things with them. But for now it’s more like an information exchange. For example we have a tag that has a thermal sensor on it, so you can get the temperature but it only records temperature when you’re asking it for temperature.

Are there challenges about the product?

There’s no moving parts, there’s no reason to think they will break. I have this for eleven years. So they are different limits I guess. Some models have a memory of ten year data protection. So if you don’t write anything, the device will keep the data for 10 years but if you write something it’ll be reset at that time. There’s also a 100.000 cycle count on the memory blocks itselves so you can write to that memory 100.000 times. So even if you write once a day, every day it’ll give you around 27.9 years.

What do you think are our biggest digital problems?

Right now we have a problem. Our digital identities are becoming more valuable, even more valuable than our biological identities. You can’t go into the bank and get in your account without some kind of an identity system to identify you. Your body is not sufficient enough. So to be able to link online and offline identities is a big problem and it’s gonna get bigger as we rely more and more on online communications. That’s one of the primary focuses developing those devices.

Do you see this technology as a destructive force as well as a constructive one?

People are random odd things so there’s going to be chaos in any kind of endeavour. My hope is being able to unequivocally prove your identity in digital and online interactions is only going to be beneficial. Any kind of change if you look back through recorded history makes people afraid. So there’s always a period of acceptance, the same was true with the pacemakers.

The first pacemakers were condemned as the devil’s work. And slowly people started to accept it through understanding of what it’s doing and why it’s important. It’s easier for medical technologies to get acceptance because they are helping in a way that they seem to be critical and urgent. I think once we have a safe power source cell then we can start making all kinds of crazy devices. People have wanted bioelectric devices to monitor muscle movement, for example to activate a prosthetic, but getting a good sensory reading from outside the skin is very difficult. However being able to have an implanted device that can listen to muscle movement is very interesting. So that’s just one example. So there’s gonna be medical devices but also non medical ones, gesture controls for example. It’s that kind of capability that we’re gonna see through biohacking devices.

What kind of shifts do you see in biohacking in the future?

Again, once we have an active power cell and are able to have devices that are powered within the body, then data collection becomes possible and contributing to big data stream is going to be one of those possibilities. Wearables are interesting but a wearable is a burden to manage. They are inconsistent in collecting data because people don’t wear them all the time. So being able to establish a data connection routine through an implant device that’s always listening and always recording has more interesting opportunities for big data.

 

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“Technology can free people from wage slavery” – An Interview with Xavier Damman, Open Collective https://dataconomy.ru/2016/12/05/xavier-damman-open-collective/ https://dataconomy.ru/2016/12/05/xavier-damman-open-collective/#respond Mon, 05 Dec 2016 08:00:11 +0000 https://dataconomy.ru/?p=16968 Xavier Dammanis is an entrepreneur working on a new form of association that enables the Internet generation to fund communities in full transparency: Open Collective. He founded Tribal in 1999, a site that gathered student content from around Belgium that was published in a 30,000-circulation magazine distributed nationwide to high schools. He built content partnerships with […]]]>

xavierXavier Dammanis is an entrepreneur working on a new form of association that enables the Internet generation to fund communities in full transparency: Open Collective. He founded Tribal in 1999, a site that gathered student content from around Belgium that was published in a 30,000-circulation magazine distributed nationwide to high schools. He built content partnerships with leading brands such as Microsoft and other Belgian companies. He moved from Belgium in the summer of 2009 with the vision that there are voices on social media that deserve to be published on main stream media. This vision is the foundation of Storify, the largest social media curation platform used by top publishers, brands and organizations around the world which was acquired by Adobe in 2016.  Xavier earned a Master’s Degree with Distinction in Computer Science from Belgium’s Leuven University, and is one of MIT’s 2016 ‘Innovators under 35’.


What was your first experience with social projects?

I left Belgium seven years ago to go to San Francisco, because I couldn’t raise money for my first startup, Storify. After a while I raised enough money and made Storify what it became, which we then sold (three years ago) and after that I went back to Belgium. I wanted to pay it forward and help communities and entrepreneurs trying to build startups, so a friend of mine and I came up with this movement to start a manifesto in Belgium. The goal was to come up with recommendations for the government and society at large, including journalists, teachers and parents, to basically make it suck less for startups in Belgium. And it was a great movement indeed. Thousands of people started supporting it and it got a lot of media coverage.

How did that drive you to starting Open Collective?

At some point we wanted to print some stickers to distribute at startup events. They cost a few hundred of Euros so we wanted to create a Stripe account to raise money from the supporters. But in order to do that you have to create a legal entity. And the last thing we wanted to do is to create a legal entity for what we were doing. And this was so frustrating. They were literally thousands of people who share the mission, who would love to contribute, and the money is there and we cannot take it. And we could have used that money to have a larger impact. So we said ‘alright, this is broken and we need to fix this’.

So the internet so far is helping a lot of movements to exist but they cannot have economic power. Occupy Wall street is a good example of that compared to Podemos in Spain. The difference between them is that Podemos was able to turn the energy of the movement to a political party. To convince 85.000 to give them five dollars a month. And that makes the difference between a movement that’s gonna be ephemeral to a more established institution.

What is your goal with Open Collective?

Our goal as Open Collective is to create this new light type of association for our generation who really love doing those side projects, creating those meetup groups, taking the initiative to create a conference located in your city, do open source projects together, create movements like occupy Wall Street, Black Lives Matter, all of that. There’s a whole bunch of things that our generation is doing but we don’t have any platform to enable those movements and these communities to collect money. So it’s all about finding a new way, fund those communities that can have a larger impact.

What does ‘Open Source’ mean to you?

Open source happens to be one of those communities where you could have much more impact if they were better funded. I’m a big fan of open source myself and we open source everything. Open Collective is an open source platform because we believe in open source. We believe it’s the future of work. There’s no reason for having two different engineers in two different parts of the world solving the same problem. And also open source is the right business decision, I’m an engineer, as well as a developer and we tend to make much better code if we know that other people can look at it.

How do you see the future of technology in five years from now?

The future of technology in five years from now looks great. In the past 15 years technology has evolved so much. And now I think the biggest opportunities are applying all the things we learned to auto industries in addition to applying the philosophy of open source to those industries. That’s going be the next big thing in ten years. Technology for me is about money, it’s a means to an end, and it’s about how technology can free people from wage slavery. Because today there are still the vast majority of people who are doing things they’re not passionable about, doing it only because they need money to live. If I could solve one big problem with technology today that would be cancer. Technology for me is giving the tools to people so that can pursue what they truly love.

What does is it mean to be a data native?

To be a data native to me is more about building a fair world, an equal world, and the only way we can do that in a scalable way is through data.

 

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“I look forward to working on a Sunday evening” – Scala development with Tobias Johansson https://dataconomy.ru/2016/12/04/valo-tobias-johansson/ https://dataconomy.ru/2016/12/04/valo-tobias-johansson/#respond Sun, 04 Dec 2016 08:00:30 +0000 https://dataconomy.ru/?p=16933 Tobias Johannson is technical lead developer for Valo.io in London. Tobias has nearly 15 year’s experience developing in .NET and Scala. He has a background in the financial sector as a front-office developer but changed track in 2013 to be part of a team building Valo, a new real-time analytics platform from the ground up. […]]]>

tobbiasjohansson

Tobias Johannson is technical lead developer for Valo.io in London. Tobias has nearly 15 year’s experience developing in .NET and Scala. He has a background in the financial sector as a front-office developer but changed track in 2013 to be part of a team building Valo, a new real-time analytics platform from the ground up. His goal is to outlive the JVM and his tea addiction. His talk ‘Einstürzenden Neudaten: Building an Analytics Engine from Scratch’, at Data Natives Berlin 2016, was his first appearance on the conference scene as a speaker.


Tobias, what is your professional background?

I’m a software developer with nearly 15 years’ experience. The first 10 were as a .NET developer but now I am doing Scala development full time.

When and why did you start learning software development?

At university, and then I was lucky to get a software development job straight after graduating at a Hedge Fund in Madrid. Developing for the financial industry was a great learning ground as it is a very fast moving environment where screw ups could have a direct financial implication. In this environment you learn to build systems in fast iterations without losing focus on correctness and stability.

What is your role at Valo?

I’m technical lead and developer for Valo. We do a distributed analytics system for streams of data. The idea was born out of building P&L and risk systems for banks and hedge funds. It focuses on simplicity from a user perspective without compromising on the analytical powers.

What do you love about your job?

I’m probably one of the few people who look forward to work on a Sunday evening. I love creating things and currently that something is Valo.

You have experience working in the defense industry, in large financial investment banks, in hedge funds, and in startups. What lessons have stuck with you throughout your career?

Each environment teaches you new things, and I always encourage people to try to move around between industries because of that. Investment banks and hedge funds are naturally money driven and it is reflected in the culture and how systems are implemented. People tend to take shortcuts for their own benefit – which is what I dislike the most of those industries.

How has your background influenced your current approach to development?

Many of the developers I work with have the same background as me. In fact, some of us have worked together in the past at different places. This is what creates the foundation of our development team. It is immensely valuable to have a well working core within a team. Without a core it won’t work. This is probably what has been the most important experience in my past career – to find people you like working with and who inspire you.

Which are the fundamental skills that you learned as an aspiring software developer, that have stuck with you throughout your career?

Don’t blame someone else for your own issues. Be humble. Move forward. Have passion.

I feel embarrassed when looking at some code I have done in a rush and it does not look neat or isn’t well tested. It is something which cannot be avoided when you need to move fast but it is always in the back of my head, and whenever I can, I go back to old code and sort it out. Another thing I have realised over the years is that if your unit tests do not look nice (i.e. small and easy to read) then it is most likely to be an issue with the underlying implementation.

What is your favourite technological setup?

Valo of course! But I do miss the .NET platform.

 

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“You can’t stop the device from getting hacked, you have to defend your data” – A Primer with Kevin Mahaffey https://dataconomy.ru/2016/11/28/cybersecurity-kevin-mahaffey-lookout/ https://dataconomy.ru/2016/11/28/cybersecurity-kevin-mahaffey-lookout/#respond Mon, 28 Nov 2016 08:00:41 +0000 https://dataconomy.ru/?p=16882 Kevin Mahaffey is an entrepreneur, investor and engineer with a background in cybersecurity, mobile and machine intelligence. He is CTO and Founder of Lookout, a cybersecurity company dedicated making the world more secure and trustworthy as it becomes more connected, starting with smartphones and tablets. He started building software when he was 8 years old […]]]>

"You can’t stop the device from getting hacked, you have to defend your data" - A Primer with Kevin Mahaffey

Kevin Mahaffey is an entrepreneur, investor and engineer with a background in cybersecurity, mobile and machine intelligence. He is CTO and Founder of Lookout, a cybersecurity company dedicated making the world more secure and trustworthy as it becomes more connected, starting with smartphones and tablets. He started building software when he was 8 years old and it has been a love affair ever since. Mahaffey is a frequent speaker on security, privacy, mobile and other topics.

 


Tell us a little bit about yourself and about Lookout

I am Kevin Mahaffey, I’m the founder and CTO of Lookout. We are a cyber security company focused on mobile.

I like fixing problems. The company started in 2007 and actually myself and the other two co-founders were doing research into mobile phone security and we got our hand on a Nokia 6310i, you know, black and white screen, had snake the game on it, and this phone was notable to us because it had Bluetooth on it. We found actually some pretty bad security vulnerabilities on that device. You could hack into it and reboot it. And we looked on a whole bunch of other devices and we found similar vulnerabilities in almost every phone we ever touched. And we tried to work with all the different manufacturers, everyone from Blackberry to LG to Nokia in this case and nobody really took security very seriously because the question was why would anyone wanna hack a phone? This is in 2004 mind you. And one of the other excuses was, well the range of Bluetooth is only 10 meter so you had to be really close to someone.

And so Bluesniper was created to extend the range of Bluetooth to 1.2 miles away. And in doing so we proved that you could actually hack a phone from really far away. We thought that this is maybe something we’d talk about at some technical security conference but we were surprised to be on the front page of the business section of the Wall street journal of the NY Times, so we thought “this is a big problem that we can solve”. So we said ok lets start a company to solve the problem and in 2007 we started Lookout to build software to protect both individuals and businesses from cyber threats on their mobile phone.

What makes you want to hack things?

Hacking is not like we see in the movies. The way a system does work is different than the way it was designed to work. And they surface that. Good hackers, people who want to make things better, when they find a way to manipulate a system in a way that wasn’t intended they try to get it fixed.

Where is the company’s HQ located and why?

We are based in California, San Francisco, and we have offices in Europe and Asia.  The reason we are all over the world is because this is a global problem. Mobile security doesn’t affect only one country but every person on the planet. From individuals who’re using their phones for online purchases to large companies who’re using mobiles to run their businesses to manufacturers.

We started in LA and in 2009 we moved to San Francisco because Google and Apple became big in mobile.

Are you going to stick to phones? Or you have plans for other devices, such as cars?

We don’t have any products in that space, [car security etc], I’m not sure if we will ever have a product for cars but the passion of everyone in the company is [understanding] how to make the world a safer place and sometimes that means releasing a product, sometimes it means doing and publishing research. And if there is a product that is needed, we go build it. IoT security needs to be taken very seriously. However, we are focused on mobile right now. We’re focused on one problem at the time.

Can you also hack an offline network?

Most people are focused on how to secure a network. How to stop bad things from happening. But if you think of your body, your immune system doesn’t work that way. And most networks are architected to assume you can block those things. But nowadays you can’t control what’s in your network anymore. So a lot of companies are getting breached everyday, and usually by someone inside their network, they use some valid credentials to access the data that they shouldn’t, and that’s a really big problem. So we advocate for this concept of the immune system where you gather data, preferably no personal identifiable data to know how things are working, everything from your smartphones to laptops, then you process that data and analyse it for find indicators of a threat and sometimes you can automatically respond, or sometimes you need to escalate to some smart human in a security team to think about it some more and decide what to do. But this is very different than stumbling upon a hack because they take out your internet connection for taking so much data from the company, sometimes that’s how you discover a hack.

What kind of advancements do you see happening in the future for your company and in the world?

So right now a lot of individuals use Lookout. The big course for us right now is helping large companies and governments secure their mobile devices because 3-4 years ago people could get email (if that) on their phones. Basically everything you can do on your PC most organisations started to be able to access from a smartphone or tablet. But the organisations don’t have any idea what’s going on on these devices right now. So we see a lot of demand on that. How to secure these devices. We look at a modern way to stop advanced threats that it’s not just signature based to stop attacks on mobile.

And do you see this happening in general?

Everyone is moving towards data security. Some companies are building their own software and they’re very far down on that road, other companies are just starting to get there. But it’s not the device, it’s the data. You can’t stop the device of getting hacked, you have to defend your data and you have to respond to threats and hope they never happen. Those two principles are really coming forward. Unfortunately it means a lot of organisations have to rip out some things and replace some things but I think it’ll make companies and people more secure because when companies are more secure, as an individual your data will be breached less often.

What are some key hurdles in the industry that you’re experiencing and how do you see data science applications solving this problem?

The hurdle is there’s too much data in security or not enough data. In the case of not enough data, many security organisations apply. You can ask any given system, what is the data that will show the hacker gets in. And if you don’t have data coming from that system, then you never gonna know that a the hacker gets in. Other times you have so much data that it is not very useful and you don’t know what to do with it. So you have to set the security teams that are drowning alerts. They’re so busy that they can’t focus on the really important threats. And what I have to see is machine learning emerging to actually helping with these issues.

First there are organisations stitching together the data. so instead of a bunch of isolated data streams we use the phrase joined-in and analyse it. Joined-in is where you take your source code data and mash it with your vacation data so if an engineer checks in for threat indications that’s actually something you wanna look at. But if you only look at source data you’ll never be able to make that conclusion. And analyse it means to look deeper and extract more information. And then, using machine learning to take that huge volume of information and funnel it down to a simple message which says, okay, here are the things that humans need to look at and here are the things that humans don’t need to look at and we know how to deal with it. We can automate responses, cut the device from the network etc. Ultimately humans can only make so many decisions per hour and we have more and more connected things in the world and so if we try to add those things and do the security the same way we did in the past, we’re gonna lose.

What are the possibilities and benefits of using data science in cyber security?

[Using data science] I think security teams will get more sleep, companies will be more secure, hacked less frequently, and individuals will see their data be more protected.

When did you notice that things started to take off?

When we started the company we were securing windows mobile smartphones. And projections for how many smartphone there will be in 2017 were very few. So when we went to investors they were like ‘oh yeah the smartphone market is not very big one’. And now there’s billion smartphones shipped every year and what changed was iPhone and Android launched and that made smartphones easy and fun to use and then at the same time you had 3G and now 4G networks and made the data connection very fast. And the growth of Android and iPhone helped business to grow because it turns out everyone is using smartphones personally. And more recently they started to use them more for work and we’re using things for data, for shopping and for sensitive business data that attract hackers.

So if you could tackle any technology exists today to solve a challenge which would it be?

I think there’s still a lot of misinformation around machine learning and big data systems, I think a lot of people believe that you can just apply machine learning to data and magic happens and problem solved. It’s not true. Machine learning is something that can be a good classifier can detect anomalies in some cases it’s not just machine learning it’s what we call a cyborg. It’s machines doing one thing and humans doing another and find the right handoffs approach so that they can operate together.

 

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“Before big data and all the buzzwords, it was all data mining” – Kathleen Kennedy, MIT Technology Review https://dataconomy.ru/2016/11/25/kathleen-kennedy-mit-technology-review/ https://dataconomy.ru/2016/11/25/kathleen-kennedy-mit-technology-review/#comments Fri, 25 Nov 2016 08:00:51 +0000 https://dataconomy.ru/?p=16888 Kathleen Kennedy is the president of MIT Technology Review and the MIT Enterprise Forum. MIT technology review is MIT’s media platform and it covers emerging technologies and their impact. Their job is to help the audience understand the world shaped by technology, and obviously data is one of those huge disruptive and ultimately exciting forces […]]]>

kkmitKathleen Kennedy is the president of MIT Technology Review and the MIT Enterprise Forum. MIT technology review is MIT’s media platform and it covers emerging technologies and their impact. Their job is to help the audience understand the world shaped by technology, and obviously data is one of those huge disruptive and ultimately exciting forces that have a big impact on us. In her 16 years at MIT, Kathleen has helped MIT Technology Review to redefine the magazine brand and to achieve success in a rapidly changing market. She has established several new lines of business in the United States as well as in Asia, Europe and Latin America.


Data collection has been around forever, but MIT Technology Review started thinking about it as a real disruptive element in 2001, when it included data mining on its Top 10 technologies. So before big data, before all the buzzwords, it was all data mining. In 2001, they predicted that data and the ability to analyze, generate and harness data for business intelligence and all types of things were going to really change the world. Fast-forward to 2016, and their prediction couldn’t be any more accurate – Try to go anywhere without hearing about big data.

What problems/challenges are you trying to tackle at MIT?

There’s a couple of different levels. Us as a business, as a media organization, we’re trying to grapple with our own data and we’re based at MIT, so we have some advantages and insights of doing it, but still it’s really challenging to organize data, analyze them and act on it in effective ways so as a media organization you probably understand that too, and it’s not free, so there’s so much data but you actually have to think about how you are going do this effectively. Then from a coverage perspective, this is the world that we look at, so we’re looking at the people in our community that are CEOs of large companies and dealing with all the privacy and security issues and then figuring out that balance of being able to utilize the data to its fullest potential while balancing it with privacy and security. I was just in Helsinki, at a European CEO conference and that was a huge topic. In Europe, privacy security is very important more so than the US where they’re a little more open and probably a little bit more relaxed about it but if you go to Asia it’s a different story.

What do you think the future of technology will look like 5 years from now?

I think it’s very difficult to predict what will happen in 2020 or 2030, I mean how do you even predict what’s going to happen in two years from now. I’m here in Germany for part of a road show event that we’ve been doing with Enterprise database which is a company that focuses on open source databases and thinking about how you harness open source as a way to save some money and be able to innovate faster and invest more in other areas around innovation. And then also have flexibility and we’ve been doing panels where we’ve been gathering a bunch of really interesting people to talk about how they are harnessing data, what platforms they are using and and then what they are predicting for the future.

We did a great panel in Boston with the Chief Digital Officer of the city of Boston, the CTO and  Head of Technology for a really big healthcare organization and then an individual that was building a startup that is around data and how to help customers.  So on one side you have healthcare and the CTO that are almost crippled, ‘crippled’ probably is a little bit too extreme of a word but held  back due to regulations tied to privacy and security which are obviously critically important for your personal data to be secured.

These troubles that they are having in terms of  how to innovate, move forward and use data was an interesting conversation. Then you have the public sector with the CDO of Boston and she was really interesting because she was new to the job, she always worked in the private industry so she said going to the public sector was fascinating. The public sector never had much money so they’re always trying to do things cheaply. She said their website hasn’t been updated since 2006 and that didn’t work on mobile platforms so she had a real struggle. She’s a data native and she’s finding all these different ways to put together all that data they have collected as the government. If you think about it back in seventeen hundreds  the government was in charge of the libraries, and basically the way we organise our paper data. You would have these public libraries for the public to visit and have access of data. So she’s starting a program where they try to take that library model that we’ve always had and do that for digital data. And then you had the kid with the startup, another uber digital data native whose bigger struggle was how do I find the talent to help me build this company. So it was very interesting to have those three different perspectives on data. And I thought it illustrated where we stand today.

Ray and Maria Stata Center, MIT
Ray and Maria Stata Center, Massachussets Institute of Technology

Do you think that open source is going to be helpful?

I think open source data and open source platforms are an excellent way to not invest millions on platforms where the licensing fees are really taking up a lot of your budget where you can apply that budget in different ways, hire different types of people that have the skillsets that you need. Secondly, open source was always going to be evolving because everyone’s working on the code so you can really have folks innovate within your organization to make it do what you need it to do.

The other piece that’s really interesting that came out of the talks we’ve been doing is, if you are an open source data shop that has open platforms it’s easier to find talent because programmers work is visible, their work is seen versus if they’re in a closed system they’re sort of drowned in that system. So it’s easier to hire good talent if you’re using open source.

Do you code yourself?

I do not code anymore, I used to, at Technology review when I first started one of my first projects was to start our email newsletter, so I created the whole thing in html and I took a bunch of classes at MIT and I learned how to do it but now I have teams that do stuff like that and I don’t have time to do it myself.

What does it mean to you to be a data native?

I would say that in the mid-2000s I took over our whole commercial business, I took over the sales team and all of that and I actually personally really built out our sales force database system. I love data and I love the ability to slice and dice it and I’m a very data driven person, my husband and I are renovating the house and we’re trying to make all these decisions and I’m like, ‘I don’t have enough data to make this decision’. I am a very logical person and I think all the decisions that I make are very data-driven, with always a little bit of instinct. There’s the art and the science and you have to find a way to balance that. Because you need to follow the data and understand the data but you also need to anticipate where they’re going and if you can’t make decisions based on past data. So it’s thinking about what are your projections for the future.

How do you see Germany fitting in this technological future?

There’s a lot of startups here in Germany. I think that a country like Germany will absolutely be able to scale up in this world and deal with that, it’s a very data-driven. I think Europe in general is really grappling with security and privacy issue and so I think that that needs to get sorted because that will slow innovation down. But I think the startup scene in Berlin is really exciting, next door we have an innovators under 35 competition. So there’s all these young innovators that are absolutely thinking how they can harness data and new technology and really unique and interesting ways to solve big problems.

If you could tackle any problem today (that could be solve with technology) what would that be?

At MIT, we started a new initiative, launched in October 2015, called ‘Solve’. It’s solve.mit.edu, and it’s around how new technologies can solve big problems. And it is about the idea that in the world we solved a lot of the small problems. Those who are left are really complicated and require a new way of thinking about how to solve them.

And we’re looking at four areas. The first is around climate change. If I had to put them in order I’d say saving the planet is probably the most important thing. I mean if that doesn’t get fixed all the other ones wouldn’t exist. The next one is around education and thinking about how education is fundamentally changing and the way we think about education probably in the next 50 years is going to be dramatically different. If you think about it education is probably been the same for decades, centuries almost. And thinking about lifelong learning, thinking about how new technologies can deliver information, do you need actually been sitting in a classroom all the time.

And also how do we get education to everyone. If you think about it a lot of the problems that we have as a world is due to the lack of education. In the US if they’re gonna elect Donald Trump as the president… I’ll leave it at that.

Then the other area is around healthcare and how we are evolving to the point where actually with technology we can do surgery on a cellular level and we can actually sequence someone’s genome, understand where there is a defect problem and edit that out. And that disease can be eradicated. So there is an awesome case to that, but if you look at it from a flipside standpoint, and of course when I was in the European summit, the Europeans said ‘well what if someone gives someone the disease’? So technology can be used for good and bad and we have to think hard about these powers that we have.

And the last one is, how new technologies can be harnessed to help the economic advancement of all people. So what we call that is inclusive innovation and thinking about how things like airbnb allow people to take a resource, their home, that’s not been utilized and monetarily participate in the innovation economy. And really thinking about how we can bring a lot more people into it than just the 1%

 

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“AI is the next step for robots” – A Conversation with Nicolas Boudot https://dataconomy.ru/2016/11/21/ai-next-step-robots-nicolas-boudot/ https://dataconomy.ru/2016/11/21/ai-next-step-robots-nicolas-boudot/#comments Mon, 21 Nov 2016 08:00:34 +0000 https://dataconomy.ru/?p=16876   Nicolas Boudot is the EMEA Director at Softbank Robotics, where he has been responsible for partnerships and for promoting the use of humanoid robots in the B2B, educational and consumer markets, since 2001, when Aldebaran Robotics, the makers of Nao humanoid robots, was acquired by Softbank Mobile. He has over 20 years of experience […]]]>

 


nboudot

Nicolas Boudot is the EMEA Director at Softbank Robotics, where he has been responsible for partnerships and for promoting the use of humanoid robots in the B2B, educational and consumer markets, since 2001, when Aldebaran Robotics, the makers of Nao humanoid robots, was acquired by Softbank Mobile. He has over 20 years of experience in sales and marketing management in the European marketplace.  Before taking up this role at Softbank Robotics Europe, Boudot was active in the fields of IT-Security, and Electronic Design Automation.

 


 

Nicolas, can you give us a quick introduction to you, to Aldebaran, and your work there?

We are part of Softbank Group – Softbank being a Japanese mobile company. We’re based in Paris. That’s where I get my French accent!

I now manage a team of sales guys for developing the user product across Europe, Middle East and Africa. I have a bit of an engineering background, and as soon as I discovered the existence of Aldebaran, I jumped at the opportunity to work with the company. That was 6 years ago, it was a pretty small company, less than 100 people, and it’s now over 450 people.

At Aldebaran, we are working on developing these fully managed robots that we are showing at exhibits.

What is your mission statement?

Aldebaran brings the future to your customer. That is our mission statement. We are developing robots that will enhance the daily lives of people. Today we do that with various small robots – for example, one that is helping autistic kids develop behaviors and improve communication skills. But also we’re working on helping people grow their businesses with robots in stores. That’s what we do with the Pepper robot.

Can you tell us more about Pepper?

Why humanoid robots? We are focusing on developing robots with the humanoid shape, because we know it’s the best shape to be accepted by other people and to evolve in a world designed by humans, for humans. Also, the humanoid shape allows the device to have body language, and that’s important when we are interacting human to human. Because of that, all the robots we design are really engaging in interactions, and we see that people are showing empathy with the robots because of the way they behave. We work a lot on what we call emotional intelligence – when we can analyze the emotional mood of people and have the robot adapt its behavior to those moods.

Pepper is now used in over 3,000 stores in Japan, where he’s increasing [foot traffic], welcoming people, managing queues, doing the repetitive speech on the phones, doing the sales. The robot is really bringing a lot to the customers.

The use of Pepper is to be in places that are very public – for example retail, banks, hotels and government services. It’s a wonderful device to push information and services to people, as well as retrieve information and data from them.

Are you focusing on a specific industry?

Aldebaran is just focusing on developing the robot and making sure it’s working correctly, and then managing a community of partners who will be asked to provide a complete solution to the customer.

Do you think robots can replace some human jobs?

Yeah, that’s an interesting question. The way we see it at Aldebaran is, robots today are taking the tasks that humans don’t value. For example, our robot will be joining the AIDA cruise ships in May to assist with welcoming people and answering all the FAQs – like connecting to WiFi and booking some time in the spa. There is no value to dedicate a person to those tasks, so we free people from doing that.

Globally, [robots] are going to have some impact…but at the same time, we’re creating more positions. You need people to set up the robot, maintain it, clean it and develop it. We think that having robots will not have a dramatic impact on the global employment situation.

Can you tell us a little about the technology behind Pepper?

The technology is complex and easy at the same time. It’s an intelligent mix of electronic, mechanic and programming. The DNA of Aldebaran is maybe not to use the best technologies: If you look at those from Boston Dynamics or the ASIMO robot from Honda, they are wonderful robots. They can run, jump a wall, take glasses and give you drinks – but these machines are far from ready to enter the market and they cost millions of dollars. We believe that with current technology, we are capable of producing a robot that is able to move, able to interact and can go to market today even if it doesn’t have the best motor. We use technology that is affordable and know how to make it work correctly.

AI is the next step for robots and it’s what we’re working on. We’re not doing that alone. We partnered with IBM Watson technology, and we’re going to implement some of their technology in our robots. The best example I can show you is natural language processing. Today, when you interact with a robot, you have nice and wonderful dialogue – but most of it is scripted. You can’t have a real, open conversation with a robot, but we will have that soon with the Watson technology. You will be able to tell anything to the robot, connect to the cognitive database of Watson, and if the answer is there, it will be provided back to you through the robot.

What benefits does data collection bring to your business?

We use it to improve our technology. We collect the data from voice recognition to improve how we recognize voice, and use data we collect from the emotions to make the robot more capable of adapting its behavior. Other than that, we’re not specialists in analytics. But again, we partner with other people to allow customers to have analytical data.

What do you think will produce the biggest shift in the robotics industry?

Clearly AI is a key subject, and I think that it’s the biggest [shift] expected [in] the industry!

 

 

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“Companies are finally able to start a data culture” – Interview with Frank Bien, CEO of Looker https://dataconomy.ru/2016/10/05/data-culture-frank-bien-looker/ https://dataconomy.ru/2016/10/05/data-culture-frank-bien-looker/#respond Wed, 05 Oct 2016 08:00:12 +0000 https://dataconomy.ru/?p=16601 With over 20 years growing and leading technology companies, Frank Bien built his career on nurturing strong corporate culture and highly efficient teams. Prior to Looker, Frank was SVP of Strategy for storage vendor Virsto (acquired by VMware) and VP of Strategic Alliances at big-data pioneer Greenplum, leading their acquisition by EMC (now Pivotal). He […]]]>

"Companies are finally able to start a data culture" - Interview with Frank Bien, CEO of LookerWith over 20 years growing and leading technology companies, Frank Bien built his career on nurturing strong corporate culture and highly efficient teams. Prior to Looker, Frank was SVP of Strategy for storage vendor Virsto (acquired by VMware) and VP of Strategic Alliances at big-data pioneer Greenplum, leading their acquisition by EMC (now Pivotal). He led Product Marketing and Strategy at early scale-out data warehousing company Sensage and was VP of Solution Sales at Vignette/OpenText. Earlier in his career he held executive roles at Dell and the Federal Reserve. Frank recently co-authored the book, Winning with Data: Transform Your Culture, Empower Your People, and Shape the Future (Wiley), which takes a deep dive into big data in business, explores the cultural changes it will bring and discusses how to adapt an organization to leverage data to its maximum effect.


Frank, could you describe the first ‘a­ha’ moment when you realized the power of data?

When you work every day in data, you constantly hear fun stories about how data helped in some one-off way. It’s kind of like data trivia. My big ah-ha moment came about eight months after starting at Looker. I visited several customers in San Francisco one day when we were shooting testimonial interviews — they were completely unscripted and un-prepped. But all of the customers said the same thing — that they had finally been able to start building a data culture. They actually had a lot of their business team users going into the tool to ask questions or get information that guided what they did that day. They would get facts because they were easy to access. The projects had moved from traditional analytic “rearview mirror” analysis to using massive amounts of data to inform very practical business questions. My experience until then was that people just stopped asking questions because it was too hard — even with expensive new tools. But here was a rapid succession of customers I was talking to that all were telling us that Looker had actually moved them to the next level. It was a huge ah-ha moment that we were onto something. That shifting culture was actually possible given the right environment and toolset.

When should a company start building a data culture? At what stage of growth does data start to make a difference? 

I think new companies are almost always starting off with data in mind. Generally, they’re trying to disrupt some entrenched larger company that’s moving too slowly or not seeing things clearly. Data is often part of their business plan. So it may actually be a different question — how do you build data culture before it’s too late. I’d bet a lot of taxi companies might be asking that question now. It’s never too early, but it’s often too late.

If a company has its data and BI tools in place, what’s the biggest impediment to spreading a data­-driven culture? What steps can help overcome the challenge?

I think the problem is often in the data supply chain — the myriad of piece-meal tools people have put in place often to replace some monolithic beast like Business Objects or Cognos. But this mess of tools is probably even more complex. There are pieces to do integration, transformation, wrangling, governance, visualization and exploration. We’ve really created a mess in data supply chains right at the time everyone woke up and realized data is important. We haven’t seen the new, modern data stack that solves these piece-meal problems. Looker is hoping to be that platform.

 What are the biggest pitfalls or common mistakes that people make when interpreting data?

We actually have a phrase for it: “data brawls”. So often, as people have tried to do self-service visualization, everyone is describing the same data metrics differently. For example, Lifetime Value of a customer (LTV) or even more core questions like what is a “customer”.  Marketing Ops uses a different formula, Sales is using the wrong data, Finance is stripping out tax but other teams aren’t. When you don’t have a common language, you end up with a Tower of Babel… everyone is screaming, but nobody can hear each other. Nobody can agree. Because everyone is working in silos, often they don’t even know that they’re disagreeing until it’s too late. This underlying failure to have reliability in data makes people think they are all right, when clearly that can’t be true.

How do you decide which data you should be collecting? Is it really best to just store everything you can, or does that create a mess later?

We think this is the big disrupter. It’s when people can capture enormous amounts of data in really inexpensive analytic data stores, and they don’t have to know how to organize it toward specific questions up front. Call it a data lake, or call it something else — it doesn’t matter. What we have to do as technology vendors is provide that last mile of value on top that lets organizations take advantage of the giant steps forward on the data infrastructure side. So far, the tools on top have only just evolved slightly. We’re trying to cause a revolution.

What do you see as the next significant innovation in how businesses use data? Where is this movement headed?

A. We have to clean up the mess first. We’ve only solved how to capture and store the information — now we need to help people get real value out of it. Not just for science experiments, but to engrain data into the core decision-making processes in companies. To actually wrap data around business process in the same way transactional systems like Salesforce.com did.  That’s where data moves from an out-of-cycle business step to part of the core work process. When we solve that, the next big evolution can take place.

 

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“Machine intelligence is the next step in the evolution of machine learning” – Data Natives 2016 https://dataconomy.ru/2016/09/27/machine-learning-data-natives-2016/ https://dataconomy.ru/2016/09/27/machine-learning-data-natives-2016/#comments Tue, 27 Sep 2016 17:40:17 +0000 https://dataconomy.ru/?p=16587 Francisco is the Founder and CEO of cortical.io, a machine learning company that develops Natural Language Processing solutions for Big Text Data. Francisco’s medical background in genetics combined with over two decade’s of experience in Information Technology, inspired him to create a groundbreaking technology, called Semantic Folding, which is based on the latest findings on […]]]>

"Machine intelligence is the next step in the evolution of machine learning" - Data Natives 2016Francisco is the Founder and CEO of cortical.io, a machine learning company that develops Natural Language Processing solutions for Big Text Data. Francisco’s medical background in genetics combined with over two decade’s of experience in Information Technology, inspired him to create a groundbreaking technology, called Semantic Folding, which is based on the latest findings on the way the human neocortex processes information. Prior to Cortical.io, Francisco founded Matrixware Information Services, a company that developed the first standardized database of patents. Francisco also initiated the Information Retrieval Facility, a non-profit research institute, with the goal to bridge the gap between science and industry in the information retrieval domain.


Go to datanatives.io instant access to tickets and speaker information on Data Natives 2016


Let me introduce you to Francisco Webber, Founder and CEO of cortical.io. He is one of the 70+ speakers who will take the stage at Data Natives 2016. Don’t miss his talk, “Semantic Folding: A new, brain-inspired model for Big Data Semantics” – head to datanatives.io and get your ticket now!

Q: What topic will you be discussing during Data Natives Berlin?

I will explore the concept of Big Data Semantics and explain how new technologies that leverage the intelligence of the brain can unveil insights hidden in Big Text Data in a very precise and efficient manner. I will present a new machine learning approach that reproduces the way our brain processes information and makes language computable.

Q: How did you get involved with machine learning?

When I founded my first start-up (Matrixware Information Services) and worked with patent search and databases, I realized that the state-of-the art search tools were not delivering satisfactory results. In fact, professionals looking for very specific information within patent databases, technical documents or scientific publications were quite desperate because there was no tool that was half as satisfactory as their own brain. This is the time I began to think about an intelligent search engine, what it would look like, what would be the mechanism behind it. Intuitively, I thought such a machine should somehow mimic our brain, but I was at a loss about how to achieve this goal.

Q: Where did you find the inspiration for ‘Semantic Folding’?

I read a lot about neuroscience, trying to build a bridge between neuroscience and computer science. When I discovered Jeff Hawkins’ book On Intelligence, I felt I had found something that could be applied to language processing. In fact, Jeff’s theory about how the brain processes information was the missing link to transpose the brain’s natural intelligence to the understanding of language by a machine.

Q: How is data being applied to create change in this field?

To sort and make sense of the gigantic amount of data created every day is impossible with current technologies, which largely rely on statistics and necessitate huge amounts of processing power. Take a bank, for example, which must comply with tight regulations and risk huge fines if it fails, say, to detect fraud by an employee. This bank needs a system that is not only able to monitor 100% of all messages sent and received by the thousands of employees worldwide, but also understands their content in order to identify them as a threat if necessary. Lists of keywords, word count statistics and linguistic rules just don’t work any more. This bank needs an intelligent system that screens all messages quickly and efficiently as they come in and that does not require absurd amounts of computing power.  This bank needs an accurate system that does not generate expensive false alarms; a system that does not need to translate messages into English in order to understand their meaning.

The only way I know of to achieve these goals is to monitor the emails with the intelligence of an artificial brain. I am talking of making a computer system use the same principles as the brain to understand the meaning contained in text. This is what is called machine intelligence and is the next step in the evolution of machine learning, away from large training data sets and complex rule sets, towards simple, straightforward comparisons of similarity in the meaning of text.

Q: What do you hope to gain/learn during data Natives Berlin ?

The field of Artificial Intelligence (AI) is monopolized by big names like Google and Facebook. They invest huge amounts of money in AI research and have become the reference in that field. The media tend to represent the approach they focus on (Deep Learning and Neural Networks) as the holy grail of AI. My goal at Data Natives Berlin is to demonstrate that there are other approaches worth considering. These don’t yet benefit from the attention of the tech media, but that have already proven their effectiveness when applied to real life business cases, especially in the field of Natural Language Processing. Semantic Folding, the theory behind Cortical.io’s approach, belongs to these promising new paths.

Q: Why is Berlin a strategic market for showcasing data-driven technologies?

Berlin has a strong start-up community and has proven to be a market of early adopters. I am particularly interested in those developers that have already applied machine learning tools to their data sets and reached their limits. I want to tell them: “This is not the end of the journey. There are other ways of making sense of your text data.”

 


Go to datanatives.io to get instant access to tickets and speaker information on Data Natives 2016


 

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5 Reasons to Attend Data Natives 2016: #1. Building A Strong Professional Network https://dataconomy.ru/2016/09/22/reasons-data-natives-2016-professional-network/ https://dataconomy.ru/2016/09/22/reasons-data-natives-2016-professional-network/#comments Thu, 22 Sep 2016 12:21:45 +0000 https://dataconomy.ru/?p=16561 Go to datanatives.io and get instant access to speaker and ticket discount information on Data Natives Berlin 2016. The second edition of Data Natives is coming soon, and it will focus on the intersection of key trends in the Data Science realm, to make sure you’re in the loop and ahead of the curve. We celebrate key […]]]>

Go to datanatives.io and get instant access to speaker and ticket discount information on Data Natives Berlin 2016.


The second edition of Data Natives is coming soon, and it will focus on the intersection of key trends in the Data Science realm, to make sure you’re in the loop and ahead of the curve.

We celebrate key areas of technology that are driving innovation and the next wave of billion dollar startups. We’ll introduce popular streams related to technological advancements in AI, Machine Learning, IoT and trends revolving around booming industries such as FinTech, HealthTech, AdTech and RealTech.

At Data Natives, individual consumers and multi-billion dollar industries alike have a tremendous opportunity to learn and network with leading scientists, founders, analysts, investors and economists.

Conference tickets are still available until October 1st – Don’t miss your chance to attend Berlin’s best big data conference before prices go up! In case you’re still not completely convinced, we came up with the top 5 reasons why you should attend, based on our conversations with some of last year’s attendees.

Building a strong professional network

Networking is a necessary ability in every professional field, and conferences are perfect playgrounds to put your people skills to work. Over coffee breaks, lunch, cocktails, or even during the time in between sessions, you will have the chance to make connections with truly talented individuals, and even interesting prospects for your organization. Who knows, maybe you’ll sit next to a potential customer at a talk, or meet your mentor at a workshop.

Attending conferences is also a great way to be at the forefront of innovation in your industry. There’s nothing like sitting in a room full of like-minded people, full of energy and looking to meet others who share their passion. At Data Natives, you’ll get to mingle with your peers from all over the world, and also meet experts and influencers face to face, who will surely leave you profoundly inspired.

Pierre Martinow is part of what we call the ‘early adopters’ of Data Natives. Now a Data Science Business consultant at DataRobot, he attended Data Natives Berlin 2015 while working as a consultant with Think Big. I caught up with him directly after the conference, to talk about how they benefitted from coming down to Berlin, and how the larger Data Natives European network helped them find interesting candidates for data engineering and project management positions.

When I asked if he could summarize his experience at Data Natives 2015, his emphasis was on how Think Big was able to benefit from brand exposure:

“In terms of brand awareness the event was very successful. It was a truly great opportunity to reach out to people interested and employed within the Big Data industry. We had countless one-on-one conversations with people who were interested in what we do. It was fantastic, since, at the time, no one knew who we were. We were nonetheless able to get our foot in the door in many new recruiting and PR opportunities just be being at the conference and talking to people.”

This was a definite a success, seeing that Pierre and Think Big attended Data Natives with more of an open mind, rather than an action plan – “We didn’t define any recruitment targets before we started sponsoring the conference. But, even if we would’ve had specific expectations, I’m sure they would’ve been surpassed.”

Data Natives are International

The more I talked to Pierre, the more I realized that one of the fundamental powers of the conference lies on our vast network of over 32,000 data enthusiasts. Think Big was “ looking to expand and interested in finding senior project managers and data engineers”, Pierre explained. At Data Natives, “the applications and interest we received came mostly from the mid-level of the career spectrum”. Back in London – one of Think Big’s two European locations – they were able to double down on their recruitment efforts, by tapping into our Data Lovers, London community of over 2,000: “We became quite active in your London meet-ups and were able to find very good data scientists from that community, eventually finding the some great fits for our ‘Most Wanted’ list.”

Pierre’s and Think Big’s story is only one of many Data Natives success stories – stay tuned for the next reason why you can’t miss Data Natives 2016: our bad ass program.


Go to datanatives.io and get instant access to speaker and ticket discount information on Data Natives Berlin 2016


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Q&A with Angel García, Managing Director of Startupbootcamp – Data Natives Tel Aviv 2016 https://dataconomy.ru/2016/09/22/qa-angel-garcia-managing-director-startupbootcamp-data-natives-tel-aviv-2016/ https://dataconomy.ru/2016/09/22/qa-angel-garcia-managing-director-startupbootcamp-data-natives-tel-aviv-2016/#comments Thu, 22 Sep 2016 10:40:09 +0000 https://dataconomy.ru/?p=16553 Meet Angel García, Data Natives Tel Aviv 2016 Judge and Speaker You can register for tickets to Data Natives Tel Aviv here. Angel García is the Managing Director of Startupbootcamp Internet of Things & Data Tech, the Barcelona based acceleration program. Angel has years of experience working with startups and will serve as a speaker and […]]]>

Meet Angel García, Data Natives Tel Aviv 2016 Judge and Speaker

You can register for tickets to Data Natives Tel Aviv here.

angel-garcia

Angel García is the Managing Director of Startupbootcamp Internet of Things & Data Tech, the Barcelona based acceleration program.

Angel has years of experience working with startups and will serve as a speaker and a judge during Data Natives Tel Aviv’s Startup Battle. Here is what Angel has to say about Data Natives Tel Aviv, Startupbootcamp Barcelona and the Israeli tech ecosystem.

Q: How is Startupbootcamp Barcelona driving innovation?

We accelerate startups that are re-inventing the future by helping companies involved in the fields of Smart Data and the Internet of Things. These companies join Startupbootcamp’s acceleration program to improve the way we live – we believe these entrepreneurs are building the future with their technologies. We’re helping these entrepreneurs do so by connecting them with the right corporations and bringing new innovations to the market.

Q: What data-driven technologies are of particular interest to you and why?

 Startupbootcamp Barcelona focuses on data-driven technologies (software oriented) in the following categories:

  • Analytics – Behavior patterns, business intelligence solutions, data-mining, data modeling
  • Artificial Intelligence – Cognitive computing, deep learning, machine learning, neural networks, pattern/image/speech/text recognition software, predictive analysis
  • Augmented & Virtual reality – Applied to B2B or B2C environments in the mentioned sectors & verticals
  • Connectivity – Data transmission, synchronisation, virtualization, Infrastructure Analytics, cloud, clusters, distributed, fog, IoT, management, networking, platforms, security, storage
  • Internet of Things – Compression, control, networking, software, platforms, protocols, remote access, power management
  • Security – Access control, encryption, identification, intrusion detection, storage, tracking
  • Storage – Access control, backups, categorization, compression, extraction, software

The transformation of analog systems to digital ones and the support they offer for making better decisions at the right moment is a fundamental question. Yet, how are we going to build software to make decisions or to find pragmatic solutions to solve a problem?

Startupbootcamp brings to market the full potential of computational power in order to find better propositions to achieve the goal mentioned above. We focus on the most disruptive technologies that amplify the possibilities of this new era.

Q: What do you hope to gain or learn during Data Natives Tel Aviv?

At Startupbootcamp Barcelona, we are working on what we call ‘the next big thing.’ During Data Natives, we are looking forward to meeting people who would want to join us in our path to achieve new challenges, to solve new problems and to open new markets.

Data Natives is bringing together people working on data-driven solutions that have the power to shape every aspect of our lives – from the way we understand ourselves to the way we run our cities. These are the talented people I’m looking forward to meeting.

Q: Do you believe that Israel is a strategic market for showcasing data-driven technologies? If so, why?

This year, investors have been looking towards Israel, New York, China and all over the world to find opportunities. The entrepreneurs in Israel are becoming pioneers in technologies, specifically, in those related to analytics in the world of Smart Data, Cybersecurity and many more.

Q: Can you offer advice for startups wanting to get involved with Startupbootcamp Barcelona?

Entrepreneurs whom want to change and improve people’s lives and the world around them need to make that possible not only with their technologies, but also with their attitude. That’s why we spend a lot of time meeting the teams that join Startupbootcamp Barcelona. We want to see if these teams are open minded and humble enough to keep on learning daily, while receiving feedback from mentors, partners and potential clients.

It’s not easy to lead a company. At Startupbootcamp, we prepare startups to be ready for everything they will encounter. Moreover, our global network of programs help startups quickly scale and expand to different markets.

Meet Angel and learn more about Israeli startups driving innovation during Data Natives Tel Aviv 2016 –Save your spot by registering today!

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“Finding a relevant, trustworthy dataset can be like finding a needle in a haystack” – Interview with Satyen Sangani https://dataconomy.ru/2016/09/19/satyen-sangani-alation/ https://dataconomy.ru/2016/09/19/satyen-sangani-alation/#respond Mon, 19 Sep 2016 08:00:11 +0000 https://dataconomy.ru/?p=16467 Satyen is the CEO of Alation. Before Alation, Satyen spent nearly a decade at Oracle, ultimately running the Financial Services Warehousing and Performance Management business where he helped customers get insights out of their systems. Prior to Oracle, Satyen was an Associate with the Texas Pacific Group and an Analyst with Morgan Stanley & Co. Satyen holds […]]]>

"Finding a relevant, trustworthy dataset can be like finding a needle in a haystack" - Interview with Satyen SanganiSatyen is the CEO of Alation. Before Alation, Satyen spent nearly a decade at Oracle, ultimately running the Financial Services Warehousing and Performance Management business where he helped customers get insights out of their systems. Prior to Oracle, Satyen was an Associate with the Texas Pacific Group and an Analyst with Morgan Stanley & Co. Satyen holds a Masters from the University of Oxford and a Bachelors from Columbia College, both in Economics.


Dataconomy: What is a data catalog?

Satyen: Much like Amazon helps users buy the right product, a data catalog helps people get the right data. A good data catalog provides rich information on all data within an organization, so members can find a relevant data set, understand what it means and where it came from, trust that it’s accurate and up-to-date, and then put it to use. A modern data catalog will leverage powerful technologies—like crawling and indexing, query log parsing, artificial intelligence, machine learning, and natural language processing—appropriately combined with crowd-sourcing and expert-input, to achieve both broad coverage and high quality of data knowledge. In addition to describing the data, it will also show how it’s been used in the past and ought to be used in the future.

Dataconomy: Who uses a data catalog?

Satyen: Data catalogs are used by data consumers (i.e. people who use data to make reports, models, analyses, products, or decisions) including data analysts, data scientists, statisticians, marketers, product managers, salespeople, customer support personnel, finance and operations workers, and even executives. By making data more searchable and consumable, a data catalog can broaden the data audience and make an organization more data-driven across the board.

Data curators and creators also play a role in populating and enriching the data catalog. A modern data catalog will automatically fill in lots of information, freeing humans to add differentiated value.

Dataconomy: Why do today’s data consumers need a data catalog? What’s its value?

Satyen: Today, organizations have more data than ever, so finding a relevant, trustworthy dataset can be like finding a needle in a haystack. And often, many different datasets look similar, so it’s very challenging to determine which is accurate and up-to-date. Data catalogs save data consumers time and help them deliver accurate analyses. This increases organizational trust in data and yields smarter decisions.

Dataconomy: What made you get into this market and why now?

Satyen: In the 90s, the internet was growing faster than Yahoo! could taxonomize it; then Google came along and indexed the web intelligently (leveraging implicit human signals with PageRank) so everyone could actually find useful information.

We saw a similar trend within organizations, that the scale and complexity of data environments was increasing faster than the human workforces tasked with leveraging them. One of our customers has literally tens of millions of data fields and saw that number more than double in just two years, during a time where they had only hired a handful of new analysts. Storing data has been getting easier and easier, but finding it and putting it to use was actually getting harder.

It was clear that someone needed to solve the human problem with data, and to do so in an automated, scalable way that learns from people without requiring human labor. So we did.

Dataconomy: Where do you see the data catalog market going in five years from now?

Satyen: We see data getting further democratized. In five years, anyone who can look at a spreadsheet or a line chart will be using self-service tools to get data, without depending on “techies.” They’ll use natural language in conversational, English-In/Answers-Out interfaces to find insights and make better decisions, much like they use Google today. A data catalog is like Google’s index of the web, a platform on which incredibly empowering apps can be built for end-users.

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“German companies are behind on the trend of using data to solve business challenges” – Interview with Dr. Jonathan Mall – Data Natives Berlin 2016 https://dataconomy.ru/2016/09/13/data-natives-jonathan-mall/ https://dataconomy.ru/2016/09/13/data-natives-jonathan-mall/#respond Tue, 13 Sep 2016 08:00:01 +0000 https://dataconomy.ru/?p=16516 Data Natives Berlin speaker Dr. Jonathan Mall is a computational Neuropsychologist turned entrepreneur. Seduced by the opportunity to optimize consumer experience using machine learning, he led the Science team in a IBM Big Data Venture (gumbolt.com). Afterwards, he founded Neuro-Flash.com, a market research institute, using online experiments that illuminate the true drivers of desire and […]]]>

"German companies are behind on the trend of using data to solve business challenges" - Interview with Dr. Jonathan Mall - Data Natives Berlin 2016Data Natives Berlin speaker Dr. Jonathan Mall is a computational Neuropsychologist turned entrepreneur. Seduced by the opportunity to optimize consumer experience using machine learning, he led the Science team in a IBM Big Data Venture (gumbolt.com). Afterwards, he founded Neuro-Flash.com, a market research institute, using online experiments that illuminate the true drivers of desire and purchase behaviour. When he’s not combining Neuroscience and Big-Data to test innovative ideas, he eats burgers and trains for the next marathon. Connect with Jonathan on LinkedIn.


Go to datanatives.io and get instant access to speaker and ticket discount information on Data Natives Berlin 2016


What topic will you be discussing during Data Natives Berlin?

My talk is called “Brain + Data: How Neuroscience can increase data mining effectiveness” Neuroscience insights can guide Data Mining techniques, especially in terms of feature reduction. In other words, by predicting the impact of information on recall and associations, you may uncover better ways of finding the data that matters. My talk is of interest to data scientists across the board, because without understanding how our brains process information, just mining data becomes a discipline of amassing cold data with a bleak outlook on meaningful application for warm humans.

What prompted you to co-found Neuro-Flash?

Opinion and Market research is still wildly inaccurate with certain traditions being more akin to homeopathy rather than being evidence based. By combing more refined understanding of human behavior with the right kind of data, we strive to revolutionize the way that human behavior is understood and predicted on a grand scale.

How are you applying brain data to create change in the field of market research?

We started by applying the learnings from the last 10 years of cognitive research and experimental design to our online studies. We furthermore created and validated novel approaches to measure people’s unconscious preferences.

What do you hope to gain/learn during Data Natives Berlin?

I’m most fascinated by tapping into novel data sources for measuring and predicting human behavior. Talking to people with such ideas or solutions would make my day.

What data-driven technologies are of particular interest to you and why?

We currently invest in AI-driven creativity and statistical interpretation. Finding a better way to solve currently non-scalable parts of our business model.

Do you believe that Germany is a strategic market for showcasing data-driven technologies? If so, why?

No, I think that stereotypically speaking, German companies are lacking behind the general trend of using data to solve business challenges. Nevertheless, raising awareness in Germany about the benefits of evidence based business practices is a worthwhile goal.

How is Neuroscience driving the data revolution?

Generally, behavioral economics and cognitive neuroscience is challenging the way we understand the processes behind human decisions. It can offer explanations for unexpected patterns found in big-data analyses and open up hypothesis driven approaches to gathering and interpreting data.

Can you offer advice for others wanting to get involved in this particular field?

Challenge every assumption about yourself and the world. If you didn’t become terribly depressed doing so, continue exploring in this field.

 

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Q&A with Startup Expert, Maren Lesche – Data Natives Tel Aviv 2016 https://dataconomy.ru/2016/09/09/qa-startup-expert-maren-lesche-data-natives-tel-aviv-2016/ https://dataconomy.ru/2016/09/09/qa-startup-expert-maren-lesche-data-natives-tel-aviv-2016/#comments Fri, 09 Sep 2016 15:16:33 +0000 https://dataconomy.ru/?p=16526 Maren Lesche is the Communications Manger and Startup Expert at the European Innovation Hub, a Mentor for Startupbootcamp Digital Health Berlin and a notable blogger with a focus on startups. Maren has years of experience working with startups and will serve as a speaker and a judge for Data Natives Tel Aviv’s Startup Battle. Here […]]]>

Q&A with Startup Expert, Maren Lesche – Data Natives Tel Aviv 2016Maren Lesche is the Communications Manger and Startup Expert at the European Innovation Hub, a Mentor for Startupbootcamp Digital Health Berlin and a notable blogger with a focus on startups. Maren has years of experience working with startups and will serve as a speaker and a judge for Data Natives Tel Aviv’s Startup Battle. Here is what Maren has to say about Data Natives Tel Aviv, data-driven technologies and the Israeli tech ecosystem.


You can register for tickets to Data Natives Tel Aviv here.


Q: What do you hope to gain or learn during Data Natives Tel Aviv?

Data Natives differs from many of the other startup conferences that I’ve attended. I joined the first Data Natives event in Berlin in the fall of 2016 and was impressed by the high level of speakers, by the topics of discussion, the deep tech focus and, of course, by the variety of startups that participated in the startup battle.

That being said, I’m looking forward meeting more big data and eHealth experts during Data Natives Tel Aviv to talk about future trends and also to discuss how the IoT community can come together and become even closer in the future.

At the European Innovation Hub we coordinate the EU-funded IoT-European Platforms Initiative. Within this program, seven research consortia with more than 100 partners collaborate to build a strong IoT ecosystem in Europe. Following the motto “from lab to market,” we encourage the development of interoperable platform technologies and foster the adoption by the developer and entrepreneur community.

Shortly after DataNatives Tel Aviv, we will start the first of ten Open Calls. Within this call, SMEs and startups in eHealth, IoT, Big Data and logistics can apply for financial support of up to 125,000 Euro per project. Just in this one call we will invest 850,000 Euro to strengthen IoT technologies made in Europe and Israel.

What data-driven technologies are of particular interest to you and why?

I tend to focus more on the consumer side of technology. In the B2C area, I am most interested in technologies that are changing peoples lives, that overcome boundaries and that will support people in future such as eHealth technologies including smart textiles and robotics in MedTech. In terms of B2B solutions, I am fascinated by industrial IoT, e.g. Virtual Reality in B2B, predictive maintenance, beacon solutions.  

Do you believe that Israel is a strategic market for showcasing data-driven technologies? If so, why?

Israel is a very interesting market for any tech and data-driven industry. Many big players have strong innovation hubs in the “Startup Nation” including Deutsche Telekom, Microsoft and IBM.

Developers in Israel are also extremely experienced and well educated. The community of Israeli tech and business experts are also highly energetic. However, there are reports such the one from Innovation Endeavours that highlight uncovered potential, especially in the area of IoT.

According to investment experts, 80 percent of the startups active in IoT (during the end of 2015) are focused on applications rather than IoT platforms or components. I am curious to see how the data-driven startup community in Israel is taking on this challenge. 

How is your particular field of interest driving the data revolution?

Industrial IoT is still a playing field for entrepreneurs. In Europe, we have many very successful mid-sized companies operating all over the world, but are in areas consumers hardly see. They produce components, take care of logistics, develop new materials and are therefore the backbone for entire industries. If we manage to link these companies with smart startups, then both side swill benefit. Plus, we can access hidden data to kick off cool innovation in the B2B sector. Would I call this a revolution? No, but I think it will be a “smart evolution.”

I also would like to add another layer to the discussion: When we talk about the drivers, we must also talk about current barriers. I believe that working on trust is one of our top priorities in IoT. If we deal with sensitive data such as in eHealth and Fintech, we have to discuss privacy and security. These are two strong topics that always come up when we talk with the partners of the IoT-European Platforms Initiative. Trust plays an essential role in the development of strong IoT businesses in Europe. Customer –and societal – acceptance of data driven services is crucial for its success. 

Can you offer advice for others wanting to get involved in these particular fields?

There is a general piece of advice that I give entrepreneurs as well as executives: Listen first!

Whatever you do, you have to understand how businesses work, what the pain points are and how people are dealing with problems right now to find the proper solutions. 

In IoT, this also means talking to users to understand how processes are connected. Think about collaboration instead of product ownership. First and foremost, connected technology requires communication to overcome borders between sectors, countries and most importantly, competition. Within the IoT-European Platform Initiative, we just started to bring more than 200 researchers, industry representatives and SMEs together – in my mind this is the first step towards building a healthy IoT ecosystem. 

 

Meet Maren and learn more about Israeli startups driving innovation during Data Natives Tel Aviv 2016 – Save your spot by registering today!

 

Think you have what it takes to compete in the Startup Battle? Apply for the Data Natives Tel Aviv Startup Battle here.

 

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“Smart Data Is The Future of FinTech” – Speaker Spotlight: Q&A with Patrick Koeck – Data Natives Berlin 2016 https://dataconomy.ru/2016/09/07/smart-data-future-fintech-speaker-spotlight-qa-patrick-koeck-data-natives-berlin-2016/ https://dataconomy.ru/2016/09/07/smart-data-future-fintech-speaker-spotlight-qa-patrick-koeck-data-natives-berlin-2016/#respond Wed, 07 Sep 2016 08:00:35 +0000 https://dataconomy.ru/?p=16463 Patrick Koeck is a Chief Operating Officer and a previous Chief Risk Officer in European Smart-data powered lender Creamfinance. Before coming to Creamfinance he tested his mettle in companies such as Alkoe GmbH, the Coca-Cola Hellenic Bottling Company and KPMG Austria, where he focused on database management and Financial Controlling. At the moment he is […]]]>

"Smart Data Is The Future of FinTech" - Speaker Spotlight: Q&A with Patrick Koeck - Data Natives Berlin 2016Patrick Koeck is a Chief Operating Officer and a previous Chief Risk Officer in European Smart-data powered lender Creamfinance. Before coming to Creamfinance he tested his mettle in companies such as Alkoe GmbH, the Coca-Cola Hellenic Bottling Company and KPMG Austria, where he focused on database management and Financial Controlling. At the moment he is responsible for improving automation and development of all 5 countries where company is located – Latvia, Poland, Czech Republic, Georgia and Denmark.


Patrick, please tell us a little bit about yourself

My name is Patrick; I work as a COO in the fastest-growing European Fintech Creamfinance in Latvia but I tend to spend a lot of time across all the operating countries. At the moment I’m responsible for improving operational development in all 5 countries where company is located – Latvia, Poland, Czech Republic, Georgia and Denmark. We’re also now about to open an office in Mexico, which is exciting. Before coming to Creamfinance I worked in Alkoe GmbH, the Coca-Cola Hellenic Bottling Company and KPMG Austria, where I focused on management reporting, customer behavior databases and controlling.

What topic will you be discussing during Data Natives Berlin?

I will be talking on Smart Data and its benefits. I believe the topic is both relevant and interesting to the great majority of Fintech startups and scale-ups along with anybody using data sources. If you miss the speech you will not know about the benefits that Smart Data can bring for the company and, believe me, there are many!

What is Smart Data? How does it compare to Big Data?

We all probably have heard of Big Data, which is usually defined by four key elements – data volume, velocity, veracity and variety. Whereas volume and velocity refer to data generation process, veracity and variety deal with quality and type of the data overall. Since the amount of data is huge, one can make conclusions that not all of it is valuable. Smart data starts by collecting data mostly from internal sources which are highly related to the outcome. Therefore, veracity is highly reduced and also the variety is reduced as you gather on your own terms. Overall it results in highly trustable and stable data sources with low level of noises generated by unrelated data.

How is Smart Data being applied to FinTech? What other approaches to data are being applied to create change in this field?

I would say that Smart Data is actually the future of Fintech: it minimizes the effect of data leakages events, focuses on quality (disregards and filters noise) and overall, is a lot more economical. Generally speaking, Fintech is changing very rapidly as technology develops, so companies need to adapt to changes and be flexible to accommodate these occurring changes (e.g. privacy terms of social media, etc.). Generation and aggregation of that data is what the finance industry needs, and that’s where Smart Data delivers.

What do you hope to gain/learn during Data Natives Berlin?

First of all, I want to get acquainted with great minds working in the industry with data, meet like-minded people and expand my network. I am open for new ideas and want to absorb as much news and ideas as possible. In addition that, I’m excited to share my experience working with Smart Data on the big stage during the conference and I’ll be more than happy to spark some discussions! So drop by and say hi if you’re nearby 🙂

What data-driven technologies are of particular interest to you and why?

Homepage analytics (specifically mouse movements/individual behavior) and statistical methods, especially with R. That’s just my personal and professional interest.

Do you believe that Germany is a strategic market for showcasing data-driven technologies?

Yes, I do. It’s a high-technology market, so it’s natural that the country is cultivating and showcasing data-driven technologies.

How is data driving the FinTech revolution?

Data is the main element within the Fintech revolution. The biggest difference comparing Fintech players to other financial institutions is the ability to change and adapt fast to changes, and generation, aggregation and analytics of the data.

Can you offer advice for others wanting to get involved in this particular field?

Be active & proactive – read, explore, attend conferences, meet experts & try to broaden your knowledge. Start by doing what is necessary, then do what is possible, and suddenly you are doing the impossible.

 

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How Successful Data Science Interviewees Made It https://dataconomy.ru/2016/09/05/successful-data-science-interviewees-made/ https://dataconomy.ru/2016/09/05/successful-data-science-interviewees-made/#respond Mon, 05 Sep 2016 08:00:09 +0000 https://dataconomy.ru/?p=16371 As part of Springboard’s effort to put together a comprehensive, free guide to acing the data science interview, we interviewed Springboard alumni and one of our mentors to get a handle on how they made it past the data science interview. Springboard teaches data science by pairing learners with one-to-one instruction with leading industry experts. Niraj […]]]>

As part of Springboard’s effort to put together a comprehensive, free guide to acing the data science interview, we interviewed Springboard alumni and one of our mentors to get a handle on how they made it past the data science interview. Springboard teaches data science by pairing learners with one-to-one instruction with leading industry experts.


1nirajsheth

Niraj ShethData Analyst at reddit, Springboard graduate

What is advice you’d have for how to ace the data science interview process?

I wish I had studied more fundamental statistics before interviewing. It’s silly, but people often look for whether you are familiar with terms like Type I and Type II errors. Depending on the time you have, I suggest getting a statistics textbook and at least becoming familiar with the terms out there.
I should have probably expected this, but I was surprised how poor we are as an industry in evaluating projects. When I talked about past projects, everyone just cared about interest value (does the analysis say something interesting?) — nobody questioned deeply the methods I used.
You didn’t ask this, but there were also some things I did that I think worked out well. One is to have a live project up somewhere with a neat visualization (i.e. more than a github repo with a readme). It doesn’t have to be fancy–just prove you can build something that works (mine was a fog prediction map, for example). It definitely helps get your foot in the door.
The other thing is to ask for a take-home data set. I don’t know about you, but I’ve found that for myself and other people who don’t have a formal data background, it can be intimidating to work on a data set on the spot; I just hadn’t developed the muscle memory for it yet. However, I knew the right questions to ask, and I could figure out how to answer them if I had a little time, so getting a take-home set let me show what I could do that way.


Saraweinstein2

Sara WeinsteinData Scientist at Boeing Canada-AeroInfo, Springboard graduate

What is advice you’d have for how to ace the data science interview process?

In terms of preparation, I wish I spent more time thinking about analytics strategy. I prepped hard on stats, probability, ML, python/R…all the technical stuff, but was nearly caught off guard by a straightforward question about how I’d approach a particular problem given a specified data set. My answer wasn’t as confident as I would have liked. I’d been so focused on the “hard” stuff that I hadn’t thought that much about higher-level analytics methods & strategies.

What surprised me and what I found difficult:

How long the process took. I knew to expect several interviews, and in fact had three. With nearly a week between each, plus waiting for my background check to clear, the process from first contact to firm offer took a month. It was stressful to say the least. Staying positive, confident, and prepared for a whole month was challenging. It would have been much easier to bear if I’d known in advance that it would take that long. For others facing a lengthy multi-interview hiring process: meditation is your friend. It helped me sleep at night, and I used the techniques right before interviews to channel calm and confidence.


sdrjansantic3Sdrjan Santic Data Scientist at FeedzaiData Science Mentor at Springboard

What is some advice you’d have for how people can ace the data science interview process? What were some of the toughest questions?

The most important thing, in my opinion, is understanding how the major supervised and unsupervised algorithms work and being able to explain them in an intuitive way. A good command of Data Science terminology is crucial. Candidates should also have a thorough knowledge of relevant accuracy metrics, as well as the various approaches to evaluation (train/test, ROC curves, cross-validation). The tougher questions would
relate to these same affairs, but with having to break out the math on a whiteboard.

How did your interview process go?

Luckily, very smoothly! Most of my interviews had a feeling of being a conversation between peers, so I didn’t find them very stressful. The companies I interviewed with moved very quickly (one round a week), which helped streamlined the process. I was also very impressed as to how most companies that turned me down gave me very honest feedback as to why!

What were some of the factors for you in choosing your current job?

Primarily, it was the opportunity to use a technical toolset and solve problems I hadn’t solved before. My previous role was very focused on just building models. The data was already completely cleaned and pre-processed, and the exploratory work was done using a commercial GUI-based tool. I felt that my data-wrangling and command line edge was being dulled slowly and jumped at the opportunity to work in an environment where I’ll be able to “get my hands dirty” once more!

 

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Speaker Spotlight: Q&A With Dr. Stefan Kühn – Data Natives Berlin 2016 https://dataconomy.ru/2016/08/26/stefan-kuhn-data-natives-berlin-2016/ https://dataconomy.ru/2016/08/26/stefan-kuhn-data-natives-berlin-2016/#respond Fri, 26 Aug 2016 08:00:13 +0000 https://dataconomy.ru/?p=16331 Data Natives Speaker Dr. Stefan Kühn is Lead Data Scientist at codecentric. Together with his team he is developing robust, fast and intelligent algorithms and is applying modern machine learning methods for analyzing large datasets. Before codecentric he was a researcher in the Scientific Computing Group at the Max Planck Institute for Mathematics in the […]]]>

Speaker Spotlight: Q&A With Dr. Stefan Kühn - Data Natives Berlin 2016Data Natives Speaker Dr. Stefan Kühn is Lead Data Scientist at codecentric. Together with his team he is developing robust, fast and intelligent algorithms and is applying modern machine learning methods for analyzing large datasets. Before codecentric he was a researcher in the Scientific Computing Group at the Max Planck Institute for Mathematics in the Sciences Leipzig with a focus on Tensor Approximation and Higher-Order Singular Value Decomposition. He earned a Diploma in Applied Mathematics, with a focus on Mathematical Optimization, at the University of Hamburg. He went on to get PhD in Applied Mathematics with a focus on Numerics, Tensor Approximation and Higher-Order Singular Value Decomposition, at the Max Planck Insitute for Mathematics in the Sciences Leipzig in 2012.


Go to datanatives.io and get instant access to speaker and ticket discount information on Data Natives Berlin 2016


Who are you, and what do you do?

I’m Dr. Stefan Kühn, Lead Data Scientist at codecentric AG in Hamburg

What topic will you be discussing during Data Natives Berlin, Stefan?

Visualizing high-dimensional Data. High-dimensional data is very hard to analyze and understand. It is even harder to visualize, explain and communicate the properties of the data and the results of the Advanced Analytics and Machiner Learning tools to others, even for/to professionals in this field. On the other hand, there is an increasing need to derive human-understable insights: It’s not only about the accuracy of a method, it’s about understanding the inner workings.

How did you get involved with Data Science and Machine Learning?

I’ve been working in Machine Learning since 2000, due to my studies in Mathematical Optimization, Numerics and Statistics and my PhD. I’ve been working Data Science, applied to the business world, since 2014, when I started working at codecentric. I have worked on Intelligent Application Performance Monitoring and Management, Bot detection, Recommender Systems, among other projects.

What do you hope to gain/learn during Data Natives Berlin?

I want to learn about some state-of-the-art applications of Data Science and Machine Learning. I want to get in contact with experts in this field

What data-driven technologies are of particular interest to you and why?

I’m very interested in Spark.  I also think Recommender Systems are a nice example for demonstrating the interplay between data science and business

Do you believe that Germany is a strategic market for showcasing data-driven technologies? If so, why?

Indeed. It is definitely a growing market, with plenty of innovation and lots of startups

How is your field of interest driving the data revolution?

It is more or less at the heart of this revolution. Statistics, Machine Learning, Mathematical Optimization: Mathematics is everywhere!

Can you offer advice for others wanting to get involved in this particular field?

Take some courses in Mathematical Optimization. You will get insights into the inner workings of machine learning algorithms.

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“The market still needs a little more time to get ready for autonomous driving.”- Interview with Christian Bubenheim https://dataconomy.ru/2016/08/24/interview-autoscout24-christian-bubenheim/ https://dataconomy.ru/2016/08/24/interview-autoscout24-christian-bubenheim/#respond Wed, 24 Aug 2016 08:00:15 +0000 https://dataconomy.ru/?p=16310 Since January 2015 Christian Bubenheim has been Senior Vice President Marketing & Product of AutoScout24. Before that he was part of the management team of Amazon Deutschland GmbH and responsible for the business unit „Consumables“ including health & beauty as well as foods. From 2003 to 2008 he was General Manager for the worldwide end […]]]>

christianbubenheimSince January 2015 Christian Bubenheim has been Senior Vice President Marketing & Product of AutoScout24. Before that he was part of the management team of Amazon Deutschland GmbH and responsible for the business unit „Consumables“ including health & beauty as well as foods. From 2003 to 2008 he was General Manager for the worldwide end consumer business and the product marketing of Thales Magellan, a worldwide leading manufacturer of GPS devices.


Tell us about AutoScout24 and their mission.

AutoScout24 is all about empowerment. We put people center stage. Our mission is to empower and support users in successfully buying and selling cars.

Today, people are deciding and acting more independently and on their own terms: They research online for the information they need in order to make the best decision. This is true, of course, for buying and selling a car as well. Being online on mobile devices lifts any time- and location-related constraints. No matter how and where people get online: We provide all relevant information and the online tools needed for taking the right decision and going through with it. We draw on sixteen years of experience and the data we collected throughout that time. This data signifies a great and profound knowledge that we make available to our users.

You mentioned you use a data driven approach in everything you do, can you expand on this? Does AutoScout24 have a particular model?

We use data to empower our users. Our tool for car valuation, for example, is a data-driven product. Many (private) sellers are insecure when it comes to setting the price for their used car. The car valuation tool analyzes historical and current data, compares the seller’s car with existing car ads on our platform and returns a price recommendation. The entire tool is based on a statistical model which allows us to process and evaluate ten years of historical data and 50 million different car prices – we combine our expertise with the market data supplied by our platform to support sellers in determining a realistic price for their car.

Our data collection empowers our partners in advertising as well. Scout24Media offers cutting-edge, data-driven advertising products such as real-time advertising (RTA) or targeting according to browsing behavior/interests, We also implement cross-marketplace analyses in order to benefit from the numerous positive synergies between the markets for real estate and cars: 30% of AutoScout24 users are interested in real estate objects and 43% of ImmobilienScout24 users intend to buy or/and sell a car. This significant overlap between the users’ interests allows us to provide customized solutions with regard to both market places. Simultaneously, we enable companies to implement target-group-related advertising and we support them in the acquisition of new customers – and we offer a consumer basis of about 17 million users per month.

And we empower our employees. We are data-driven to the bone and our analytics are deeply woven in our everyday working process. We developed our own tools for our teams in the product, sales and marketing departments. This allows us to evaluate data, draw lessons from that evaluation and develop new products and marketing schemes based on those lessons. Our success in reaching our defined performance indicators can be assessed by data evaluation as well. This agile and data-driven approach guarantees continuous improvement of our products and campaigns.

What kinds of questions do you need to ask your customers to find the right data?

Scout24 online portals offer a broad-range database with more than 400 defined parameters for automobile, real estate and finance. The challenge with putting this data to use is to ensure it always effectively supports our users and customers. Combining data from different marketplaces against the backdrop of this challenge delivers even better results. We use qualitative and quantitative results from market research as well as behavior-based user data and market data (e.g. supply and demand).

What is the most interesting data you have come across? Why?

The market data we collect are really exciting, because of the fact that we can leverage synergies with the real estate market. We are Europe’s largest online automotive marketplace with more than 2.4 million cars on offer. This provides us with manufacturer-independent, cross-border insights about the car market.

What new technologies or Data Science applications are you integrating into the business?

Currently, we are moving to the Cloud. We develop new data applications and establish one centralized hub for the data collected from our marketplaces AutoScout24, ImmobilienScout24 and FinanceScout24. This allows us to develop improved data functions and to generate optimized information – all tailored to the needs of our users. For example: Things change radically when people are having a baby. The apartment and the car might be too small for this new situation in life. In those life-changing moments, we can provide easy and stress-free support for our users to make the right decisions.

Do you think the smart car/driverless car trends will impact your business?

We are, of course, closely monitoring but also actively shaping the developments on the car market. Simultaneously, we constantly focus on the needs of our clients, ready to react as soon as they are open to something new. We believe the market still needs a little more time to get ready for autonomous driving. Still, we are testing various approaches of how to integrate data from Connected Cars into our business model. In contrast to manufacturers that are rather hardware-driven, however, we pursue an approach that can be applied to all car makes and models.

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Speaker Spotlight: Q&A With TU Wien’s Allan Hanbury – Data Natives Berlin 2016 https://dataconomy.ru/2016/08/22/allan-hanbury-data-natives-berlin-2016/ https://dataconomy.ru/2016/08/22/allan-hanbury-data-natives-berlin-2016/#respond Mon, 22 Aug 2016 08:00:35 +0000 https://dataconomy.ru/?p=16323 Data Natives Berlin speaker Allan Hanbury is Senior Researcher and Privatdozent at the TU Wien, Austria. Since 2010, he has coordinated EU-funded research and development projects on analysis and search of medical text and image data. This led to the recent founding of a start-up, ContextFlow, which is bringing the developed radiology image search technology […]]]>

Data Natives Speaker Allan HanburyData Natives Berlin speaker Allan Hanbury is Senior Researcher and Privatdozent at the TU Wien, Austria. Since 2010, he has coordinated EU-funded research and development projects on analysis and search of medical text and image data. This led to the recent founding of a start-up, ContextFlow, which is bringing the developed radiology image search technology to the market. His team at the TU Wien also works on the analysis and search of unstructured data in technical documents (patents, scientific publications), financial documents (bank and company annual reports), and enterprise social networks. He also leads a project to develop the fundamentals of a Data-Services Ecosystem in Austria.


Go to datanatives.io and get instant access to speaker and ticket discount information on Data Natives Berlin 2016


Tell us a few words about yourself

My name is Allan Hanbury, and I am Senior Researcher at Technische Universität Wien (Vienna University of Technology). I work with the Institute of Software Technology and Interactive Systems, and will be speaking at Data Natives Berlin 2016.

What topic will you be discussing during Data Natives Berlin?

My talk focuses on analyzing and searching unstructured medical data

How did you get involved with medical data analysis?

I started off with a degree in physics, but I discovered that I had a lot more fun analyzing the data than actually setting up and doing the experiments to collect it, so I ended up in computer science. Here I have been concentrating on the analysis of unstructured data, which is the most challenging data to work on.

I got involved in working on analysis of data in the medical area after identifying an unmet technology need for analysis of multilingual medical text and medical images by discussing with people working in medicine and related areas. With this information, I wrote a proposal for an EU-funded project that was granted.

How is data being applied to change the medical field?

The most visible case in the medical field has been the discovery of major drug side effects by mining medical records. If a correlation between a drug and a specific side effect is discovered by mining the information in a huge number of anonymised patient records (i.e. a certain drug and a certain side effect happen to be mentioned together in many medical records), then this provides evidence for an investigation of whether the drug is actually causing the side effect. This has led to at least one drug being taken off the market, as it had heart attacks as a side effect.

There is a huge amount of data that is collected during routine medical care that is simply archived and never analyzed. There is a huge potential to access the implicit information in this data by mining it (in an anonymised form).

What do you hope to gain or learn during Data Natives Berlin?

I hope to meet many interesting people, and learn more about what is currently happening in data analytics in medicine and other areas.

What data-driven technologies are of particular interest to you and why?

I am interested in technologies that allow analysis and search of unstructured data, such as text, and which can find the relations between unstructured and structured data. For example, other work in my team is looking at predicting the evolution of financial indicators, based on the analysis of company and bank annual reports. At present, Word Embedding technologies are proving to be very powerful in text analysis, but they are basically mathematical tools that allow a text representation to be transformed in a way that is potentially useful – there is still plenty of investigation to be done on their limits and capabilities in practice.

Do you believe that Germany is a strategic market for showcasing data-driven technologies? If so, why?

Yes. Germany is a huge market that has a history of being innovative.

How is your field of interest driving the data revolution?

There is plenty of unstructured data stored everywhere, simply because it is easier to create unstructured data than structured data. It is easier to type text into a report, than going through the process of defining and using a data structure. Reading text is also easier for people, but of course it is challenging for automated analysis. The need to analyze the text out there is leading to really interesting technological approaches, but there are still many challenges.

Can you offer advice for other Data Natives wanting to get involved in this particular field?

It’s not necessary to get a first degree in computer science. Any field that involves data analysis could be a starting point (such as physics, mathematics, statistics…) People coming from other fields into Data Science often bring useful skills and unexpected insights into the problems that need to be solved.

 

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“Security is the big issue to solve around IoT.” – Interview with Cesanta’s Anatoly Lebedev https://dataconomy.ru/2016/08/17/security-big-issue-to-solve-in-iot-interview-anatoly-lebedev/ https://dataconomy.ru/2016/08/17/security-big-issue-to-solve-in-iot-interview-anatoly-lebedev/#respond Wed, 17 Aug 2016 08:00:31 +0000 https://dataconomy.ru/?p=16305 Anatoly Lebedev is the CEO and Co-Founder of Irish company Cesanta. Together with his team, he helps define the future of embedded communication technologies. He believes that if we want to get to 20 billion connected devices by 2020, then IoT integration needs to be made simple, secure and fast. Anatoly ensures that Cesanta products […]]]>

Anatoly_headshot

Anatoly Lebedev is the CEO and Co-Founder of Irish company Cesanta. Together with his team, he helps define the future of embedded communication technologies. He believes that if we want to get to 20 billion connected devices by 2020, then IoT integration needs to be made simple, secure and fast. Anatoly ensures that Cesanta products match this vision. Cesanta has been named as the ‘One to Watch’ by Business & Finance Magazine and is the 2015 winner of the Web Summit’s ‘GoGlobal’ competition. Previously, Anatoly shaped strategic partnerships in Europe, Middle East and Africa in his 8-year tenure at Google. He is heavily involved in the Irish startup scene and can be found as a mentor at hackathons and startup weekends. When he’s not driving the business side of Cesanta forward, you can find the racing enthusiast driving through the Irish countryside.


A little bit about you and your company

My name is Anatoly Lebedev and I’m the CEO of Cesanta, an Irish technology startup working in the Internet of Things field. We are on a mission to bring all devices online. This means we apply connectivity and networking solutions by bringing devices and equipment to the Internet.

I spent 8 years at Google before founding this company.  I started in the tech field and then I moved to the business side and then to strategic partnerships. I was doing multimedia, hardware distribution, data acquisition and so on. And when we decided to start Cesanta, a bunch of other engineers joined us. 70% of the company is comprised of ex-Google employees. The mission itself is quite challenging. How do you bring all those devices online? How do you actually program them physically? That’s why we created Mongoose IoT Platform and made it easier for people with limited skills to actually prototype and build these connected things.

What was the reason for starting this company?

We have a different product called Mongoose Web Server Library. And, this product is widely used by companies like Intel, HP, Dell, Samsung, even NASA; Mongoose is now on the international space station. What we noticed was that Mongoose had been used as part of systems that companies built in-house to achieve IoT connectivity. While those guys I mentioned might have big pockets and a lot of resources to do that, the majority of companies developing IoT-enabled products are smaller and don’t have those budgets.  These smaller companies often stumble upon problems that will create insecurity and unstable products. So what we said was why don’t we just provide them with a platform to create simple, secure IoT connectivity? We know how to build it, and that’s how we decided to build the platform.

More and more companies are going to bring their products online, and all of them will be struggling with infrastructure. But, for most of them their core business is the device and what it does for the consumer; not the connectivity piece. That’s where we enable them. We’ve taken away a big, very specific problem in regards to infrastructure, connectivity and security and we’re giving them more time to concentrate on what they do best – product development.

What do you think is the benefit of using data science in IoT?

Big Data has been ‘the’ big topic, right? I think one of the reasons is that big data probably didn’t succeed as much as expected. There was a hype but then it dropped. There was not enough Big Data. What IoT actually creates effectively is simply a huge amount of data coming in. So in a nutshell IoT is just an enabler for Big Data. We see ourselves as being an intermediary between the business which will create tons of data and the solutions and data scientists who slice and dice the data, providing the actual intelligence for these businesses.

Why did you choose Ireland for your HQ?

We have a lot of diversity inside the company. Most of the people came to Ireland for work.  The majority of our staff worked for Google. What’s beautiful about Ireland is that it’s part of Europe, it’s a relatively inexpensive place to live, it’s in the Eurozone, it’s a 3 – 4 hours short flight from anywhere in Europe.  It’s between the US and Europe, flying to NY is 6 hours and it has a small market. What you see here in Germany [at CeBIT] is that most of the promotional material is in German. Unfortunately, I don’t speak German so I can’t understand what they’re talking about and this [CeBIT] is an international event with 200.000 people coming from all over the world. Plus, Germany is a big market in which companies can  produce mainly for Germany. The diversity that can be achieved in Ireland is not needed if you aim at one large market.

We had a chance to move to Silicon Valley for example. But, in that moment, economically it made more sense to stay in Ireland and we achieved much more. In Silicon Valley everything is more expensive. There’s a lot of talent. But, the talent is hopping from job to job because there’s a large variety of jobs.

Ireland also has pretty good conditions for startups now. It’s actually great when the government helps you as a company.

What are the other significant shifts you see in IoT?

Everyone tries to play into it [IoT]. Every week you see a huge announcement of a big company going into IoT, pouring tens if not hundreds of millions of dollars into development, saying they are going to be the next big player. And it’s great, because it creates more awareness and brings more opportunities to the market. So, we definitely see more businesses entering the market because they figured out that an existing product or a new product that is IoT-enabled will be much more sellable and actually bring more revenue to them. To businesses it’s a no-brainer that IoT is positive, it’s not a fake hype. If you’re for example in British Gas and you install a thermostat which is connected, customers are more interested and their electricity bill will decrease because you are only using heating when you need to, not according to preset timers. Or take connected cars – you won’t have to bring your car to a service station to install a new feature. It can be pushed over the air. Or in health, what we now have are simple trackers, but they will move to becoming  solutions like nano robots in your bloodstream that  tell you ‘oh this fella is about to have a heart attack’. This is not not very far away.

By bringing all devices online we create an additional value not only for businesses but for people. It’s going to be securer and safer because you can prevent a lot of things before they happen. When you have things that talk to each other it’ll be easier to prevent issues.

A lot of problems in the world right now are because people or things don’t communicate in the right manner. Apart from the golden billion, there are an additional six billion people on this planet that will leapfrog. Take parts of Africa where they never had landlines, they leap-frogged into mobile phones directly.

I told you about this chip right? It’s actually a Chinese company producing that chip, it costs about 3 dollars and has an MCU which holds enough memory to make one person feel like a WiFi antenna. It works for a distance of up to 350 meters and there is enough capability to actually embed it into pretty much anything and make that thing connected. At the price point, you can put it pretty much everywhere. Take trackable clothes. Clothes producers are already thinking about how to track shorts and trousers etc.

In five years we’re going to live in a whole different world. But, you need to have security, sensibility, data protection and privacy. Ten years ago we had no phones in our pockets. And now everyone has at least one. Actually, we do much more with them than just phone people. We share everything that’s happening in our lives with our technology.

I watched a talk from Eugene Kaspersky, CEO of Kaspersky Antivirus and he said the biggest threat to the user is himself. Because the amount of information we share about ourselves is enormous. A lot of people post so much on Facebook. They don’t even understand that people outside do have access to that. How old you are, where you live, when and where you are going to travel. So something like connecting them, sending data somewhere can be actually more sensible because you can actually create hard rules to do what you actually need to do. Security is the big issue to solve around IoT.

If you could tackle any technology solvable problem existing today what would that be and why?

One of the biggest challenges for the IoT is it’s diversity.  Companies create a lot of different things that don’t talk to one another. Ideally, I would like to see everything in our lives (which is connected) be able to talk to each other without us. And, I think it’s a long shot, but, when things start talking to each other, things will be way easier. Let’s say you arrive to your house in your smart car. By the time you approach your house, the gates are opening, you park in your driveway and you walk out and the car parks itself in the garage. You come to the door and the door opens because it knows it’s you, you don’t need keys.  Once you enter, by the way your heater knew you’re coming, your system knows that it is Thursday and usually Thursday night you have a glass of wine. The fridge already knew this but also knew that you had ran out. It ordered wine for you and it’s on the way before you even arrive home. These are small things but imagine how much time we’ll free up!

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Q&A with Big Data Thought Leader, Ami Gal – Data Natives Tel Aviv 2016 https://dataconomy.ru/2016/08/15/qa-big-data-thought-leader-ami-gal-data-natives-tel-aviv-2016/ https://dataconomy.ru/2016/08/15/qa-big-data-thought-leader-ami-gal-data-natives-tel-aviv-2016/#respond Mon, 15 Aug 2016 17:17:21 +0000 https://dataconomy.ru/?p=16340 Meet Ami Gal, Data Natives Tel Aviv 2016 Speaker Ami Gal is the CEO and Founder of SQream Technologies, a big data startup based in Tel Aviv, Israel. Ami is an expert when it comes to big data and will serve as a speaker during Data Natives Tel Aviv 2016. Ami will be discussing how […]]]>

Ami

Meet Ami Gal, Data Natives Tel Aviv 2016 Speaker

Ami Gal is the CEO and Founder of SQream Technologies, a big data startup based in Tel Aviv, Israel. Ami is an expert when it comes to big data and will serve as a speaker during Data Natives Tel Aviv 2016.

Ami will be discussing how SQream’s technology leverages GPUs for large-scale data crunching. Here is what Ami has to say about how big data is being applied to drive innovation:

Q: What motivated you to start SQream Technologies?

 I’ve been working on high computing performance challenges for more than 20 years. CPU-based computing has always been limited, expensive and complex in scale. I’ve always been fascinated by the option of scaling with GPUs as they offer so many cores and parallel computing capabilities.

Before SQream, I tried to accomplish high-compute with GPUs a few times without succeeding. When the opportunity to establish a GPU database company came along, it was clear to me that I had to go for it, and turn years of brainstorming into actions and reality.

At the time SQream was founded in 2010, the common industry view was that we [SQream] were completely insane for trying to compete with big players like Oracle, with a GPU database. Today, however, it’s evident that next generation databases are running on GPUs with leading companies such as SQream and others.

Q: What is SQream doing differently from other big data companies?

 SQream’s core technology and innovation leverages GPUs for large scale data crunching. SQream brings simplicity to big data through next generation robust solutions that can tackle challenges so big, which are often considered out of scope.

Adding simplicity to such high volume challenges in the business world is one thing SQream does well, but our technology also has an impact on humankind. SQream is being utilized in areas such as:

  • Homeland Security – Where speed and scale are critical
  • Cyber Defense – For risk and threat allocation in real time
  • Research and Healthcare – Enabling an outcome of more precise and knowledgeable clinical treatments, more bandwidth and precision to genome research that will enable an educated, more precise clinical treatment in areas such as cancer and Parkinson.

Q: How is big data advancing healthcare in 2016?

Based on what we know from our clients and from the market, cancer research based on big data analysis has advanced tremendously this past year and is anticipated to accelerate even more going forward. Predictive analysis for exposing a possible disease outbreak is advancing, along with more precise, personalized medicine. These are just two examples of how big data is affecting healthcare.

SQream Technologies and other big data technology companies enable the handling and correlation of large-scale datasets originating from a wide range of sources, in order to get to insights that are more accurate and are based on a larger statistic sampling. In addition, SQream and its GPU capabilities enable much more advanced machine and deep learning algorithms on top of those datasets, accelerating research around the aforementioned by leaps and bounds.

Q: Explain Big Data’s role in genome research?

Genome research includes heavy analytical workloads that cannot be analyzed on a human level. In order to find patterns and reach educational and actionable conclusions, big data technologies are necessary. It is surprising that even today, genome research institutions are doing manual, highly time consuming singular comparisons of post-sequenced data, using a file base (yes, a file base!). This is one of the reasons why genomic research is taking years to complete.

At SQream, we addressed this problem by developing GenomeStack, a database solution that enables bioinformatics researchers to pre upload commonly used genomic databases such as 1000 Genome to SQream’s database, upload the genomic post sequence data from the research, and with a click of a button perform a simultaneous large-scale database search lasting a few seconds or minutes – as opposed to weeks or months when using a file base.

Summarizing it into one sentence – SQream can cut research time and significantly shorten time to cure – by months and even years.

Q: How will big data’s role impact the future?

 The impact of big data is already being felt in almost every aspect of our lives and its role is only growing. Thanks to big data we are able to watch television with less disruptions (network optimization), shop more effectively (with personalized ads and discounts that match our shopping history, needs and preferences) and work more effectively (with more comprehensive reports and information analyzed and delivered quickly).

Big data’s role in the future will have a major effect in areas such as our driving experience – with self-driving cars and systems that manage traffic more effectively, we will spend less time in traffic jams and hopefully be able to decrease the number of accidents.

As a result of less traffic, we will breath air that is less polluted. Instead of physically going to stores or wasting long hours online searching for the right products at optimized prices, we will have it delivered to us directly – saving us time and money.

We will be medically diagnosed and treated in a more tuned and precise manner, perhaps even alerted enough time in advance to prevent a life threatening condition. Nature disasters will be predicted and human lives will be saved as a result. These are only a few examples of how big data technologies and digitalization are changing the world.

This is exactly what’s driving SQream to continue delivering a next generation GPU database able to handle such previously unseen amounts of data constantly being generated in an accelerating speed. When I say “accelerating speed”, I am referring to the following facts – From the beginning of recorded time until 2003, the world created 5 billion gigabytes (exabytes) of data. In 2011, that exact same amount of data was created every two days. In 2013, the same amount was created every ten minutes.

In other words – the quantities of data being generated don’t only accumulate over time – as time goes on, the amounts being generated only expand. The world is spinning faster and faster and we have designed a database from scratch addressing exactly the implications and challenges that arise with it. In every challenge lies opportunity.

Join us for Data Natives Tel Aviv 2016. Save your spot by registering today!

 

 

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Q&A With MapR’s Crystal Valentine – Data Natives Tel Aviv 2016 https://dataconomy.ru/2016/08/09/crystal-valentine-data-natives-tel-aviv/ https://dataconomy.ru/2016/08/09/crystal-valentine-data-natives-tel-aviv/#respond Tue, 09 Aug 2016 09:30:35 +0000 https://dataconomy.ru/?p=16275 Meet Crystal Valentine, Data Natives Tel Aviv 2016 Keynote Speaker Crystal Valentine is VP of Technology Strategy at MapR Technologies in San Jose, California and will serve as a keynote speaker during Data Natives Tel Aviv 2016. Dr. Valentine received her doctorate in Computer Science from Brown University and was a Fulbright Scholar at the […]]]>

Crystal Valentine

Meet Crystal Valentine, Data Natives Tel Aviv 2016 Keynote Speaker

Crystal Valentine is VP of Technology Strategy at MapR Technologies in San Jose, California and will serve as a keynote speaker during Data Natives Tel Aviv 2016.

Dr. Valentine received her doctorate in Computer Science from Brown University and was a Fulbright Scholar at the University of Padua in Italy. Dr. Valentine’s unique background in computer science has allowed her to apply big data use cases to impact real-world applications.

Dr. Valentine will be discussing the impact of big data during her keynote discussion. Here is what she has to say about how data-driven technologies are driving innovation:

Q: How did you get involved in the field of Big Data?

I’m a computer scientist by training. I fell in love with computer science in college, having had a strong mathematics background but no prior experience with programming. After college, I got involved in computer science research at MIT Lincoln Laboratory and at the University of Padua in Italy as a Fulbright Scholar. Wanting to continue my research, I entered a doctoral program at Brown University where I studied discrete, combinatorial algorithms for problems in computational biology.

Since receiving my PhD, I’ve spent time both in industry and academia—first learning about big data as a consultant at Ab Initio Software and then accepting a position as a tenure-track professor at Amherst College.  Since big data is a field where the line between industry and academia is blurred, it feels natural to have moved again into my new role with MapR Technologies where I get to collaborate with brilliant thought leaders, while having an impact on real-world applications.

Q: How is big data driving the data revolution?

My philosophy is that we should be empowering individuals and organizations to leverage whatever data they have available to them—in whatever form it happens to be in.

Traditional enterprise applications were designed with a particular question in mind and then the appropriate data sets had to be identified, filtered, transformed, and loaded into a database where the query could be run to answer the question. In my mind, that whole process is unnecessarily slow and cumbersome.

Today, we’ve flipped the traditional assumptions about how to get value from data on their heads. We should be looking at all the data that’s available to us and then asking “How can I leverage all of this data?”.  That is, the data should dictate the application—not the other way around.  This is what I would call a “data-first” philosophy.

Q: Provide one use case/example on how data is being applied to create change?SparkSummit2016Luncheon_speakers-1

A great example of how data is creating real change can be seen in India’s Aadhaar Project. India is a country of 1.2B people, and the government provides $50B annually in food and subsidies for the country’s poor.

Traditionally, the task of proving one’s identity in order to receive these subsidies was an almost impossible challenge for poor citizens. Aadhaar is the Indian government’s unique identification project, built on the world’s largest biometric database.  Today, an Indian resident can prove his/her identity without paper documents any time of day in less than 200 milliseconds.

This has facilitated the receipt of subsidies by those who really need them, while drastically cutting the incidence of fraud.  The impact this project has had on the population of India cannot be understated; it’s one of the best examples of big data making a real-world impact.

Q: Do you believe that Israel is a strategic market for showcasing data-driven technologies? If so, why?

Israel is a great market from our perspective. There is a tremendous amount of innovation in Israel as a result of its strong universities and an incredible cadre of burgeoning startups. I am excited to meet with Israeli researchers and entrepreneurs to learn about how they are thinking about data and what challenges they are seeing in the field.

Q: What do you hope to gain or learn during data Natives Tel Aviv?

I’m excited to attend Data Natives Tel Aviv to learn about how data-driven applications are making an impact on other people’s lives. There are brilliant people all over the world who are looking at today’s problems through the lens of data. I’m hoping to gain new ideas and insights into how data can be leveraged by smart people to solve real-world problems.

Q: Can you offer advice for others wanting to get involved in this particular field?

I think that there are a couple ways that people are becoming involved in the big data field.  A number of senior technology folks who have been in the industry for a few decades and have witnessed the great data revolution are approaching big data with an invaluable perspective and a great set of experiences and lessons about computational architectures and algorithms.
For younger folks, the best way to get involved in the big data sector is to study computer science, computer architectures, and statistics.  These fields are all quite deep and their intersection really holds the keys to the kingdom.

Join us for Data Natives Tel Aviv 2016. Save your spot by registering today!

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“Precision medicine is going to play a key role in the future of healthcare…” – Interview with Constant Therapy’s Veera Anantha https://dataconomy.ru/2016/07/15/precision-medicine-is-going-to-play-a-key-role-in-the-future-of-healthcare/ https://dataconomy.ru/2016/07/15/precision-medicine-is-going-to-play-a-key-role-in-the-future-of-healthcare/#comments Fri, 15 Jul 2016 08:00:53 +0000 https://dataconomy.ru/?p=16104 Dr. Veera Anantha is an experienced hands-on tech entrepreneur. He has held executive positions at tech startups and Fortune 100 companies with 2 successful startup exits (Apple and Motorola). Veera has founded and launched several award winning enterprise software and consumer oriented tech products in the past decade. Veera has a PhD from Northwestern University, […]]]>

VeeraAnantha-ConstantTherapy

Dr. Veera Anantha is an experienced hands-on tech entrepreneur. He has held executive positions at tech startups and Fortune 100 companies with 2 successful startup exits (Apple and Motorola). Veera has founded and launched several award winning enterprise software and consumer oriented tech products in the past decade. Veera has a PhD from Northwestern University, BS from the Indian Institute of Technology, Mumbai, and holds several patents.


What are the top trends you see in Healthtech?
The biggest trends I see in Healthtech are the growing adoption of mobile/cloud- based technologies and use of big data analytics. With increasing adoption of these technologies, we see the potential for improved sharing of clinical data across the continuum of care, and greater patient engagement with their healthcare outside clinical settings. Ultimately, this will enable us to deliver precision medicine to everyone at scale, leading to lower costs and better population health.

What are the greatest challenges Healthtech firms face today?
As consumers, we greatly benefit from the mass adoption of technologies in our daily lives – from smart phones to tech-enabled services such as Uber. Yet, the benefits of such technology currently take a long time to help us in our healthcare. This disconnect – in the rate of adoption of technology by our healthcare system – is the biggest challenge facing Healthtech firms today. It also presents significant opportunities for Healthtech entrepreneurs and companies that can build solutions to solve this challenge.

Describe the role big data has had in delivering precision medicine to the field of rehabilitation therapy.
Precision medicine is going to play a key role in the future of healthcare, and we are already seeing early adoption in the areas of behavioral, cognitive and speech therapy. With the advent of mobile healthcare technologies, patients can now get their therapy anytime and anywhere. This creates significantly higher patient engagement, and greater/faster recovery than was possible before. Additionally, smart technology-enabled systems can monitor each patient’s progress in great detail, such as the exact recovery path of each individual.

As these big data-enabled systems collect more granular data and apply sophisticated machine learning techniques, they get better and more precise at recommending the right therapy regimen and dosage for each patient, so that it is tailored specifically to their needs. It is really exciting to see the first versions of these Precision medicine systems for rehabilitation gaining adoption among clinicians and patients today.

How has technology helped us move beyond a ‘one size fits all’ approach to therapy?

Every patient is different and requires a personalized therapy regimen, based on who they are, their diagnosis and the progress they are making towards their recovery goals. With the use of mobile technology and big data-driven analytics, it is now possible to micro-personalize therapy for patients. For patients it’s like having an expert clinician with them all the time – and best of all, this can be achieved at large scale with the use of technology.

Will mobile therapy herald a shift in in-clinic rehab or professional clinicians?
Clinicians play a critical role in providing therapy to patients. However studies have shown that it is equally important for patients to continue systematic therapy between clinic visits or after traditional therapy ends. With mobile solutions such as Constant Therapy, we enable patients to continue their therapy at home where they spend most of their time. In fact we have found that patients can receive five times the amount of therapy compared to before. Also, accuracy in language and cognitive exercises improved 15% in stroke patients with severe impairments by completing 100 exercises, and 40% for those completing 500 or more of the same exercises. Given the potential for faster and increased recovery, we expect that a combination of in-clinic rehab and systematic home therapy will become the new standard of care.

Any surprises from your study?
With the large amount of therapy data now available through the Constant Therapy platform, we are starting to gain new insights on how patient’s recover. For instance, we found that patients with severe language or cognitive impairments after a stroke have the potential to improve significantly. They should not be written off – in fact, just the opposite. They need more therapy and more time to recover their skills. Such insights drawn by analyzing large amounts of data can be extremely valuable for clinicians, families and patients to help set goals.

Are real-time predictive analytics the goal?
Definitely. The goal is to continue to improve predictive analytics, both in terms of making it more real-time and to make it more precise for each individual. Unlike brain games, we can achieve this with the use of advanced machine learning techniques applied to greater amounts of data in each sub-segment of the patient population.

Why is data-driven therapy only now becoming a reality? What can we expect over the next 3-5 years?

Widespread patient and clinician adoption of smartphone & tablets, advances in cloud computing and advanced machine learning have made data-driven therapy a reality. We are already seeing the significant potential of these technologies to augment traditional therapy.

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“Fashion needs ‘datafication’ that clearly benefits fashion.” – Interview with Data Scientist Jessica Graves https://dataconomy.ru/2016/07/01/fashion-needs-datafication-clearly-benefits-fashion-interview-data-scientist-jessica-graves/ https://dataconomy.ru/2016/07/01/fashion-needs-datafication-clearly-benefits-fashion-interview-data-scientist-jessica-graves/#comments Fri, 01 Jul 2016 08:00:48 +0000 https://dataconomy.ru/?p=16031 Jessica Graves is a Data Scientist who currently works on fashion problems in New York City. She’s worked with Hilary Mason at Fast Forward Labs and keeps in regular contact with the London startup scene. She shares her unique perspective on the datafication of Fashion. She comes from a background in visual and performing arts, […]]]>

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Jessica Graves is a Data Scientist who currently works on fashion problems in New York City. She’s worked with Hilary Mason at Fast Forward Labs and keeps in regular contact with the London startup scene. She shares her unique perspective on the datafication of Fashion. She comes from a background in visual and performing arts, as well as fashion design. In her spare time you’ll find her reading a stack of papers or studying dance.


1. What project have you worked on do you wish you could go back to, and do better?

I worked with Dr. Laurens Mets on an iteration of the technology behind Electrochaea, a device where microbes convert waste electricity to clean natural gas. My job was to translate models from electrochemistry journals into code, to help simulate, measure and optimize the parameters of the device. We needed to facilitate electron transport and keep the microbes happy. Read papers, write code, and design alternative energy technology with math + data?! I would hand my past self How to Design Programs as a guide and learn to re-implement from scratch in an open source language.

2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

Listen! If you are a data scientist, listen carefully to the business problems of your industry, and see the problems for what they are, rather than putting the technical beauty of and personal interest in the solution first and foremost. You may find it’s more important to you to work with a certain type of problem than it is to work at a certain type of company, or vice versa. Watch very carefully when your team expresses frustration in general – articulate problems that no one knows they should be asking you to solve. At the same time, it can be tempting to work on a solution that has no problem. If you’re most interested in a specific machine learning technique, can you justify its use over another, or will high technical debt be a serious liability? Will a project be leverageable (legally, financially, technically, operationally)? Can you quantify the risk of not doing a project?

3. What do you wish you knew earlier about being a data scientist?

I wish I realized that data science is classical realist painting.

Classical realists train to accurately represent a 3D observation as a 2D image. In the strictest cases, you might not be allowed to use color for 1-3 years, working only with a stick of graphite, graduating to charcoal and pencils, eventually monotone paintings. Only after mastering the basics of form, line, value, shade, tone, are you allowed a more impactful weapon, color. With oil painting in particular, it matters immensely in what order at what layer you add which colors, which chemicals compose each color, of which quality pigment, at what thickness, with what ratio of which medium, with which shape of brush, at what angle, after what period of drying. Your primary objective is to continuously correct your mistakes of translating what you observe and suspending your preconception of what an object should look like.

There are many parallels with data science. At no point as a classical realist painter should you say, ‘well it’s a face, so I’m going to draw the same lines as last time’ just like as a data scientist, you should look carefully at the data before applying algorithm x, even if that’s what every blog post Google surfaces to the top of your results says to do in that situation. You have to be really true to what you observe and not what you know – sometimes a hand looks more like a potato than a hand, and obsessing over anatomical details because you know it’s a hand is a mistake. Does it produce desirable results in the domain of problems that you’re in? Are you assuming Gaussian distributions on skewed data? Did you go directly to deep learning when logistic regression would have sufficed? I wish I knew how often data science course offerings are paint by numbers. You won’t get very far once the lines are removed, the data is too big to extract on your laptop, and an out-of-memory error pops up running what you thought was a pretty standard algorithm on the subset you used instead. Let alone that you have to create or harvest the data set in the first place – or sweet talk someone into letting you have access to it.

In addition, Nulla dies sine linea – it’s true for drawing, ballet, writing. It’s true for data science. No day without a line. It’s very difficult to achieve sophistication without crossing off days and days of working through code or theoretical examples (I think this is why Recurse Center is so special for programmers). Sets of bland but well-executed tiny piece of software. Unspectacular, careful work in high volumes raises the quality of all subsequent complex works. Bigger, slower projects benefit from myriads of partially explored pathways you already know not to take.

Also side notes to my past self: Linux. RAM. Thunderbolt ports.

4. How do you respond when you hear the phrase ‘big data’?

Big data? Like in the cloud? Or are we in the fog now? Honestly the first thing I see in my mind is PETABYTES. I think of petabytes of selfies raining from the sky and flowing into a data lake. Stagnant. Data-efficient AI is all the rage — less data, more primitives, smarter agents. In the meantime, optimizing hardware and code to work with large data sets is pretty fun. Fetishizing the size of the data works well …as long as you don’t care about robustness to diverse inputs. Can your algorithm do well with really niche patterns? What can you do with the bare minimum amount of data?

5. What is the most exciting thing about your field?

Fashion is visual. It’s inescapable. Every culture has garb or adornment, however minimal. A few trillion dollars of apparel, textiles, and accessories across the globe. The problems of the industry are very diverse and largely unsolved. A biologist might come to fashion to grow better silk. An AI researcher might turn to deep learning to sift through the massive semi-structured set of apparel images available online. So many problems that may have a tech solution are unsolved. Garment manufacturing is one of the most neglected areas of open source software development. LVMH and Richemont don’t fight over who provided the most sophisticated open-source tools to researchers the way that Amazon and Google do. You can start a deep learning company on a couple grand and use state-of-the-art software tools for cheap or free. You cannot start an apparel manufacturing vertical using state-of-the-art tools without serious investment, because the climate is still extremely unfavorable to support a true ecosystem of small-scale independent designers. The smartest software tools for the most innovative hardware are excessively expensive, closed-source, and barely marketed — or simply not talked about in publicaly accessible ways. Sewing has resisted automation for decades, although is finally now at a place now were the joining of fabrics into a seam is robot-automatable with computer vision used on a thread-by-thread basis to determine the location of the next stitch.

High end, low end, or somewhere in between, the apparel side of fashion’s output is a physical object that has to be brought to life from scratch, or delivered seamlessly, to a human, who will put the object on their body. Many people participate in apparel by default, but the fashion crowd is largely self-selected and passionate, so it’s exciting (and difficult) to build for such an engaged group that don’t fit standard applications of standard machine learning algorithms.

6. How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

Artists learn this eventually: volume of works produced trumps perfectionism. Even to match something in classical realism, you start with ridiculous abstractions. Cubes and cylinders to approximate heads and arms. Break it down into the smallest possible unit. Listen to Polya, “If you can’t solve a problem, then there is an easier problem you can solve: find it.”

As for when to finish? Nothing is never good enough. The thing that is implemented is better than the abstract, possibly better thing, for now, and will probably outlive its original intentions. But make sure that solution correlates thoroughly with the problem, as described in the words of the stakeholder. Otherwise, for a consumer-facing product or feature, your users will usually give you clues as to what’s working.

7. You spent sometime as a Consultant in Data Analytics. How did you manage cultural challenges, dealing with stakeholders and executives? What advice do you have for new starters about this?

Be open. Fashion has a lot of space for innovation if you understand and quantify your impact on problems that are actually occurring and costing money or time, and show that you can solve them fast enough. “We built this new thing” has absolutely nothing to do with “We built this useful thing” and certainly not “We built this backwards-compatible thing”. You might be tempted to recommend a “new thing” and then complain that fashion isn’t sophisticated enough or “data” enough for it. As an industry that in some cases has largely ignored data for gut feelings with a serious payoff, I think the attitude should be more of pure respect than of condescension, and of transitioning rather than scrapping. That or build your own fashion thing instead of updating existing ones.

8. You have worked in fashion. Can you talk about the biggest opportunities for data in the fashion industry. Are there cultural challenges with datafication in such a ‘creative industry’.

Fashion needs ‘datafication’ that clearly benefits fashion. If you apply off-the-shelf collaborative filtering to fashion items with a fixed seasonal shelf life to users that never really interact with, you’re going to get poor results. Algorithms that work badly in other domains might work really well in fashion with a few tweaks. NIPS had an ecommerce workshop last year, and KDD has a fashion-specific workshop this year, which is exciting to see, although I’ll point out that researchers have been trying to solve textile manufacturing problems with neural networks since the 90s.

A fashion creative might very well LOVE artificial intelligence, machine learning, and data science if you tailor your language into what makes their lives easier. Louis Vuitton uses an algorithm to arrange handbag pattern pieces advantageously on a piece of leather (not all surfaces of the leather are appropriate for all pattern pieces of the handbag) and marks the lines with lasers before artisans hand-cut the pieces. The artisans didn’t seem particularly upset about this.

The two main problems I still see right now are the doorman problem and fit. Use data and software to make it simple for designers of all scales to adjust garments to fit their real markets instead of their imagined muses. And, use as little input as possible to help online shoppers know which existing items will fit. Once they buy, make sure they get their packages on time, securely, discreetly.

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What Recruiters and Hiring Managers Are Looking for in a Data Scientist https://dataconomy.ru/2016/06/27/recruiters-hiring-managers-looking-data-scientist/ https://dataconomy.ru/2016/06/27/recruiters-hiring-managers-looking-data-scientist/#comments Mon, 27 Jun 2016 08:00:01 +0000 https://dataconomy.ru/?p=16037 Lindsey Thorne, Manager of the Open Source & Big Data Practice at Greythorn Lindsey has been in HR and recruiting for more than 12 years, and after narrowing her focus to the open source and data science market in 2012, she’s built a reputation for being the one recruiter “inside” the industry. Mary Kypreos, Recruiting […]]]>

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Lindsey Thorne, Manager of the Open Source & Big Data Practice at Greythorn
Lindsey has been in HR and recruiting for more than 12 years, and after narrowing her focus to the open source and data science market in 2012, she’s built a reputation for being the one recruiter “inside” the industry.

Mary Kypreos
Mary Kypreos, Recruiting Manager of the Open Source & Big Data Practice at Greythorn
Mary is lucky enough to combine her passion for hiring the best talent with her love of big data, and one of her specialties is finding data scientists (and actually knowing how to).

 


“What makes me stand out as a candidate for a data scientist role?” We’ve heard this question asked many different ways – over beers at a Meetup or in a Reddit forum. Whether you’re a new data scientist or just researching the career path to see if it’s for you, it’s important to understand the basics of growing your career. We spoke to Lindsey Thorne and Mary Kypreos who answer some common questions about what companies are looking for in a data scientist, how to stand out as a candidate, and the best ways to start networking.

When a recruiter or hiring manager is looking for a data scientist, what indicates that they may be a good candidate?

This is a tricky question, because it completely depends on what type of data scientist the company is looking for—there is no standard definition (though the book Analyzing the Analyzers has four loose categories that are helpful).

In general, most of our clients look for an individual who graduated with a technical or quantitative PhD, which could be anything from mathematics or statistics to physics or computational linguistics. In addition, they are usually looking for someone with at least one industry position outside their PhD—without that experience, you’re often considered a junior candidate due to a potentially longer ramp-up time in the role.

As a candidate, you’ll also want to emphasize any hands-on engineering experience you possess, since data science teams and engineering teams continue to work closely to achieve a company’s goals.

What kind of experience are employers looking for in data scientists?

This depends on what kind of data scientist the company needs: statistics focused? Machine learning focused? Hands-on business case experienced?

The experience needed for each of these would be different. For example, not every company is looking for a data scientist with a PhD—that requirement is often removed if they want someone with 5-10 years of relevant experience working in the same space. Other companies, however, do need a candidate who can work through ambiguous problems using a scientific method-like process, which is often learned through doctoral work. Just as many companies need someone who is equally strong in analytics and engineering—and those skills can be gained from a variety of backgrounds and degrees, such as starting out as a statistician who takes on an engineering role, or someone with a bachelor’s degree in a science-related field who earns an advanced degree in computer science, etc.

So before tailoring your resume or cover letter, you need to ask, “What does this company need their data scientist to do?”

How can a candidate early in their career stand out?

Get the hands-on engineering experience first; this way, on average, most companies will know that you come to the table with more than just “big picture” concepts. They should know that you also understand the fundamental challenges their teams face. We’ve seen more and more companies looking for data scientists with both engineering and analytical skills, and this will set you ahead of the curve if the industry continues to move in that direction.

If you do nothing else, find a way to gain practical experience that is relevant to the industry or position you’re interested in. Whether you are graduating with your PhD soon, taking courses to supplement your education, or looking to transition your career, practical experience will always give you an edge over a candidate without it.

In which communities should data scientists network and be active?

There is no one community out there that is better than another, but there surely is a community that would interest you more than another. Be sure to spend some time researching ones that you find interesting and where you think you could contribute.

Check out Meetup.com and other professional networking groups in your area, and regularly attend and become well known in your group! Follow trends, people, and companies you think are doing important things on Twitter, Facebook, Stack Overflow, etc., and—more importantly—contribute to the conversation. You’d be surprised how many people make friends and professionally network over the internet without ever meeting in person!
We also recommend you attend and even submit talks to any conferences or training sessions in your area, especially if they could give you practical experience (sometimes these events have scholarships or subsidies available). Once you’ve found interesting groups and communities, it’s important that you become an active and visible addition to the community, no matter the medium.

At the end of the day, it’s important to remember that every company’s requirements for a data scientist will be different—and you will not always be the right match for every opportunity. That is not a reflection on your skills or experience, but on the wide range of areas in which people labeled “data scientists” work. Ultimately, you need to understand your strengths and what you have to offer an employer, and communicate those well. If you aren’t sure how to do that, working with (ahem) a recruiter that specializes in data science roles might be your best bet.

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“From my point of view, FinTech won’t exist as a stand alone industry…” – Interview with SolarisBanks’ Peter Grosskopff https://dataconomy.ru/2016/06/24/point-view-fintech-wont-exist-stand-alone-industry-interview-solarisbanks-peter-grosskopff/ https://dataconomy.ru/2016/06/24/point-view-fintech-wont-exist-stand-alone-industry-interview-solarisbanks-peter-grosskopff/#respond Fri, 24 Jun 2016 08:00:06 +0000 https://dataconomy.ru/?p=15898 As CTO, Peter leads the tech team of solarisBank. He’s the former CTO of HitFox Group and software engineering company Zweitag. Peter is experienced in building tech-heavy startups and fintechs.   Tell us about the mission of SolarisBank  and why you chose Berlin as your headquarters? We’re offering a horizontally-integrated Bank as a Platform (BaaP) […]]]>

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As CTO, Peter leads the tech team of solarisBank. He’s the former CTO of HitFox Group and software engineering company Zweitag. Peter is experienced in building tech-heavy startups and fintechs.

 


Tell us about the mission of SolarisBank  and why you chose Berlin as your headquarters?

We’re offering a horizontally-integrated Bank as a Platform (BaaP) with the sole purpose of enabling the actual products and services that people want to have. We’re essentially saying to all types of companies, “we’ve done the hard work of getting a banking license so you don’t have to.” If you partner with SolarisBank it’s really simple to enable a financial solution, so come build on our platform and realize your vision.

Berlin is the obvious choice for a tech company for three reasons. First of all, you’re close to all potential partners – from fintechs, to early stage startups and established digital players. Second, we love the inspiring entrepreneurial atmosphere you can only get in Berlin. Since the majority of people at solarisBank are entrepreneurs by heart, we wouldn’t want to build our company elsewhere. Third, as a tech company you need the best developers out there and need to offer them an enticing and enriching environment to work in.

What type of tech problems are you trying to solve at SolarisBank?

We’re helping digital companies invent and build business models in the financial sphere and technology is our enabler. Currently many fintechs and businesses who would like to offer financial services need a banking partner. That partner is often technologically inept and hails from an archaic mindset completely different to that of respective startups or digital businesses. Our goal is therefore straightforward: make a partnership with us as simple as as possible. And since we’re a tech partner with a banking license this has to start with our technology. We’ve built a state of the art API, which means less complexity for the engineers of our partners and a fast, straightforward integration with their systems.

What are significant shifts you have seen in the industry?

The most obvious and well discussed shift is the unbundling of banks. Customers these days are looking for the best financial services out there rather than a specific bank offering financial services. They simply expect the best digital service to be found via Google. Whatever institution offering that solution is no longer a priority. Traditional banks rely on business models which have been around for decades. Banks did not see the relevance of the digitalization of society for quite some time. This is where we come in, we use the business models of traditional banks but offer them on a platform with additional solutions to serve the digital society. This of course is the first step, our medium term goal is to provide services that exist in the current finance world and to make these accessible to digital companies. But we are also doing research on innovative concepts like blockchain because they will disrupt the networks and the way transactions are done in the future.

As the CTO, if you could tackle any technology-solvable challenge existing today, which would it be and why?

I know that this sounds quite practical but it’s true: I’m striving to build a scalable environment in an organizational as well as technical way.

What technologies do you use at SolarisBank?

We are mostly based on pure Ruby as a programming language, pretty independent from frameworks. In addition we use Grape to create APIs. Elixir which is quite a modern programming language and complimentary to Ruby complements the picture. So the idea is basically that whenever Ruby is not suitable, Elixir is a good addition. We have a polyglot mindset and want to create a mix of technologies – if we stick to one language we don’t have innovation inside the tech stack. If you introduce new technologies you increase your speed and agility and you attract the right staff – good developers want to use new technologies and have new challenges, so when you stick with modern tech you also attract good people.

What do you think the future of FinTech looks like 5 years from now?

From my point of view, FinTech won’t exist as a stand alone industry but will be integrated or connected with every business model on the internet. FinTech firms can be part of any company and serve a share of it. There are big opportunities for B2B companies to improve processes and define new products. I think FinTech is a bit of a buzzword, but to be honest it has always been there – for example, banks have been using computers for decades and were basically also just a company serving a service. What might be new and is way more important from our side is the ‘tech’ part of FinTech. Chris Skinner has been using the term “TechFin” as he says the emphasis should be on the tech component which is more important than the financial element and I completely agree with that sentiment.

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“Spark has the potential to be as transformational in the computing landscape as the emergence of Linux…” – Interview with Levyx’s Reza Sadri https://dataconomy.ru/2016/06/07/spark-potential-transformational-computing-landscape-emergence-linux-interview-levyxs-reza-sadri/ https://dataconomy.ru/2016/06/07/spark-potential-transformational-computing-landscape-emergence-linux-interview-levyxs-reza-sadri/#respond Tue, 07 Jun 2016 08:00:17 +0000 https://dataconomy.ru/?p=15917 Reza Sadri is the CEO of Levyx, the creators of high-performance processing technology for big-data applications. Prior to Levyx, Sadri was the CTO for software at sTec, which was acquired by Western Digital Corporation in 2013.   What is the potential of Spark? How far is the market from realizing that potential? Spark has the […]]]>

"Spark has the potential to be as transformational in the computing landscape as the emergence of Linux..." - Interview with Levyx's Reza Sadri
Reza Sadri is the CEO of Levyx, the creators of high-performance processing technology for big-data applications. Prior to Levyx, Sadri was the CTO for software at sTec, which was acquired by Western Digital Corporation in 2013.

 


What is the potential of Spark? How far is the market from realizing that potential?

Spark has the potential to be as transformational in the computing landscape as the emergence of Linux back in the 1990’s thanks to strong buy-in, a growing developer community and an application ecosystem that will help it reach this potential.

I am not the only one that has this view. IBM has recently endorsed Spark and has committed to throw IBM-scale resources behind it. That endorsement, which called Spark the “most important new open source project in a decade,” will see IBM incorporate Spark into its platforms and cloud solution with the help of 3,500 IBM researchers, while the company also committed to educating 1 million data scientists and data engineers on Spark.

Outside that, the Spark development community is growing very quickly having already overtaken the Hadoop developer count, a precursor to more adoption among end users.

In addition, a huge application ecosystem has been built around Spark, which will assuredly expand its growth. In contrast, Hadoop failed to gain much support from application builders which may have hindered its ability to reach its full potential.

What pain points do organizations have to navigate in analyzing big data and, specifically, utilizing Spark?

Most enterprises are trying to do more with less. That is, organizations have to find a way to analyze mounting amounts of data using the same resources.

Typically this has led organizations to inefficiently scale out of their infrastructure (i.e., throw hardware at the problem). However, as data collection grows, enterprises are finding it increasing difficult to support this type of expansion.

Implementing Apache Spark as a big data platform is a major step in helping those companies, but it does not resolve the issue of cost. Apache Spark, while good at high-speed analysis of data on a large scale, still relies on memory to achieve its high-performance. That memory is inefficient and expensive – particularly since many companies simply add costly servers to ameliorate the problem. The end result: deploying Spark is usually prohibitive.

At Levyx, we created a unique way to distribute Spark (called LevyxSpark) that changes the way a system or a compute node’s available resources, including memory, tackle the analytics.

Analyzing big data requires both robust hardware and software solutions. How much of big data adoption is being driven by innovations in hardware versus software?

It is difficult to say which side is making advances faster or that are more meaningful, the hardware side or the software side. The reality is that engineers and scientists on both sides have made phenomenal advances in their respective fields. However, these development areas are often siloed off from one another to such an extent that applying these separate innovations in a common infrastructure leads to an underutilization of the systems’ resources.

What advice would you give to companies that want to start using Spark?

The benefits of Apache Spark are well-publicized and easy to understand – a faster, more manageable, full-featured open-source big data analytics platform.

But for companies that actually want to implement Spark or are in the early stages of using it already, the key for them will be to understand what the all-in costs to deploy it will be. Since Spark derives much of its real-time speed by relying heavily on memory, the conventional scale-out methods to deploy are usually cumbersome and complicated. Furthermore, after factoring in the associated costs of power, connectivity, networking, management/maintenance, and additional space, the costs of implementations are often underestimated and could be potentially prohibitive.

That’s why Levyx believes it has a complementary enabling technology for Apache Spark that makes deployments simpler – ultimately helping to stimulate adoption. By allowing each Spark node to process more data using less memory and more Flash, we can shrink the node count (i.e., less power, space, and total TCO) and make deployments more affordable for both small and large scale customers.

What industries are not using Spark, but should to optimize efficiency?

While most industries have just started to adopt Spark, we would like to see key areas of Life Sciences become more frequent users of not only Spark, but other cutting edge big-data technologies. The processes and methods by which huge amounts of data are being analyzed and correlated in the biotech and healthcare industries could really benefit from a broad-based platform like Apache Spark.

And further, this could have an exciting impact on all of our lives. Imagine, for example, that all available health, fitness, biological, and genetic data could somehow be more readily interpreted and used to solve some of medicine’s toughest problems. We will leave the biotech firms to answer those questions, but we believe that leveraging the benefits of Spark in a cost-effective process could lead to huge breakthroughs.

What is the future of data among enterprises?

Data science is going to become more focused on “response time.” Many current applications are going to move toward real-time and interactive use cases. As people build applications to analyze data, they should notice that the key differentiation will be in speed.

The great thing is that new improvements in hardware and system software is making it possible and economically feasible to achieve low-latency data processing even with very large amounts of data.

Therefore, I think that new entrants in data science field have the ability to be more ambitious than ever before. It has become easy to adjust infrastructure demands, opening up the door to what is both possible and affordable.

image credit: jonas maaloe jespersen 

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“The application of informatics will continue to grow with access to more data and technology, enhancing human health innovation…” – Interview with Vium’s Joe Betts-Lacroix https://dataconomy.ru/2016/06/03/application-informatics-will-continue-grow-access-data-technology-enhancing-human-health-innovation-interview-viums-joe-betts-lacroix/ https://dataconomy.ru/2016/06/03/application-informatics-will-continue-grow-access-data-technology-enhancing-human-health-innovation-interview-viums-joe-betts-lacroix/#respond Fri, 03 Jun 2016 08:00:41 +0000 https://dataconomy.ru/?p=15775 Joe Betts-Lacroix is the CTO and Co-Founder of Vium. He is a proven entrepreneur and as an inventor holds over 80 granted and pending patents in fields ranging from biophysics and safety systems to antennas, thermal systems, user interfaces and analog electronics. He was the primary technical founder of OQO that in 2005 built the […]]]>

Joe Betts-Lacroix 1 (1)

Joe Betts-Lacroix is the CTO and Co-Founder of Vium. He is a proven entrepreneur and as an inventor holds over 80 granted and pending patents in fields ranging from biophysics and safety systems to antennas, thermal systems, user interfaces and analog electronics. He was the primary technical founder of OQO that in 2005 built the smallest full-powered, full-featured personal computer, according to Guinness World Records. Joe holds a Harvard A.B., MIT S.M. and Caltech research fellowship.


What is Vium’s mission and why did you choose to be located in Silicon Valley?

Dr. Tim Robertson and I co-founded Vium in 2013 to build a better animal testing facility called a ‘vivarium,’ but quickly realized our approach had wide-spreading implications. Our mission became transforming how preclinical in vivo drug testing is done through the application of technology to accelerate drug development, create a more humane environment for research animal subjects, and ultimately improve human health.

Why? Early drug experiments, once conducted exclusively in the labs of academia and drug companies, are increasingly being outsourced, distancing researchers from their work. In addition, the process of in vivo studies is outdated, with technicians manually observing animals and recording data such as an animal’s weight, food intake and clinical signs of pain and distress. This process takes a long time and leaves room for much ambiguity, slowing down the discovery of novel medicines and driving up costs of drug development. It also stresses the animals.

The reason we’re based in Silicon Valley is simple. Tim and I, both serial entrepreneurs and inventors, live in the valley. In fact, we initially met as neighbors.

What does Healthtech mean to you and what kind of growth do you foresee for this industry?

Healthtech companies target applications at points along the spectrum of health and wellness, e.g. hospital workflow, home monitoring, and consumer wellness applications. Vium operates within preclinical drug research in the area of living informatics, a type of biomedical informatics. We have created the first living informatics platform, which applies life sciences, digital technology and large-scale interpretive methods to living systems. Robust, connected and multi-dimensional data sets are continuously generated, providing researchers with richer understanding and deeper insights of experiments. The application of informatics will continue to grow with access to more data and technology, enhancing human health innovation along the way.

You say that “Vium empowers biomedical investigators with technology that accelerates the preclinical drug discovery and development pipeline.” – could you expand on the technology that is being used?

Vium has developed hardware and software that for the first time enable researchers to design, purchase, run, analyze and reproduce in vivo studies online. In the Digital Vivarium™ via the online Research Suite researchers can continuously monitor and record animal motion, respiration, physiology, behavior as well as environment and husbandry from any computer, tablet or smartphone. The vivarium transmits petabytes of data to the Vium Cloud where data is stored, computed and analyzed using modern algorithms that produce meaning and insights. Vium’s technology also provides a more humane, natural environment for research subjects, which will set a new standard for animal care. What Vium has done is to empower scientists to focus on research and discovery of novel drug candidates, not routine process.

Are there any Data Science applications that Vium specifically uses? If so, why do you see these being so beneficial?

Vium integrates techniques and theories from many fields: mathematics, statistics, information science, and computer science, including signal processing, probability models, machine learning, neural networks, statistical learning, data mining, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, and high performance computing.

Vium’s technology delivers multiple improvements over traditional in vivo research, such as massive data sets, real-time and retrospective analysis, high throughput of multiple compounds in parallel, sensitivity to subtle animal signs and behavior, and less error-prone and stressful human-animal interactions.

As a result, researchers can make faster and better informed decisions about which compounds will have a greater probability of success, then accelerate their development and reduce overall R&D costs.

Do you think that there is not currently enough quality data or technologies to extract quality data for researchers?

Both are true.

Traditional in vivo research collects very little data, usually amounting to less than a kilobyte, consisting of a few weights, dates, manual observations and blood numbers. Occasionally there are some images from histopathology.

Vium collects gigabytes per animal per day. In a way we are creating our own challenges, which we uniquely have to solve using tools from the tech world, e.g. advanced IT infrastructures and data science. We are fortunate to be launching Vium at just the right time, i.e. when technologies have recently become available from other industries.

Subjective measurements in traditional in vivo research are generally small data since that is what humans are capable of recording. One of the key values we bring to larger data streams is objectivity, which translates into accuracy, repeatability, and scalability – all of which will help to speed the development of new therapies.

Do you think the onus is on the government, private institutions, universities, or consumers to push for the expansion of data being used to develop better technologies for the health industry?

As private institutions drive the development of new technologies or the translation of science from universities, the public’s role is to help recognize value. Government watches and applies careful regulation, where and if necessary, against a cost of slowing down innovation. Different aspects of the overall therapeutic pipeline require varying amounts of regulation, with stricter regulations the closer applications are to humans. Because mice and rats are substantially unregulated by governments, this sector can innovate rapidly. Vium is fully accredited by AAALAC International and consistently improves on both industry and government standards of animal welfare.

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“We believe that personalization is the key word for FinTech this year”-Interview with Meniga’s Georg Ludviksson https://dataconomy.ru/2016/05/26/believe-personalization-key-word-fintech-year-interview-menigas-georg-ludviksson/ https://dataconomy.ru/2016/05/26/believe-personalization-key-word-fintech-year-interview-menigas-georg-ludviksson/#respond Thu, 26 May 2016 08:00:10 +0000 https://dataconomy.ru/?p=15783 Serial entrepreneur Georg Ludviksson co-founded Meniga in 2009in the wake of the global financial crisis in Iceland. Georg has spent 20 years founding, building and managing global software start-ups. Georg holds an MBA degree from Harvard Business School with emphasis on Entrepreneurship and Finance. He also holds a BS degree in software engineering from the […]]]>

"We believe that personalization is the key word for FinTech this year"-Interview with Meniga's Georg Ludviksson
Serial entrepreneur Georg Ludviksson co-founded Meniga in 2009in the wake of the global financial crisis in Iceland. Georg has spent 20 years founding, building and managing global software start-ups. Georg holds an MBA degree from Harvard Business School with emphasis on Entrepreneurship and Finance. He also holds a BS degree in software engineering from the University of Iceland.


WHAT IS YOUR MISSION STATEMENT?
Helping people become smarter consumers by transforming the way banks and advertisers use data.

WHERE ARE YOU HEADQUARTERED & WHY?
Meniga was founded in Reykjavík, Iceland but is headquartered in London, UK. We chose London because it is Europe’s central hub for the retail banking & FinTech industry as well as a thriving, creative and innovative environment. Geographically it is closer to our current clients and gives us greater access to potential prospects and partnerships.

WHAT TYPE OF PROBLEMS ARE YOU TRYING TO SOLVE AT YOUR COMPANY?
Banks sit on a huge pile of personal finance data that contains information that is mostly not being utilized to create value. By applying data science techniques, Meniga enables banks to drastically improve their customer experience, create new revenue streams and cut costs.

One of the biggest problems in the FinTech industry is in accessing the data required to properly test and measure the success of solutions around the world. Meniga is in the unique position of having 25% of all Icelandic households registered in our stand-alone platform. We therefore have the freedom to try, test and measure as much as we want in Iceland with a significant population. We feel this gives us an edge when it comes to understanding what people like and what people don’t like which we take into consideration when moving forward. We also work in more than 17 markets and there are great differences in the availability, quality and structure of data between different countries.

Meniga consolidates and enriches personal finance data with data science from multiple sources, including accounts, transactions, assets, liabilities, bills and CRM to create personal finance management tools and personalized relevant messaging for banks. Meniga’s Card Linked Marketing solutions also use data science to allow merchants to send personalized offers to relevant group of bank customers.

WHAT ARE THE SIGNIFICANT SHIFTS THAT YOU SEE IN THE INDUSTRY?
We have been observing a great number of FinTech companies arising from all over the world in the last years. They will often start off with one solution or one purpose but if they manage to firmly establish themselves and continue grow they will quite often move towards using big data to advance their product offering. This is an indication of the true magic that his hidden in understanding and properly using big data. However it takes great deal of experience and skill to properly work with data science to create real value and insight.

IF YOU COULD TACKLE ANY TECHNOLOGY-SOLVABLE CHALLENGE EXISTING TODAY, WHICH WOULD IT BE – AND WHY?
We would tackle exactly the one we are working on currently. That challenge is to use big data to optimize the customer, bank and merchant relationship, this will drastically improve the financial lives of people globally. It will also help banks improve their customer engagement with a personalized experience, create new revenue streams and cut costs, and finally help merchants reach the right customers and stop wasting money on aimless marketing.

WHO DO YOU THINK WILL BE THE MOST INFLUENTIAL FIGURES (OR COMPANIES) IN FINTECH IN 2016?
We believe Meniga will definitely make some noise in 2016 with our data-driven solutions. Then there are many very interesting companies working on cool things all over the world. Betterment — a New York City-based startup that provides automated investment services and personalized advice, Affirm — a San Francisco based installment loans company helping users finance large e-commerce purchases, Transferwise — a peer-to-peer money wire platform and Swedish online payment business Klarna are all examples of companies that might influence the industry with their solutions.

WHAT ARE SOME KEY HURDLES IN THE FINTECH INDUSTRY THAT YOU’RE EXPERIENCING? AND HOW DO YOU SEE DATA SCIENCE APPLICATIONS HELPING SOLVE THESE HURDLES?
One of the key hurdles we see in the FinTech industry is that banks often don’t know how to properly consolidate and enrich their data to start creating valuable insights for decision making and improved services. This is where we believe data science applications are game changers by providing a powerful solution suite that can do this for them along with insightful consultation.

Data science is fact-based instead of intuition-based and therefore superior for financial decision making and cutting edge solutions. We also use it internally a great deal to improve Meniga as a company by measuring our performance and testing our products.

CAN YOU TELL US WHAT YOU THINK THE FINTECH INDUSTRY WILL LOOK LIKE 5 YEARS FROM NOW?!
The FinTech industry will only continue to grow as understanding of the industry and the technology continues to grow with new companies emerging every year and from all over the world. There are a lot of opportunities and issues that can be solved with clever solutions. However there will likely come a point where we will have too many FinTech companies. Most of the main industry issues have been ironed out and then it will really come down to the ones that know how to use big data to broaden their product portfolio, offer something new and eventually succeed. Good data sets will help banks improve their customer experience, create new revenue streams and cut costs.

We believe that “personalization” is the key word for FinTech this year. Customers want a personalized experience and in this day and age we believe they deserve just that! All our solutions provide a personalized experience for customers; we believe that in the instance of banks this will separate those who prevail from those that might become obsolete. Banks need to realize that they are not just competing against each other anymore but rather against the FinTech companies of the world which are squeezing themselves between the bank and their customers. If banks don’t take action by providing a superior, above all personalized, customer experience, then they might start bleeding customers if they aren’t already.

image credit: Yodlee NEXT

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Behind the Scenes of Dataconomy: Meet Elena! https://dataconomy.ru/2016/05/25/behind-scenes-dataconomy-meet-elena/ https://dataconomy.ru/2016/05/25/behind-scenes-dataconomy-meet-elena/#respond Wed, 25 May 2016 08:01:48 +0000 https://dataconomy.ru/?p=15817 Elena Poughia Managing Director Originally from Greece (the country with 6,000 islands) Calling herself a Berliner for just over a year Her motto “When there is will, there is a way” as she believes life is about hustling with a purpose (ideologist at heart) but really, in it for the ride. What brought you to Dataconomy […]]]>

Behind the Scenes of Dataconomy: Meet Elena!
Elena Poughia
Managing Director

Originally from Greece (the country with 6,000 islands)
Calling herself a Berliner for just over a year
Her motto “When there is will, there is a way” as she believes life is about hustling with a purpose (ideologist at heart) but really, in it for the ride.


What brought you to Dataconomy in the first place?

Chance brought me to Dataconomy – I was interested in data science because I was working a lot as an art curator with artists who critically examined the issue of “big data” related to data privacy, data security, the morals behind using big data and the importance of it from an anthropological, philosophical and sociological perspective. I was looking for a job whilst applying for PhD funding to go to New School to do research on “IoT & art practice.” I then started freelancing for Dataconomy and realised that a hands on approach is more for me. I really liked the team, the vibe, the purpose and the mission and so here I am. 🙂

What do you do at Dataconomy?

I do a lot of things but I learn even more – I started with events working on our meetups, seeing the community grow from zero to 25K and expanding into 19 international cities. Then a small team of 5 people put on our first Data Natives conference last year and I had to learn to do things I’ve never done before, again. We brought together 60+ speakers from around the world in front of 500 people and we did all this just a year after our existence – it involved blood, sweat and tears from all of us, but it was really worth it and I m really proud of the accomplishment. Now, after working for Dataconomy for a year and a half, I feel lucky to be managing it and learning even more. Let’s say, excel spreadsheets are my best friend now (ha!).

How do you use data driven technologies in your day to day life?

I don’t use or fully utilize technology as much as I’d want to. I am fascinated by cyborgs (read Donna Harraway cover through cover) and I’m waiting for the day humans will “hang out” with robots and everything will be working with sensors. For the time being, I am pushing myself to learn python by the end of 2016 (New Year’s Resolutions) and to read and learn about new advancements in tech as much as possible.

Dataconomy puts on several events on a yearly basis with the philosophy of being a “Data Native” – what does this mean to you?

I like that it’s our philosophy and I see it as the future – we grew up entrenched in technology, it’s in everybody’s cultural identity from the way we communicate, we meet, we document, we learn it all happens online and its all stored in the cloud. We have these devices now to assist us, to improve our lives, to make us better people, as long as we use it for good. I am weary about the human behind the machine and I hope the philosophy of being a “Data Native” could highlight the good and not the bad of what being that entails.

What topics do you like to read on Dataconomy?

I’m fascinated by bitcoin and blockchain technology at the moment and very curious about the future of health tech. I like scoping out what is out there and interviews with very interesting people who have done great things: like Kirk Borne, number 1 big data influencer for 2016, or fascinating startups who are disrupting the current status quo.

Dataconomy is based in Berlin, what is your favourite thing about living in such a booming tech town?

The vibe: people are very creative, innovative, open to meet and talk but yet, they have a life. They will work till late at the startup office and then will go and party it out at Berghain. This city combines my two favorite things in the world: art and tech :).

Touch base with her on LinkedIn or by email at elena@dataconomy.ru.

image credit: Sebastian Thewes

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“I expect the market to become much more fragmented as some areas of FinTech”… – Interview with LaterPay’s Cosmin Ene https://dataconomy.ru/2016/05/19/expect-market-become-much-fragmented-areas-fintech-interview-laterpays-cosmin-ene/ https://dataconomy.ru/2016/05/19/expect-market-become-much-fragmented-areas-fintech-interview-laterpays-cosmin-ene/#respond Thu, 19 May 2016 08:00:47 +0000 https://dataconomy.ru/?p=15670 Cosmin Ene is an entrepreneurial founder with an excellent first-hand understanding of the life cycle of entrepreneurial ventures, which he has accumulated over 18 years. Between 2005 and 2009, Ene was co-founder and managing director of DELUXE Television. Prior to this, he was analyst and associate at TecVenture Partners. In 2010 Ene started LaterPay, a […]]]>

Cosmin Ene
Cosmin Ene is an entrepreneurial founder with an excellent first-hand understanding of the life cycle of entrepreneurial ventures, which he has accumulated over 18 years. Between 2005 and 2009, Ene was co-founder and managing director of DELUXE Television. Prior to this, he was analyst and associate at TecVenture Partners. In 2010 Ene started LaterPay, a micropayment enabler.

 


The idea of LaterPay started over sushi, when Cosmin Ene realized that people like to “cherry-pick” and he thought this need was not yet introduced on the internet where you have to pay for everything at once.

And so LaterPay was born in Munich, Germany with a mission of being a one-click solution to enable digital purchases, making it easy for people to purchase content without registration and cost, initially. By doing so LaterPay empowers the paid content businesses across every open ecosystem on the internet.

What type of problems are you trying to solve at LaterPay? 

To make people purchase paid content in an easy and convenient way. No one else is currently doing this, everyone is adopting a “safe solution” for themselves. Where as we are trusting people first to consume content and pay later.

What are the significant shifts that you’ve seen in the industry? 

Plain and simple, subscriptions as the one and only model for publishing doesn’t work. Given the fact that they are not working, companies like us are coming up with new ideas – most users don’t want traditional subscriptions and so you can start offering content to them in different packaging, e.g. as individual content.

If you could tackle any technology-solvable challenge existing today, which would it be and why? 

I would tackle the problem of the annoying upfront registration and payment processes, which is exactly what we do. We want to make it super easy for users to consume content. People don’t have a problem paying for content, they are just annoyed to register and pay up front for content. People want to be treated as they are in the real world where they get served first and pay later – after receiving and accepting the benefits. Bringing this behavior to the Internet world is a challenge that I am addressing in order to make a difference!

Who do you think will be the most influential figures (or companies) in FinTech in 2016? 

Companies that are democratizing investments, for example companies like Scalable Capital that make investment services affordable for everyone. And other companies that offer services that were previously reserved for banks. These previous exclusive services are moving from behind the walled gardens of banks to the backyard of startups and tech companies who speak the language of the customers and offer benefits that are appreciated by the customers. FinTech companies are painfully showing big banking brands that it’s all about benefits and less about the brand.

What are some key hurdles in the FinTech industry that you’re experiencing? And how do you see data science applications helping solve these hurdles?

Mainly the acceptance of innovation by merchants for companies that need the innovation. Sometimes you may not be a solution that your B2B customer want but a solution that they need. So, you need to create proof of concept and illustrate the gathered business intelligence, in order to get them to accept innovation. And this is a longer process.

Can you tell us what you think the FinTech industry will look like 5 years from now? 

More democracy for banking services, smaller companies solving both large and small problems that are not being addressed. We will move to an environment where big companies will no longer have a monopoly but rather where many smaller boutique firms will populate the market. This will be a little bit like the USP of craft beer breweries which are offering all kinds of cool or strange blends of beer instead of having only big beer giants.

I expect the market to become much more fragmented as some areas of FinTech are not yet defined. The insurance industry for example will see a significant changes over the next 10-15 years. There will be more atomized insurance options that will have customized solutions for what you need. They will address the needs of the Facebook generation, which is more interested in individual offers then only one option that covers all. For example, imagine going on a skiing vacation and easily booking an insurance policy covering you and your skiing equipment, for just that single instance.

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Behind the Scenes of Dataconomy: Meet Darya! https://dataconomy.ru/2016/05/11/behind-scenes-dataconomy-meet-darya/ https://dataconomy.ru/2016/05/11/behind-scenes-dataconomy-meet-darya/#respond Wed, 11 May 2016 08:00:01 +0000 https://dataconomy.ru/?p=15622 Darya Niknamian Content Marketing Manager Originally born in Germany but grew up in Canada (the land of sorry’s and eh’s) Calling herself a Berliner for just over a year Thinks life is not complete without a little daily sarcasm What brought you to Dataconomy in the first place? I have a bit more of an […]]]>

DNimage2

Darya Niknamian
Content Marketing Manager

Originally born in Germany but grew up in Canada (the land of sorry’s and eh’s)
Calling herself a Berliner for just over a year
Thinks life is not complete without a little daily sarcasm


What brought you to Dataconomy in the first place?

I have a bit more of an analytical and science background as I completed a Chemistry and Life Science degree but ended up in Marketing! I was drawn to the growing field of Data Science and all the applications to different industries :). I am also big fan on Behavioural Economics and a lot of findings are made as a result of studying mountains of data and discerning trends.

What do you do at Dataconomy?

I’m the Content Marketing Manager at Dataconomy so I help curate content; find interesting and beautiful data stories; discover new partners and people to profile and manage all social media channels.

How do you use data driven technologies in your day to day life?

Well, I am constantly on my phone, whether it’s to communicate with people back home, read stories, map out where I am going to go or monitor our social media accounts. Also at work I am a big fan of Google Analytics and other metric reporting programs to help me identify what content our readers like!

Dataconomy puts on several events on a yearly basis with the philosophy of being a “Data Native” – what does this mean to you?

We can’t escape data, it’s everywhere and is ingrained in our daily lives and will continue to become a bigger part of society. So to me, being a Data Native is living and breathing data – becoming more aware of what impact it has.

What topics do you like to read on Dataconomy?

I am a big fan of articles that parallel Data Science with industries that may not be apparently using data applications! For example there was an article written by one of our awesome contributors Hannah titled – Will Big Data Write the Next Hit Song? I forgot how much programs like Spotify or Shazam use data and machine learning to optimize the user experience and generate recommendations. And since I am a big music lover, this article really resonated with me.

Dataconomy is based in Berlin, what is your favourite thing about living in such a booming tech town?

Berlin is a city that keeps changing and I love it as there is always something going on, especially in the tech scene – you can never tell me you had a dull moment in Berlin. People in the tech world are super open and creative, so it’s easy to get new ideas and build off that energy.

Interested in learning more about Marketing at Dataconomy? Touch base with Darya on LinkedIn or email her at darya@dataconomy.ru

image credit: CartanTours

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“Hadoop practitioners alike should rejoice in the rise of Spark…”- Interview with Altiscale’s Mike Maciag https://dataconomy.ru/2016/05/05/hadoop-practitioners-alike-rejoice-rise-spark-interview-altiscales-mike-maciag/ https://dataconomy.ru/2016/05/05/hadoop-practitioners-alike-rejoice-rise-spark-interview-altiscales-mike-maciag/#comments Thu, 05 May 2016 08:00:01 +0000 https://dataconomy.ru/?p=15620 Mike Maciag is the COO of Altiscale. Prior to Altiscale, he served as the president and CEO for DevOps leader Electric Cloud, where he grew the revenue from zero to tens of millions while building a worldwide presence and signing hundreds of blue-chip customers. Mike holds an MBA from Northwestern University’s Kellogg School of Management, […]]]>

Mike-Maciag (2)
Mike Maciag is the COO of Altiscale. Prior to Altiscale, he served as the president and CEO for DevOps leader Electric Cloud, where he grew the revenue from zero to tens of millions while building a worldwide presence and signing hundreds of blue-chip customers. Mike holds an MBA from Northwestern University’s Kellogg School of Management, and a BS from Santa Clara.

 


You’ve spent your career on the business side of technology companies and have successfully scaled many IT companies. What attracted you to the Hadoop market?

There is no doubt that we are moving toward a data-driven economy where enterprises of all sizes need to tap into their data resources in order to provide relevant products and services to their consumers. As a key component to any data-driven enterprise, Hadoop has evolved from a simple data repository into an engine that fuels smart, data-driven decision-making.

Hadoop began as an Apache project but is now also understood as an ecosystem of rapidly evolving open-source software projects. Because there has been so much interest in Hadoop and subsequently so many developments in this ecosystem, it has become difficult for companies to keep up. Why wrangle complex Hadoop clusters when you can have experts handle this data work on your behalf?

What kinds of companies are most likely to benefit from a cloud-based approach to Hadoop and related Big Data technologies (versus on-premise)?

Hadoop in the cloud, and other Big Data technologies provided as a service, have emerged as popular alternatives for enterprises that don’t want to manage the minutiae of running these technologies at scale and in production. Mid-sized companies with large data sets and limited IT budgets certainly benefit from a managed cloud service that removes these operational burdens, helping Big Data projects flow seamlessly and ultimately resulting in cost savings. However, within our own customer base, we also work with many large companies that find tremendous value in outsourcing these operational burdens, freeing up data scientists from the “janitorial” work associated with running Big Data so that they can focus on what they do best: exploring data to discover innovative ways to drive business value. These customers span all verticals, although we’ve seen particularly strong interest coming from financial services, healthcare and manufacturing.

Do you see the rise of Spark as a threat to Hadoop?

Hadoop practitioners alike should rejoice in the rise of Spark. Spark is actually a replacement for MapReduce, a data processing platform that runs on top of Hadoop. Hadoop also consists of the YARN resource manager and the HDFS storage system, both of which are required to run Spark. Spark is a critical technology to the future of the Hadoop ecosystem as it enables the near real-time processing and analysis of Big Data, a vital technical requirement for today’s fast-paced enterprise.

How do you predict the role of the data scientist will evolve in the next five years?

Data scientists are bogged down with running the operations of Big Data instead of exploring the data. And with good reason. There is already a debilitating skills gap in most enterprises, with research firm Gartner predicting that through 2018, 70 percent of Hadoop deployments will not meet cost savings and revenue generation objectives due to skills and integration challenges. Data scientists are already in high demand and their skills are often put to poor use (like wrangling Hadoop clusters). As technological advances and service organizations that remove these operational burdens gain traction, data scientists will shed their janitorial duties and shift their focus to finding new ways to leverage data.

What is the biggest misconception of Hadoop in the market today?

Many first-time Hadoop adopters are justly concerned about compatibility of their Big Data deployments. While the industry still has some kinks to iron out regarding compatibility, initiatives like the Open Data Procession Initiative (ODPi, under the auspices of the Linux Foundation) are moving practitioners toward greater standardization of Hadoop.

Where does Hadoop fit into the world of Big Data and the Internet of Things?

It may be the buzzword du jour but the Internet of Things aptly describes a growing challenge for IT departments: new methods of data collection have created data stores that were previously incomprehensible to anyone besides the largest of internet companies. Today, that has changed as organizations of all sizes are collecting inordinate amounts of data. Hadoop, with its roots within leading Silicon Valley internet companies, is a natural platform to handle this scale of data. IT leaders may decide to get rid of certain types of data but as a rule of thumb, I recommend that organizations hold on to all their data assets. As business priorities grow or shift, that data may prove to be valuable.

How can an organization make data stored in Hadoop “business-user friendly”?

Hadoop vendors are increasingly finding success selling self-service analytic solutions to non-IT business departments in the enterprise (like marketing). Business users want to have the ability to extrapolate insights from their data by integrating with their business intelligence tool of choice (like Tableau or Microsoft Excel) and start creating business value quickly without getting stuck in data preparation purgatory. We’ll increasingly see Hadoop solutions sold to these business users so that they can get up and running quickly and extract business insights from their data.

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Behind the Scenes of Dataconomy: Meet Ioana! https://dataconomy.ru/2016/04/27/behind-scenes-dataconomy-meet-ioana/ https://dataconomy.ru/2016/04/27/behind-scenes-dataconomy-meet-ioana/#respond Wed, 27 Apr 2016 08:30:43 +0000 https://dataconomy.ru/?p=15369 Ioana-Andreea Grapa Events Manager Originally from Romania (the first nation to have a happy graveyard)  Calling herself a Berliner for just over a year Appreciates a good glass of red wine 🙂 What brought you to Dataconomy in the first place? First of all, it was my passion for events. I decided to become part […]]]>

ioana
Ioana-Andreea Grapa
Events Manager

Originally from Romania (the first nation to have a happy graveyard) 
Calling herself a Berliner for just over a year
Appreciates a good glass of red wine 🙂


What brought you to Dataconomy in the first place?

First of all, it was my passion for events. I decided to become part of the team as I also wanted to work in a startup environment, as it’s something quite hyped in Berlin, and organising Big Data events all over Europe was something that definitely fit with my goals.

What do you do at Dataconomy?

I am the Events Manager and I am responsible for the organisation from inception to completion of our European meetups (16 locations at the moment and we intend to keep growing!) and the annual conference Data Natives.

How do you use data driven technologies in your day to day life?

Personally, it’s mostly my smartphone, which keeps me connected to my dear ones. I am also now taking advantage of the cool apps that are trendy in Berlin, such as Drive Now, which is a game changer!

Dataconomy puts on several events on a yearly basis with the philosophy of being a “Data Native” – what does this mean to you?

To me it means that we are moving along the pattern of the industry changes, which is in fact a whole new era – from Digital Natives to Data Natives. There is so much technological potential nowadays, and our behaviours, mentalities and preferences are constantly changing, so we want to share this knowledge with our communities.

What topics do you like to read on Dataconomy?

We are quite popular for providing innovative content to our readers, so the more extravagant and daring the content is, the more it captures my attention. I usually like reading topics connected to AI and IoT.

Dataconomy is based in Berlin, what is your favourite thing about living in such a booming tech town?

I love how open-minded people are here, and that combined with their creativity – no wonder Berlin is such a booming tech town! You always feel that you’re part of something innovative which can change things for the better, and that’s a great feeling to have, especially when you’re a foreigner trying to integrate in a new culture.

Touch base with her on LinkedIn or by email at ioana@dataconomy.ru.

image credit: Europe.org

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“But those skills for using data to listen deeply have proved invaluable in my career” – Interview with Looker’s Daniel Mintz https://dataconomy.ru/2016/04/25/skills-using-data-listen-deeply-proved-invaluable-career-interview-lookers-daniel-mintz/ https://dataconomy.ru/2016/04/25/skills-using-data-listen-deeply-proved-invaluable-career-interview-lookers-daniel-mintz/#respond Mon, 25 Apr 2016 08:00:43 +0000 https://dataconomy.ru/?p=15377 Daniel Mintz is the Chief Data Evangelist at Looker. Previously, he was Head of Data & Analytics at fast-growing media startup Upworthy and before that he was Director of Analytics at political powerhouse MoveOn.org. Throughout his career, Daniel has focused on how people interact with data in their everyday lives and how they can use it […]]]>

daniel mintz

Daniel Mintz is the Chief Data Evangelist at Looker. Previously, he was Head of Data & Analytics at fast-growing media startup Upworthy and before that he was Director of Analytics at political powerhouse MoveOn.org. Throughout his career, Daniel has focused on how people interact with data in their everyday lives and how they can use it to get better at what they do.

 


Why and how is the role of Chief Data Evangelist important to helping companies drive innovation and change in their organizations?

I think that in the coming years, you’re going to see more and more companies with someone evangelizing for the smart use of data and analytics. That’s because data-informed decision-making is a remarkably powerful way to improve your business, but it’s one that requires very different ways of working. And so it makes sense to have an evangelist who can jumpstart teams–show them the benefits quickly and get them over the initial friction that comes with new processes.

What do you think are the biggest challenges facing Chief Data Evangelists like yourself?

[bctt tweet=”I think the biggest challenge is in helping people get used to a new way of working.” username=”DataconomyMedia”] Lloyd Tabb, Looker’s co-founder, has a thing about how data is like another sense and it takes time and practice to get good at using it. So data evangelists end up working really hard to get people to think critically about what they’re measuring, and about what assumptions are baked into those measurements.

We don’t have to worry about those things when we’re operating at human scale with our five senses. We don’t have to think about where the light we see is coming from or how we’d know if our hearing is playing tricks on us, because we’ve got decades of practice using those senses. Data can let us “see” what’s happening at vastly greater scale, but only if we know how to use it well.

Your entire career has been focused on using data to make the world a better place. 

I truly believe that data can change the way that businesses operate, but also the way that governments serve their citizens, and the way that we solve the world’s biggest challenges. And it’s not just a naive belief that “if we just give people the facts then they’ll come around.” Rather, I think that data can be an incredibly powerful tool to tell compelling stories and help individuals realize how atypical their experiences are — it can force you to put yourself in someone else’s shoes and spur you to take action for the common good.

Like, I live in New York City, and while my family is doing fine, we certainly don’t feel rich. But when you look at the data, you see that the median household income in New York City is just under $51,000. And so imagining trying to make ends meet on less than $1,000/week really jolts you, and reminds you that “hey, you’ve got it pretty darn good.” The data gives you empathy in a way that’s hard to dismiss and then tells you whether your solutions are actually fixing the problem.

How will data continue to improve the world around us? 

I love the story of GiveDirectly, a charity that proved through rigorous data analysis that charitable giving to the world’s poorest doesn’t need to be complicated. They found that simply transferring money directly to recipients is an extremely effective way to reduce poverty and hunger.

The medical community is another place that’s rife with examples. We’re on the brink of huge advances–everything from ubiquitous sensors which alert doctors about a coming patient crisis, to artificial intelligence to avoid prescription errors, to data mining of massive sets of medical records to uncover hidden links about rare diseases. Or if you look at the way Medicare has started penalizing hospitals with high readmission rates, you see that as a result, many hospitals have found simple, inexpensive ways to address the problem.

At Upworthy, you joined your publishing platform with data about site visitors. Why? What impact did that integration have on the business?

It was absolutely critical. And I think you see that across industries–when data is liberated from its silos and put into one central place, all of a sudden you start connecting dots that nobody saw before. At Upworthy, it forced us to confront the fact that pageviews is a terrible way to measure whether a piece of content is successful. That’s why we built Attention Minutes, as a much more fine-grained measure of whether an article was really delighting readers or leaving them cold.

At MoveOn, data was used to rally people around issues and move them to action. What lessons might be gleaned from your experience that would benefit someone in business?

One of the fascinating things about being a staffer at MoveOn was that there were only a few dozen of us, so we had very little power to do anything ourselves. But there was an army of millions of MoveOn members who could power movements and donate to candidates…if they were engaged. So we developed really powerful tools for listening to our members, because if we didn’t, we risked deciding on a tactic that nobody would actually do.

But those skills for using data to listen deeply have proved invaluable in my career. And I think whether you’re trying to extract signal from the noise of millions of members or millions of customers, the skills are the same.

Readers may be surprised to learn that you have a bachelor’s degree in Music and Political Science, and a master’s degree in Multimedia Engineering. How has that rather unique combination of studies prepared you for a career in big data?

There haven’t really been many dedicated data training programs until recently, and so some of the smartest people I know in the data world have similarly varied backgrounds–everything from robotics to biology to visual arts. Because it turns out that as long as you come to the table with the core skills of numeracy, curiosity, and critical thinking, you can pick up the technical skills along the way.

What advice would you offer someone who has an interest in pursuing a data driven career path, but doesn’t have a typical data science degree?

Ignore the parts of the job listings that “require” a degree in mathematics, or 5 years of analytics experience, or whatever other demands they’re making these days. Except for some very specialized positions, those just aren’t the things that make a successful data analyst. Dig into some data and come up with interesting findings that you can discuss knowledgeably. If you show that you can use data to shed new light on hard questions, that’ll impress hiring managers much more than credentials that only look good on paper.

What are the most important or valuable applications or tools your team uses to evaluate, clean or use data?

Well, obviously Looker 🙂 (After three years as a customer, I was so in love with the platform that I came onboard to evangelize about its magic.) But beyond my employer, I’d say that the one thing I basically stopped hiring people without is SQL. It doesn’t particularly matter which dialect you learn — since it’s pretty easy to go from one to another. But SQL is the lingua franca of analysts. If you’re going to have to manipulate even medium-sized datasets, you’ll need SQL.

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“Perhaps by the end of 2021, FinTech will no longer be a buzzword at all…” – Interview with SwipeStox’s Wladimir Huber https://dataconomy.ru/2016/04/15/perhaps-end-2021-fintech-will-no-longer-buzzword-interview-swipestoxs-wladimir-huber/ https://dataconomy.ru/2016/04/15/perhaps-end-2021-fintech-will-no-longer-buzzword-interview-swipestoxs-wladimir-huber/#respond Fri, 15 Apr 2016 08:00:17 +0000 https://dataconomy.ru/?p=15292 Wladimir Huber (29) worked several years as a professional trader in a German investment bank, founded an eCommerce startup and holds a M.A. in Finance from Leuphana University. Since he graduated from the university, he constantly developed internet business models. He especially enjoys the data-driven and analytical approach to business. Wladimir knows Python and has […]]]>

“Perhaps by the end of 2021, FinTech will no longer be a buzzword at all…” - Interview with SwipeStox’s Wladimir Huber
Wladimir Huber (29) worked several years as a professional trader in a German investment bank, founded an eCommerce startup and holds a M.A. in Finance from Leuphana University. Since he graduated from the university, he constantly developed internet business models. He especially enjoys the data-driven and analytical approach to business. Wladimir knows Python and has experience in machine learning. He enjoys developing trading algorithms and travelling the world.


WHAT IS YOUR MISSION STATEMENT?

Our mission is to develop the simplest mobile Social Trading App to solve the problem of new traders to enter the financial markets. We want to develop the most transparent mobile social trading community worldwide, giving everyone the possibility to make profits while trading.

WHERE ARE YOU HEADQUARTERED & WHY?

SwipeStox is headquartered in the financial center of Frankfurt, Germany. We see Frankfurt as a perfect location in terms of access to strategic partners, labor and knowledge. Further, the city is known for its focus on capital markets and has further a big international airport which gives a solid base for a global expansion.

WHAT TYPE OF PROBLEMS ARE YOU TRYING TO SOLVE AT YOUR COMPANY?

SwipeStox is the first mobile social trading application that allows everyone to trade Forex, Indices, and CFDs (we will also offer the possibility to trade physical stocks) simply by copying single trades. Everyone can swipe (Tinder-style swiping) through the trades other experienced traders have created and profit from their knowledge about the markets. By connecting traders from all over the world in one network, users can see live trades and copy them – rather than relying on fundamental or technical trading decisions alone. As a trader you can either leverage this information to execute own trades or simply follow and copy other traders and their trades. We want to bring trading to the masses and allow everyone to trade the financial markets without having many years of experience. We solve the problem of people who do not have any experience nor the time for trading successfully.

WHAT ARE THE SIGNIFICANT SHIFTS THAT YOU SEE IN THE INDUSTRY?

In the last few years, Blockchain and bitcoin were always a hot topic. Further, we saw that robo advisory was emerging. When taking a look at the financial industry as a whole, I see that more people need to take the responsibility for their financial situation since banks do not hold the holy grail anymore and FinTechs with alternative solutions are showing a stronger presence. I think that over the next years the whole finance industry will become dynamic without relying on the advice of banks and financial institutions. Accompanied by that we will also see an increasing emphasis on data security protecting customer sensitive data.

IF YOU COULD TACKLE ANY TECHNOLOGY-SOLVABLE CHALLENGE EXISTING TODAY, WHICH WOULD IT BE – AND WHY?

From my perspective it would probably have to do with a – regulated – renewed money model allowing individuals to exchange money with each other, cutting the middlemen. The challenge would be to match individuals and to give especially underbanked people the access to virtual money. An underlying solution might be the Blockchain technology.

WHO DO YOU THINK WILL BE THE MOST INFLUENTIAL FIGURES (OR COMPANIES) IN FINTECH, IN 2016? WHAT KIND OF YEAR DO YOU FORESEE FOR YOUR COMPANY?

Fintech has been booming recently and in general there are many finance sectors in need for a disruptive new approach. For the past few years the term FinTech became a synonym for basically everything that was related to banking or finance. However, many companies out there just developed a better frontend without really changing underlying process or without tackling more “fundamental” problems such as giving underbanked people a possibility to enter the financial markets for instance. As for ourselves, this will be our year for global expansion, which we need to manage operationally and strategically. Further, we will push the development of our API, allowing us to onboard every broker and bank worldwide. It will be extremely important for us to strengthen our partnerships and to develop a good reputation.

WHAT ARE SOME KEY HURDLES IN THE FINTECH INDUSTRY THAT YOU’RE EXPERIENCING? AND HOW DO YOU SEE DATA SCIENCE APPLICATIONS HELPING SOLVE THESE HURDLES?

I see mainly regulatory issues as the main problem in the industry. An idea alone is often not enough if you don’t have the necessary licenses and/or lobby behind you. Further, traditional finance is still conservative and so are the customers. With more solutions coming into place, the typical customer becomes more interested in alternative services and investment and savings possibilities. Data science will bring better products to the market and give everyone the feeling of actively influencing the return on an investment.

CAN YOU TELL US WHAT YOU THINK THE FINTECH INDUSTRY WILL LOOK LIKE 5 YEARS FROM NOW?!

On the one side I believe that we will see a consolidation of FinTech companies and that we will also see the development of really big players that might be backed by banks and other big institutions. Furthermore, big banks will continue to lose ground to, or partner with, FinTech players which will require them to buy new markets players in order not to maintain market share. Perhaps by the end of 2021, FinTech will no longer be a buzzword at all and will become the new standard in finance and banking.

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“A great deal of tech challenges in our industry are ultimately challenges in understanding people.”-Interview with Quantcast’s Peter Sirota https://dataconomy.ru/2016/04/08/great-deal-tech-challenges-industry-ultimately-challenges-understanding-people-interview-quantcasts-peter-sirota/ https://dataconomy.ru/2016/04/08/great-deal-tech-challenges-industry-ultimately-challenges-understanding-people-interview-quantcasts-peter-sirota/#respond Fri, 08 Apr 2016 07:30:05 +0000 https://dataconomy.ru/?p=15283 As Senior Vice President of Engineering, Peter Sirota is responsible for scaling engineering and managing, organizing and utilizing Quantcast’s tremendous understanding of real-time consumer behavior.   What is the impact of big data and how is it being used to identify patterns and trends especially now with media and publishers becoming active members in the industry? Big […]]]>

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As Senior Vice President of Engineering, Peter Sirota is responsible for scaling engineering and managing, organizing and utilizing Quantcast’s tremendous understanding of real-time consumer behavior.

 



What is the impact of big data and how is it being used to identify patterns and trends especially now with media and publishers becoming active members in the industry?

Big data impacts all aspects of the media and digital advertising industries — from marketing, strategy and creative, to editorial calendars and more. With so much information available, data and machine learning are being used daily to better understand human behavior and unlock actionable insights for brands and publishers. Those insights are not necessarily advertising specific, analyzing patterns in Big Data can help publishers and marketers build better and more relevant products for their customers.

How can marketers and publishers target their audiences and customers using data?

Today, marketers and publishers can understand their audience like never before. At the heart of any business is the customer. Using data and advanced modeling techniques, we can now create personalized experiences for consumers, reaching them online at critical moments within their decision-making process. There are two parts to understanding and targeting your customers. First, you need to understand their interests. This can be done by analyzing massive amounts of data where you can understand user behavior across millions of sites. Second, you need to understand the context in which a particular request is made in real-time. Details such as time of day, location, and sequence of sites that a customer visited before coming to your site could provide critical insights about timeliness of your message.

What transferrable skills did you learn as GM of Amazon Elastic MapReduce that you have been able to apply to your role as SVP of Engineering at Quantcast?

At Amazon we built highly scalable, durable and easy to use Big Data services. They are currently used by tens of thousands of customers including consumer brands such as Netflix or Unilever, financial regulators such as FINRA, and ad tech customers such as AdRoll and others.Building these services at scale and working with these customers taught me the importance of collection, storage, analysis and sharing of Big Data regardless of business size or type. Coming to Quantcast, the experience I gained at Amazon sure comes in handy. Quantcast operates at a Big Data scale so it’s like building a massive service all over again. Quantcast is a very customer centric company and working with customers, understanding their needs, and delivering solutions that are effective and easy to use is as relevant at Quantcast as it was at Amazon.

What kind of interesting and unique data have you seen recently?

Some of the interesting datasets I’ve recently seen include data from Tivo that provides panel data on media interest. Another example is data from V12 that provide shopping interest data. It’s interesting to scale that data across publishers to see things like what type of car folks prefer who read The Economist.

What kinds of tech challenges are you looking to solve at scale?

A great deal of tech challenges in our industry are ultimately challenges in understanding people. Understanding human behavior in the digital world is at the heart of what we do at Quantcast and through our proprietary data and modeling techniques, we’re tackling the challenge of understanding people, at scale. For example, we recently developed a new product called Audience Grid which allows us to work closely with data partners from across the industry to help paint a picture of what consumer’s online and offline behaviors are. To help provide these insights and integrate different data into the fine grained signal we already receive from 100 million destinations we need to build up massively scalable and reliable systems.

What do you think the future of AdTech looks like?

The AdTech industry has a couple of unique characteristics. First, unlike many other industries, AdTech accumulates tremendous amounts of human behavioral data. Second, any incremental improvement in analytics or modeling of that data has a direct impact on the performance of marketing campaigns and hence the revenue of the AdTech companies. Those two characteristics fuel the substantial innovation in data modeling and distributed system development that is transforming digital advertising. Over time, AdTech will make any advertising a more precise science instead of the intuitive, hard to measure art that largely exists today. This in turn will help make ads more relevant to consumers which could shrink the overall amount of ads we see in any medium across any device.

We face a lot of challenges along the way as the industry goes through this transformation. There is a lot of bot and other fraud going on as well as brand safety issues. Great AdTech companies invest heavily in addressing those issues. It will always be a game of cat and mouse but over time the impact of those issues will be negligible. There are also a lot of issues with attribution fraud, where last touch attribution of credit is incentivizing the wrong behaviors in AdTech companies. Marketers will become a lot more sophisticated in evaluating performance of AdTech companies and companies that drive pure net incrementality are going to win.

Finally, successful AdTech companies will help their customers not only run performance and brand advertising campaigns but also help customers understand their consumers better. Many of our customers value our performance as much as the insights that we are able to generate explaining to them who likes their products, how consumers react to them and why. This partnership will be fundamental between successful AdTech companies and marketers.

image credit: Roman Makhmutov

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“I’d like to see NoSQL become much easier to adopt” – Interview with Basho’s John Musser https://dataconomy.ru/2016/03/18/id-like-see-nosql-become-much-easier-adopt-interview-bashos-john-musser/ https://dataconomy.ru/2016/03/18/id-like-see-nosql-become-much-easier-adopt-interview-bashos-john-musser/#comments Fri, 18 Mar 2016 08:30:47 +0000 https://dataconomy.ru/?p=15131 John Musser is an expert in software development, specifically in building highly scalable and resilient software and currently serves as Vice President of Technology at Basho. In his role at Basho, Musser helps drive product strategy and direction for the Basho family of products, which simplify enterprises’ most critical data management challenges. Prior to joining […]]]>

John Musser
John Musser is an expert in software development, specifically in building highly scalable and resilient software and currently serves as Vice President of Technology at Basho. In his role at Basho, Musser helps drive product strategy and direction for the Basho family of products, which simplify enterprises’ most critical data management challenges. Prior to joining Basho in 2016, Musser founded and led API Science and the ProgrammableWeb.


You have a long standing history as an expert in API strategies. Why do you think API strategies are so valuable to a company and what are your tips for others who want to become an expert in this field?

APIs are so valuable because they enable companies to become platforms, and platforms today provide a tremendous competitive opportunity and strategic advantage. Just look at Apple and Google’s mobile platforms, Salesforce.com’s enterprise SaaS platforms, Amazon’s AWS cloud platforms and so on. All these companies have a platform strategy powered by APIs. To become an expert in this field one should look at the playbook of both these largest API players, but also a new generation of API-first companies like Twilio and Stripe that have disrupted markets through APIs.

Which use cases do you think can benefit most from NoSQL and related distributed computing?

You should start thinking about NoSQL solutions anytime you’re looking at a data-intensive project that is “at scale” — that is, a project with key requirements around high volume, high throughput and high availability. If you look at the use cases that gave rise to this market you’ll see how all those companies needed tools to handle data-at-scale: social networking, eCommerce, telecommunications and so on. As more and more enterprises find themselves with high-volume data needs, whether from customer data, sensor data, or anywhere else, they should evaluate NoSQL as a way to successfully manage and derive value from this data.

What trends do you see gaining steam over the coming year that will impact the use of NoSQL?

The first big trend driving use of NoSQL today is the rapid rise of the Internet of Things (IoT). Devices of all sorts, from connected cars to wearables to new connected industrial equipment, all generate tremendous volumes of data. Gartner, Inc. forecasts that 6.4 billion connected things will be in use worldwide in 2016, up 30 percent from 2015, and will reach 20.8 billion things by 2020. In 2016, 5.5 million new things will get connected every day. Both the volume and nature of this data is a natural fit for NoSQL.

The other big trend on the NoSQL horizon is the movement toward integrated data workflows optimized for data processing and analysis at scale. This means NoSQL will become a core component of packaged “stacks” of application components — distributed storage, message queueing, analytics, visualization — which, taken together, accelerate and simplify deploying and managing this class of big data, IoT and analytics applications. This maturation of NoSQL will bring greater value to businesses by allowing them to focus at a higher level, one that’s more results-oriented, rather than spending time and resources on the underlying data plumbing.

What one piece of advice would you like to share with companies that are trying build out big data applications?

The era of monolithic solutions is over and they should choose the right tool for the job at hand. For some companies this can be hard because it often means change; it means moving beyond past assumptions, and introducing new tools, technologies and processes. For example, many of the best NoSQL platforms come from the open-source world, and companies should embrace this approach not fear it.

How do you hope to see the use of NoSQL change?

I’d like to see NoSQL become much easier to adopt. As we talked about earlier, companies should be able to focus on how to get real value from all this data they’re collecting rather than worrying about all the infrastructure and integration logistics needed just to make it work. Today companies are cobbling together data management, analytics and visualization toolsets as part of their own custom data supply chains, but it’s too much work. Just as the idea of well integrated sets of tools like the LAMP stack really accelerated web development, this is happening in the world of NoSQL as well.

Are there any industries you think need to adopt NoSQL over others? Why?

Data plays a bigger and bigger role in driving our economy each year, and every industry that I can think of needs to be leveraging data to improve management of their assets, develop products and improve the quality of their interactions with their customers. At Basho we see how far this has spread, with customers spanning healthcare, telecommunications, ecommerce, gaming, manufacturing, utilities, security and software so I can’t single out any one industry, but those industries looking to leverage IoT data will need to adopt NoSQL and quickly.

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“Knowledge of the business really influences how one approaches analyzing issues”-Interview with MineThatData’s Kevin Hillstrom https://dataconomy.ru/2015/12/30/knowledge-of-the-business-really-influences-how-one-approaches-analyzing-issues-interview-with-minethatdatas-kevin-hillstrom/ https://dataconomy.ru/2015/12/30/knowledge-of-the-business-really-influences-how-one-approaches-analyzing-issues-interview-with-minethatdatas-kevin-hillstrom/#comments Wed, 30 Dec 2015 08:30:13 +0000 https://dataconomy.ru/?p=14656 Kevin is President of MineThatData, a consultancy that helps CEOs understand the complex relationship between Customers, Advertising, Products, Brands, and Channels. Kevin supports a diverse set of clients, including internet startups, thirty million dollar catalog merchants, international brands, and billion dollar multichannel retailers. Kevin is frequently quoted in the mainstream media, including the New York […]]]>

kevinKevin is President of MineThatData, a consultancy that helps CEOs understand the complex relationship between Customers, Advertising, Products, Brands, and Channels. Kevin supports a diverse set of clients, including internet startups, thirty million dollar catalog merchants, international brands, and billion dollar multichannel retailers. Kevin is frequently quoted in the mainstream media, including the New York Times, Boston Globe, and Forbes Magazine. Prior to founding MineThatData, Kevin held various roles at leading multichannel brands, including Vice President of Database Marketing at Nordstrom, Director of Circulation at Eddie Bauer, and Manager of Analytical Services at Lands’ End.


What is the biggest misunderstanding in “big data” and “data science”?

To me, it is the “we’re going to save the world with data” mentality. I like the optimism, that’s good! I do not like the hype.

Describe the three most underrated skills of a good analyst and how does an analyst learn them?

The first underrated skill is selling. An analyst must learn how to sell ideas. My boss sent me to Dale Carnegie training, a course for sales people. The skills I learned in that class are invaluable. The second underrated skill is accuracy. I work with too many analysts who do all of the “big data” stuff, but then run incorrect queries and, as a result, lose credibility with those they are analyzing data for. The third underrated skill is business knowledge. So many analysts put their heart and soul into analyzing stuff. They could put some of their heart and soul into understanding how their business behaves. Knowledge of the business really influences how one approaches analyzing issues.

How do you clearly explain the context of a data problem to a skeptical stakeholder?

To me, this is where knowledge of the business is really important. So many of my mistakes happened when I cared about the data and the analysis, and did not care enough about the business. I once worked for a retail business that only had twenty-four months of data. That was a big problem, given that the company had been in business for fifty years. Nobody, and I mean nobody, cared. I explained repeatedly how I was unable to perform the work I wanted to perform. Nobody cared. When I shifted my message to what I was able to do for a competing retailer who had ten years of data, then people cared. They cared because their business was not competitive with a business they all knew. Then folks wanted to compete, and we were able to build a new database with many years of data.

What is the best question you’ve ever been asked in your professional career?

A high level Vice President once listened to a presentation, and then said to me, “Who cares?” The executive went on to say that I was only sharing trivia. He told me that unless I had facts and information that he could act upon, he didn’t want me to share anything. This is a good lesson. Too often, we share information because we were able to unearth an interesting nugget in the database. But if the information is “nice to know”, it doesn’t help anybody. It is better to share a simple fact that causes people to change than to share interesting facts that nobody can use to improve the business.

What is the best thing – in terms of career acceleration – you’ve ever been told in your professional career?

Ask to be promoted to your next job. I had a boss who, in the 9th year of my career, asked me what I wanted to do next? So I told my boss – the job was outside of my area of experience, to be honest, and the job was a major promotion. I described why I wanted the job, I described how I would do the job differently, and I described my vision for how I would make the company more profitable. Within twelve months, I was promoted into the job. My goodness, were people upset! But it was a major lesson. When somebody asks you what you want to do next in your career, be ready to offer a credible answer. Maybe more important, be ready to share your answer even if nobody asks you the question! Tell people what your next job looks like, tell people your vision for that job, tell people how the company benefits, and then do work that proves you are ready for an audacious promotion!

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“I don’t think that you should approach big data as a solution in search of a problem”- Interview with Skimlinks Maria Mestre https://dataconomy.ru/2015/12/23/i-dont-think-that-you-should-approach-big-data-as-a-solution-in-search-of-a-problem-interview-with-skimlinks-maria-mestre/ https://dataconomy.ru/2015/12/23/i-dont-think-that-you-should-approach-big-data-as-a-solution-in-search-of-a-problem-interview-with-skimlinks-maria-mestre/#comments Wed, 23 Dec 2015 08:30:46 +0000 https://dataconomy.ru/?p=14630 I completed a PhD in signal processing at Cambridge developing models of user behaviour using brain data. After the PhD I joined Skimlinks as a data scientist, where I model online user behaviour and work on much larger datasets. My main role is implementing large-scale machine learning models processing terabytes of data. What project have […]]]>

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I completed a PhD in signal processing at Cambridge developing models of user behaviour using brain data. After the PhD I joined Skimlinks as a data scientist, where I model online user behaviour and work on much larger datasets. My main role is implementing large-scale machine learning models processing terabytes of data.


What project have you worked on do you wish you could go back to, and do better?

I think that pretty much applies to any project you do as a data scientist. When you’re developing algorithms that become a service used by someone either internally or externally, I think it is best to use an iterative approach where you wait for some feedback from the client before doing any further improvements. I am a true believer of “lean data science”.

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

I guess it depends what the advice is for. If it is for PhD students thinking about a career as a data scientist in industry, then I would strongly recommend them to get some experience working on real-world data at some point during the PhD. It is quite common in academia to work mainly on synthetic data. In addition to that, I would say it is important to keep a curious and open mind about the research carried out by other people, since it is very easy to only stay focused on your specific research project. For analytics professionals, I would say that learning how to code is quite useful, especially in a scripting language like Python. Knowing some classical statistics is also very helpful, if you want to learn how to apply a scientific approach to any type of data analysis.

What do you wish you knew earlier about being a data scientist?

There is not much I can think about, but maybe I wish I had spent more time using version control platforms, such as GitHub. During my PhD I had a very rudimentary version control method: copying my whole project into a different folder with today’s date. It was definitely not the best way of managing my project. In my current role we work on a shared Codebase and we need to keep track of changes, so I had to start using GitHub. I wish I had taken more time to learn how to use it properly before diving into it, as it would have saved me a lot of time.

How do you respond when you hear the phrase ‘big data’?

I say that’s boring, now it’s all about “massive data”! Now seriously, I have experienced big data at Skimlinks, where we run daily jobs on terabytes of data using Spark. I think “big data” is a real thing, but people sometimes believe they have it when they don’t, or if they have it, then they think they need to do something about it, but don’t know what. I don’t think that you should approach “big data” as a solution in search of a problem. You should always think of the problem first that you’re trying to solve, see if your data scale qualifies as “big data”, and then finally start using big data tools once you have defined all these parameters. It is a waste of time and resources to start using these tools just because they are fashionable and you’re scared of missing out.

What is the most exciting thing about your field?

I find solving real problems exciting, and if these problems are hard, then it is double as exciting. As a data scientist, you have to solve hard problems all the time, mainly because real data is never like in the textbooks! It is always biased, with missing columns or wrong values. Then, I also find it exciting to solve problems with large-scale data. It is very easy to use out-of-the-box Python libraries to run a machine learning algorithm, but what happens when you have to adapt that algorithm to run on 500 gigabytes? That’s when you need to start thinking creatively using the tools you already know to solve a new problem. You might even be the first person to solve such a problem!

In more general terms, I think that machine learning will have a huge impact on our daily lives. We have already started seeing the effects now that we are always connected and use increasingly intelligent apps, but I think this is only the beginning.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

This is a great question, and one that I keep asking myself. As I said earlier, I believe in lean data science. What this means is that I believe you need to start with a very clear objective you are trying to solve and use an iterative approach over it, always gathering feedback from the end user. If possible, the end goal should be stated in clear objective metrics, like increasing the accuracy of a classifier by 10%, or make better recommendations in 20% of the cases. You know it’s good enough when the end user is happy. I also believe that sometimes when you look at a problem from a lot of different angles and don’t seem to make a lot of progress, it is good to document all the attempts, leave it on the side, and get back to it later with a fresh pair of eyes.

How do you explain to C-level execs the importance of Data Science? How do you deal with the ‘educated selling’ parts of the job?

As a data scientist, your role is not only to develop algorithms, but also to be an evangelist in your own company on the use of data science, and generally the scientific method. If you want to convince business people that data science is important, then the best you can do is talk business. You need to think of data science projects in terms of the value they can add to your business, either because they can increase conversion rates, or keep some customers happy, or make someone’s job in the company much easier… You can start by running small experiments and gather some results to show to the executives in your company. However, data science is not the solution to any problem, and sometimes a simple rule-based model could do the job just as well. It is important not to oversell what you can do, and be realistic about what you can offer.

What is the most exciting thing you’ve been working on lately and tell us a bit about?

Skimlinks is about to launch a new product in the coming weeks, and the data science team has been heavily involved in its making. I cannot say much about it unfortunately, but these are exciting times for the company. From a technical point of view, the last thing that I have done which was exciting was classifying 1.2 billion data points using Spark. I broke a personal record in terms of the size of the data involved.

What is the biggest challenge of building a data science team?

I would have to ask my manager, since I have never built a team myself. I have been involved in the hiring process though, and I think it is sometimes difficult to find the right combination of skills across the team. You want some people who have experience working with data, others than may be stronger in engineering. It is also important to manage people’s expectations about the role, since data scientists spend a lot of time doing data processing and setting up data pipelines before they can apply machine learning algorithms. It’s all part of the job!

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“Data is the first class citizen. Algorithms and models are just helpers” – Interview with Dato’s Alice Zheng https://dataconomy.ru/2015/12/18/data-is-the-first-class-citizen-algorithms-and-models-are-just-helpers-interview-with-datos-alice-zheng/ https://dataconomy.ru/2015/12/18/data-is-the-first-class-citizen-algorithms-and-models-are-just-helpers-interview-with-datos-alice-zheng/#respond Fri, 18 Dec 2015 08:30:24 +0000 https://dataconomy.ru/?p=14587 Alice is an expert on building scalable Machine Learning models and currently works for Dato who are a company providing tooling to help you build scalable machine learning models easily. She is also a keen advocate of encouraging women in Machine Learning and Computer Science. Alice has a PhD from UC Berkeley and spent some of […]]]>

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Alice is an expert on building scalable Machine Learning models and currently works for Dato who are a company providing tooling to help you build scalable machine learning models easily. She is also a keen advocate of encouraging women in Machine Learning and Computer Science. Alice has a PhD from UC Berkeley and spent some of her post docs at Microsoft Research in Redmond. She is currently based in Washington State in the US.


What project have you worked on do you wish you could go back to, and do better?

Too many! The top of the list is probably my PhD thesis. I collaborated with folks in software engineering research and we proposed a new way of using statistics to debug software. They instrumented programs to spit out logs for each run that provide statistics on the state of various program variables. I came up with an algorithm to cluster the failed runs and the variables. The algorithm identifies variables that are most correlated with each subset of failures. Those variables, in turn, can take the programmer very close to the location of the bug in the code.

It was a really fun project. But I’m not happy with the way that I solved the problem. For one thing, the algorithm that I came up with had no theoretical guarantees. I did not appreciate theory when I was younger. But nowadays, I’m starting to feel bad about the lack of rigor in my own work. It’s too easy in machine learning to come up with something that seems to work, maybe even have an intuitive explanation for why it makes sense, and yet not be able to write down a mathematical formula for what the algorithm is actually doing.

Another thing that I wish I had learned earlier is to respect the data more. In machine learning research, the emphasis is on new algorithms and models. But solving real data science problems require having the right data, developing the right features, and finally using the right model. Most of the time, new algorithms and methods are not needed. But a combination of data, features, and model is the key. I wish I’d realized this earlier and spent less time focusing on just one aspect of the whole pipeline.

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

Be curious. Go deep. And study the arts.

Being curious gives you breadth. Knowing about other fields pulls you out of a narrow mindset focused on just one area of study. Your work will be more inspired, because you are drawing upon diverse sources of information.

Going deep into a subject gives you depth and expertise, so that you can make the right choices when trying to solve a problem, and so that you might more adequately assess the pros and cons of each approach.

Why study the arts? Well, if I had my druthers, art, music, literature, mathematics, statistics, and computer science would be required courses for K12. They offer completely different ways of understanding the world. They are complementary of each other. Knowing more than one way to see the world makes us more whole as human beings. Science is an art form. Analytics is about problem solving, and it requires a lot of creativity and inspiration. It’s art in a different form.

What do you wish you knew earlier about being a data scientist?

Hmm, probably just what I said above – respect the data. Look at it in all different ways. Understand what it means. Data is the first class citizen. Algorithms and models are just helpers. Also, tools are important. Finding and learning to use good tools will save a lot of time down the line.

How do you respond when you hear the phrase ‘big data’?

Cringe? Although these days I’ve become de-sensitized. 🙂

I think a common misconception about “big data” is that, while the total amount of data maybe big, the amount of useful data is very small in comparison. People might have a lot of data that has nothing to do with the questions they want to answer. After the initial stages of data cleaning and pruning, the data often becomes much much smaller. Not big at all.

What is the most exciting thing about your field?

So much data is being collected these days. Machine learning is being used to analyze them and draw actionable insights. It is being used to not just understand static patterns but to predict things that have not yet happened. Predicting what items someone is likely to buy or which customers are likely to churn, detecting financial fraud, finding anomalous patterns, finding relevant documents or images on the web. These applications are changing the way people do business, find information, entertain and socialize, and so much of it is powered by machine learning. So it has great practical use.

For me, an extra exciting part of it is to witness applied mathematics at work. Data presents different aspects of reality, and my job as a machine learning practitioner is to piece them together, using math. It is often treacherous and difficult. The saying goes “Lies, lies, and statistics.” It’s completely true; I often arrive at false conclusions and have to start over again. But it is so cool when I’m able to peel away the noise and get a glimpse of the underlying “truth.” When I’m getting nowhere, it’s frustrating. But when I get somewhere, it’s absolutely beautiful and gratifying.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

Oh! I know the answer to this question: before embarking on a project, always think about “what will success look like? How would I be able to measure it?” This is a great lesson that I learned from mentors at Microsoft Research. It’s saved me from many a dead end. It’s easy to get excited about a new endeavor and all the cool things you’ll get to try out along the way. But if you don’t set a metric and a goal beforehand, you’ll never know when to stop, and eventually the project will peter out. If your goal IS to learn a new tool or try out a new method, then it’s fine to just explore. But with more serious work, it’s crucial to think about evaluation metrics up front.

You spent sometime at other firms before Dato. How did you manage cultural challenges, dealing with stakeholders and executives? What advice do you have for new starters about this?

I think this is a continuous learning experience. Every organization is different, and it’s incredible how much of a leader’s personality gets imprinted upon the whole organization. I’m fascinated by the art and science behind creating successful organizations. Having been through a couple of very different companies makes me more aware of the differences between them. It’s very much like traveling to a different country: you realize that many of the things you took for granted do not actually need to be so. It makes me appreciate diversity. I also learn more about myself, about what works and what doesn’t work for me.

How to manage cultural challenges? I think the answer to that is not so different between work and life. No matter what the circumstance, we always have the freedom and the responsibility to choose who we want to be. How I work is a reflection of who I am. Being in a new environment can be challenging, but it can also be good. Challenge gets us out of our old patterns and demands that we grow into a new way of being. For me, it’s helpful to keep coming back to the knowledge of who I am, and who I want to be. When faced with a conflict, it’s important to both speak up and to listen. Speaking up (respectfully) affirms what is true for us. Listening is all about trying to see the other person’s perspective. It sounds easy but can be very difficult, especially in high stress situations where both sides hold to their own perspective. But as long as there’s communication, and with enough patience and skill, it’s possible to understand the other side. Once that happens, things are much easier to resolve.

How do you explain to C-level execs the importance of Data Science? How do you deal with the ‘educated selling’ parts of the job?

I point to all the successful examples of data science today. With successful companies like Amazon, Google, Netflix, Uber, AirBnB, etc. leading the way, it’s not difficult to convince people that data science is useful. A lot of people are curious and need to learn more before they make the jump. Others may have already bought into it but just don’t have the resources to invest in it yet. The market is not short no demand. It is short on supply: data scientists, good tools, and knowledge. It’s a great time to be part of this ecosystem!

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“Being comfortable with ambiguity and successfully framing problems is a great way to differentiate yourself” – Interview with Stitch Fix’s Brad Klingenberg https://dataconomy.ru/2015/12/14/being-comfortable-with-ambiguity-and-successfully-framing-problems-is-a-great-way-to-differentiate-yourself-interview-with-stitich-fixs-brad-klingenberg/ https://dataconomy.ru/2015/12/14/being-comfortable-with-ambiguity-and-successfully-framing-problems-is-a-great-way-to-differentiate-yourself-interview-with-stitich-fixs-brad-klingenberg/#respond Mon, 14 Dec 2015 08:30:40 +0000 https://dataconomy.ru/?p=14561 Brad Klingenberg is the Director of Styling Algorithms at Stitch Fix in San Francisco. His team uses data and algorithms to improve the selection of merchandise sent to clients. Prior to joining Stitch Fix Brad worked with data and predictive analytics at financial and technology companies. He studied applied mathematics at the University of Colorado […]]]>

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Brad Klingenberg is the Director of Styling Algorithms at Stitch Fix in San Francisco. His team uses data and algorithms to improve the selection of merchandise sent to clients. Prior to joining Stitch Fix Brad worked with data and predictive analytics at financial and technology companies. He studied applied mathematics at the University of Colorado at Boulder and earned his PhD in Statistics at Stanford University in 2012.

 


What project have you worked on do you wish you could go back to, and do better?

Nearly everything! A common theme would be not taking the framing of a problem for granted. Even seemingly basic questions like how to measure success can have subtleties. As a concrete example, I work at Stitch Fix, an online personal styling service for women. One of the problems that we study is predicting the probability that a client will love an item that we select and send to her. I have definitely tricked myself in the past by trying to optimize a measure of prediction error like AUC.

This is trickier than it seems because there are some sources of variance that are not useful for making recommendations. For example, if I can predict the marginal probability that a given client will love any item then that model may give me a great AUC when making predictions over many clients, because some clients may be more likely to love things than others and the model will capture this. But if the model has no other information it will be useless for making recommendations because it doesn’t even depend on the item. Despite its AUC, such a model is therefore useless for ranking items for a given client. It is important to think carefully about what you are really measuring.

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences and Social Sciences?

Focus on learning the basic tools of applied statistics. It can be tempting to assume that more complicated means better, but you will be well-served by investing time in learning workhorse tools like basic inference, model selection and linear models with their modern extensions. It is very important to be practical. Start with simple things.

Learn enough computer science and software engineering to be able to get things done. Some tools and best practices from engineering, like careful version control, go a long ways. Try to write clean, reusable code. Popular tools in R and Python are great for starting to work with data. Learn about convex optimization so you can fit your own models when you need to – it’s extremely useful to be able to cast statistical estimates as the solution to optimization problems.

Finally, try to get experience framing problems. Talk with colleagues about problems they are solving. What tools did they choose? Why? How should did they measure success? Being comfortable with ambiguity and successfully framing problems is a great way to differentiate yourself. You will get better with experience – try to seek out opportunities.

What do you wish you knew earlier about being a data scientist?

I have always had trouble identifying as a data scientist – almost everything I do with data can be considered applied statistics or (very) basic software engineering. When starting my career I was worried that there must be something more to it – surely, there had to be some magic that I was missing. There’s not. There is no magic. A great majority of what an effective data scientist does comes back to the basic elements of looking at data, framing problems, and designing experiments. Very often the most important part is framing problems and choosing a reasonable model so that you can estimate its parameters or make inferences about them.

How do you respond when you hear the phrase ‘big data’?

I tend to lose interest. It’s a very over-used phrase. Perhaps more importantly I find it to be a poor proxy for problems that are interesting. It can be true that big data brings engineering challenges, but data science is generally made more interesting by having data with high information content rather than by sheer scale. Having lots of data does not necessarily mean that there are interesting questions to answer or that those answers will be important to your business or application. That said, there are some applications like computer vision where it can be important to have a very large amount of data.

What is the most exciting thing about your field?

While “big data” is overhyped, a positive side effect has been an increased awareness of the benefits of learning from data, especially in tech companies. The range of opportunities for data scientists today is very exciting. The abundance of opportunities makes it easier to be picky and to find the problems you are most excited to work on. An important aspect of this is to look in places you might not expect. I work at Stitch Fix, an online personal styling service for women. I never imagined working in women’s apparel, but due to the many interesting problems I get to work on it has been the most exciting work of my career.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

As I mentioned previously, it can be helpful to start framing a problem by thinking about how you would measure success. This will often help you figure out what to focus on. You will also seldom go wrong by starting simple. Even if you eventually find that another approach is more effective a simple model can be a hugely helpful benchmark. This will also help you understand how well you can reasonably expect your ultimate approach to perform. In industry, it is not uncommon to find problems where (1) it is just not worth the effort to do more than something simple, or (2) no plausible method will do well enough to be considered successful. Of course, measuring these trade-offs depends on the context of your problem, but a quick pass with a simple model can often help you make an assessment.

How do you explain to C-level execs the importance of Data Science? How do you deal with the ‘educated selling’ parts of the job? In particular – how does this differ from sports and industry?

It is usually better if you are not the first to evangelize the use of data. That said, data scientists will be most successful if they put themselves in situations where they have value to offer a business. Not all problems that are statistically interesting are important to a business. If you can deliver insights, products or predictions that have the potential to help the business then people will usually listen. Of course this is most effective when the data scientist clearly articulates the problem they are solving and what its impact will be.

The perceived importance of data science is also a critical aspect of choosing where to work – you should ask yourself if the company values what you will be working on and whether data science can really make it better. If this is the case then things will be much easier.

What is the most exciting thing you’ve been working on lately and tell us a bit about it.

I lead the styling algorithms team at Stitch Fix. Among the problems we work on is making recommendations to our stylists, human experts who curate our recommendations for our clients. Making recommendations with humans in the loop is fascinating problem because it introduces an extra layer of feedback – the selections made by our stylists. Combining this feedback with direct feedback from our clients to make better recommendations is an interesting and challenging problem.

What is the biggest challenge of leading a data science team?

Hiring and growing a team are constant challenges, not least because there is not much consensus around what data science even is. In my experience a successful data science team needs people with a variety of skills. Hiring people with a command of applied statistics fundamentals is a key element, but having enough engineering experience and domain knowledge can also be important. At Stitch Fix we are fortunate to partner with a very strong data platform team, and this enables us to handle the engineering work that comes with taking on ever more ambitious problems.

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“I often warn data analysts not to underestimate the power of small data” – Interview with Data Mining Consultant Rosaria Silipo https://dataconomy.ru/2015/12/09/i-often-warn-data-analysts-not-to-underestimate-the-power-of-small-data-interview-with-data-mining-consultant-rosaria-silipo/ https://dataconomy.ru/2015/12/09/i-often-warn-data-analysts-not-to-underestimate-the-power-of-small-data-interview-with-data-mining-consultant-rosaria-silipo/#comments Wed, 09 Dec 2015 08:30:46 +0000 https://dataconomy.ru/?p=14548 Rosaria has been a researcher in applications of Data Mining and Machine Learning for over a decade. Application fields include biomedical systems and data analysis, financial time series (including risk analysis), and automatic speech processing. She is currently based in Zurich, Switzerland.   What project have you worked on do you wish you could go back […]]]>

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Rosaria has been a researcher in applications of Data Mining and Machine Learning for over a decade. Application fields include biomedical systems and data analysis, financial time series (including risk analysis), and automatic speech processing. She is currently based in Zurich, Switzerland.

 


What project have you worked on do you wish you could go back to, and do better?

There is not such a thing like the perfect project! As close as you can be to perfection, at some point you need to stop either because the time is over, the money is over or because you just need to have a productive solution. I am sure I can go back to all my past projects and find something to improve in each of them!

This is actually one of the biggest issues in a data analytics projects: when do we stop? Of course, you need to identify some basic deliverables in the project initial phase, without which the project is not satisfactorily completed. But once you have passed these deliverable milestones, when do you stop? What is the right compromise between perfection and resource investment?

In addition, every few years some new technology becomes available which could help re-engineer your old projects, for speed or accuracy or both. So, even the most perfect project solution, after a few years, can surely be improved due to new technologies. This is, for example, the case of the new big data platforms. Most of my old projects would benefit now from a big data based speeding operation. This could help to speed up old models training and deployment, to create more complex data analytics models, and to optimize model parameters better.

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

Use your time to learn! Data Science is a relatively new discipline that combines old knowledge, such as statstics and machine learning, with newer wisdom, like big data platforms and parallel computation. Not many people know everything here, really! So, take your time to learn what you do not know yet from the experts in that area. Combining a few different pieces of data science knowledge probably makes you unique already in the data science landscape. The more pieces of different knowledge, the bigger of an advantage for you in the data science ecosystem!

One way to get easy hands-on experience on a different range of application fields is to explore the Kaggle challenges. Kaggle has a number of interesting challenges up every months and who knows you might also win some money!

What do you wish you knew earlier about being a data scientist?

This answer is related to the previous one, since my advice to young data scientists sprouts from my earlier experience and failures. My early background is in machine learning. So, when I moved my first steps in the data science world many years ago, I thought that knowledge of machine learning algorithms was all I needed. I wish!

I had to learn that data science is the sum of many different skills, including data collection and data cleaning and transformation. The latter, for example, is highly underestimated! In all data science projects I have seen (not only mine), the data processing part takes way more than 50% of the used resources! Including also data visualization and data presentation. A genial solution is worth nothing if the executives and stakeholders do not understand the results by means of a clear and compact representation. And so on. I guess I wish I took more time early on to learn from colleagues with a different set of skills than mine.

How do you respond when you hear the phrase ‘big data’?

Do you really need big data? Sometimes customers ask for a big data platform just because. Then when you investigate deeper you realize that they really do not have and do not want to have such a big amount of data to take care of every day. A nice traditional DWH solution is definitely enough for them.

Sometimes though, a big data solution is really needed or at least it will be needed at some point to keep all company’s’ internal and external data up to date. In these cases, we can work together for a big data solution for their project. Even if this is the case, I often warn data analysts not to underestimate the power of small data! A nice clean general statistical sample might produce higher accuracy in terms of prediction, classification and clustering than a messy large and noisy data lake (a data swamp really)! In some projects, a few data dimensionality selection algorithms were run posteriori, just to see whether all those input dimensions contained useful unique pieces of information or just obnoxious noise. You would be surprised! In some cases we easily passed from more than 200 input variables to less than 10 keeping the same accuracy performance.

There is another phrase though that triggers my inner warning signals: “We absolutely need real time execution”. When I hear this phrase, I usually wonder: “Do you need effective real time responses or would perceived real time responses be enough?”. Perceived real time for me is a few seconds,something that a user can wait without getting impatient. A few seconds however is NOT real time! Any data analytics tool, any deployment workflow can produce a response in a few seconds or even less. Real time is a much faster response usually to trigger some kind of consequent action. In most cases, “perceived real time” is good enough for the human reaction time.

What is the most exciting thing about your field?

Probably, the variety of applications. The whole knowledge of data collection, data warehousing, data analytics, data visualization, results inspection and presentation is transferable to a number of fields. You would be surprised at how many different applications can be designed using a variation of the same data science technique! Once you have the data science knowledge and a particular application request, all you need is imagination to make the two match and find the best solution.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

I always propose a first pilot/investigation mini-project at the very beginning. This is for me to get a better idea of the application specs, of the data set, and yes also of the customer. This is a crucial phase, though short. During this part, in fact, I can take the measures of the project in terms of needed time and resources, and I and the customer we can study each other and adjust our expectations about input data and final results. This initial phase, usually involves a sample of the data, an understanding of the data update strategy, some visual investigation, and a first tentative analysis to produce the requested results. Once this part is successful and expectations have been adjusted on both sides, the real project can start.

You spent sometime as a Consultant in Data Analytics. How did you manage cultural challenges, dealing with stakeholders and executives? What advice do you have for new starters about this?

Ah … I am really not a very good example for dealing with stakeholders and executives and successfully manage cultural challenges! Usually, I rely on external collaborators to handle this part for me, also because of time constraints. I see myself as a technical professional, with little time for talking and convincing. Unfortunately, this is a big part of each data analytics project. However, when I have to deal with it myself, I let the facts speak for me: final or intermediate results of current and past projects. This is the easiest way to convince stakeholders that the project is worth the time and the money. For any occurrence, though, I always have at hand a set of slides with previous accomplishments to present to executives if and when needed.

Tell us about something cool you’ve been doing in Data Science lately.

My latest project was about anomaly detection. I found it a very interesting problem to solve, where skills and expertise have to meet creativity. In anomaly detection you have no historical records of anomalies, either because they rarely happen or because they are too expensive to let them happen.

What you have is a data set of records of normal functioning of the machine, transactions, system, or whatever it is you are observing. The challenge then is to predict anomalies before they happen and without previous historical examples. That is where the creativity comes in. Traditional machine learning algorithms need a twist in application to provide an adequate solution for this problem.

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“I suspect in five years or so, the generalist ‘data scientist’ may not exist” – Interview with Data Scientist Trey Causey https://dataconomy.ru/2015/12/02/i-suspect-in-five-years-or-so-the-generalist-data-scientist-may-not-exist-interview-with-data-scientist-trey-causey/ https://dataconomy.ru/2015/12/02/i-suspect-in-five-years-or-so-the-generalist-data-scientist-may-not-exist-interview-with-data-scientist-trey-causey/#comments Wed, 02 Dec 2015 08:30:31 +0000 https://dataconomy.ru/?p=14498 Trey Causey is a blogger with experience as a professional data scientist in sports analytics and e-commerce. He’s got some fantastic views about the state of the industry.   What project have you worked on do you wish you could go back to, and do better? The easy and honest answer would be to say […]]]>

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Trey Causey is a blogger with experience as a professional data scientist in sports analytics and e-commerce. He’s got some fantastic views about the state of the industry.

 


What project have you worked on do you wish you could go back to, and do better?

The easy and honest answer would be to say all of them. More concretely, I’d love to have had more time to work on my current project, the NYT 4th Down Bot before going live. The mission of the bot is to show fans that there is an analytical way to go about deciding what to do on 4th down (in American football), and that the conventional wisdom is often too conservative. Doing this means you have to really get the “obvious” calls correct as close to 100% of the time as possible, but we all know how easy it is to wander down the path to overfitting in these circumstances…

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences and Social Sciences?

Students should take as many methods classes as possible. They’re far more generalizable than substantive classes in your discipline. Additionally, you’ll probably meet students from other disciplines and that’s how constructive intellectual cross-fertilization happens. Additionally, learn a little bit about software engineering (as distinct from learning to code). You’ll never have as much time as you do right now for things like learning new skills, languages, and methods. For young professionals, seek out someone more senior than yourself, either at your job or elsewhere, and try to learn from their experience. A word of warning, though, it’s hard work and a big obligation to mentor someone, so don’t feel too bad if you have hard time finding someone willing to do this at first. Make it worth their while and don’t treat it as your “right” that they spend their valuable time on you. I wish this didn’t even have to be said.

What do you wish you knew earlier about being a data scientist?

It’s cliche to say it now, but how much of my time would be spent getting data, cleaning data, fixing bugs, trying to get pieces of code to run across multiple environments, etc. The “nuts and bolts” aspect takes up so much of your time but it’s what you’re probably least prepared for coming out of school.

How do you respond when you hear the phrase ‘big data’?

Indifference.

What is the most exciting thing about your field?

Probably that it’s just beginning to even be ‘a field.’ I suspect in five years or so, the generalist ‘data scientist’ may not exist as we see more differentiation into ‘data engineer’ or ‘experimentalist’ and so on. I’m excited about the prospect of data scientists moving out of tech and into more traditional companies. We’ve only really scratched the surface of what’s possible or, amazingly, not located in San Francisco.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

A difficult question along the lines of “how long is a piece of string?” I think the key is to communicate early and often, define success metrics as much as possible at the *beginning* of a project, not at the end of a project. I’ve found that “spending too long” / navel-gazing is a trope that many like to level at data scientists, especially former academics, but as often as not, it’s a result of goalpost-moving and requirement-changing from management. It’s important to manage up, aggressively setting expectations, especially if you’re the only data scientist at your company.

How do you explain to C-level execs the importance of Data Science? How do you deal with the ‘educated selling’ parts of the job? In particular – how does this differ from sports and industry?

Honestly, I don’t believe I’ve met any executives who were dubious about the value of data or data science. The challenge is often either a) to temper unrealistic expectations about what is possible in a given time frame (we data scientists mostly have ourselves to blame for this) or b) to convince them to stay the course when the data reveal something unpleasant or unwelcome.

What is the most exciting thing you’ve been working on lately and tell us a bit about it.

I’m about to start a new position as the first data scientist at ChefSteps, which I’m very excited about, but I can’t tell you about what I’ve been working on there as I haven’t started yet. Otherwise, the 4th Down Bot has been a really fun project to work on. The NYT Graphics team is the best in the business and is full of extremely smart and innovative people. It’s been amazing to see the thought and time that they put into projects.

What is the biggest challenge of leading a data science team?

I’ve written a lot about unrealistic expectations that all data scientists be “unicorns” and be experts in every possible field, so for me the hardest part of building a team is finding the right people with complementary skills that can work together amicably and constructively. That’s not special to data science, though.

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“To avoid losing great people, they need to be developing all the time.” – Interview with GoCardlesses Natalie Hockham https://dataconomy.ru/2015/11/27/to-avoid-losing-great-people-they-need-to-be-developing-all-the-time-interview-with-gocardlesses-natalie-hockham/ https://dataconomy.ru/2015/11/27/to-avoid-losing-great-people-they-need-to-be-developing-all-the-time-interview-with-gocardlesses-natalie-hockham/#respond Fri, 27 Nov 2015 08:30:46 +0000 https://dataconomy.ru/?p=14480 Natalie leads the data team at GoCardless, a London startup specialising in online direct debit. She cut her teeth as a PhD student working on biomedical control systems before moving into finance, and eventually Fintech. She is particularly interested in signal processing and machine learning and is presently swotting up on data engineering concepts, some […]]]>

Natalie leads the data team at GoCardless, a London startup specialising in online direct debit. She cut her teeth as a PhD student working on biomedical control systems before moving into finance, and eventually Fintech. She is particularly interested in signal processing and machine learning and is presently swotting up on data engineering concepts, some knowledge of which is a must in the field.


What project have you worked on do you wish you could go back to, and do better?

Before I joined a startup, I was working as an analyst on the trading floor of one of the oil majors. I spent a lot of time building out models to predict futures timespreads based on our understanding of oil stocks around the world, amongst other things. The output was a simple binary indication of whether the timespreads were reasonably priced, so that we could speculate accordingly. I learned a lot about time series regression during this time but worked exclusively with Excel and eViews. Given how much I’ve learned about open source languages, code optimisation, and process automation since working at GoCardless, I’d love to go back in time and persuade the old me to embrace these sooner.

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

Don’t underestimate the software engineers out there! These guys and girls have been coding away in their spare time for years and it’s with their help that your models are going to make it into production. Get familiar with OOP as quickly as you can and make it your mission to learn from the backend and platform engineers so that you can work more independently.

What do you wish you knew earlier about being a data scientist?

It’s not all machine learning. I meet with some really smart candidates every week who are trying to make their entrance into the world of data science and machine learning and this field is never far from the front of their minds. The truth is machine learning is only a small part of what we do. When we do undertake projects that involve machine learning, we do so because they are beneficial to the company, not just because we have a personal interest in them. There is so much other work that needs to be done including statistical inference, data visualization, and API integrations. And all this fundamentally requires spending vast amounts of time cleaning data.

How do you respond when you hear the phrase ‘big data’?

I haven’t had much experience with ‘big data’ yet but it seems to have superseded ‘machine learning’ on the hype scale. It definitely sounds like an exciting field – we’re just some way off going down this route at GoCardless.

What is the most exciting thing about your field?

Working in data is a great way to learn about all aspects of a business and the lack of engineering resources that characterizes most startups means that you are constantly developing your own skill set. Given how quickly the field is progressing, I can’t see myself reaching saturation in terms of what I can learn for a long time – that makes me really happy.

How do you go about framing a data problem – in particular, how do you avoid spending too much time on a problem and , how manage expectations?

Our 3 co-founders all started out as management consultants and the importance of accurately defining a problem from the outset has been drilled into us. Prioritisation is key – we mainly undertake projects that will generate measurable benefits right now. Before we start a project, we check that the problem actually exists (you’d be surprised how many times we’ve avoided starting down the wrong path because someone has given us incorrect information). We then speak to the relevant stakeholders and try to get as much context as possible, agreeing a (usually quantitative) target to work towards. It’s usually easy enough to communicate to people what their expectations should be. Then the scoping starts within the data team and the build begins. It’s important to recognise that things may change over the course of a project so keeping everyone informed is essential. Our system isn’t perfect yet but we’re improving all the time.

How do you explain to C-level execs the importance of Data Science? How do you deal with the ‘educated selling’ parts of the job?

Luckily, our management team is very embracing of data in general. Our data team naturally seeks out opportunities to meet with other data professionals to validate the work we’re doing. We try hard to make our work as transparent as possible to the rest of the company by giving talks and making our data widely available, so that helps to instill trust. Minor clashes are inevitable every now and then, which can put projects on hold, but we often come back to them later when there is a more compelling reason to continue.

What is the most exciting thing you’ve been working on lately and tell us a bit about GoCardless.

We’ve recently overhauled our fraud detection system, which meant working very closely with the backend engineers for a prolonged period of time – that was a lot of fun. GoCardless is an online direct debit provider, founded in 2011. Since then, we’ve grown to 60+ employees, with a data team of 3. Our data is by no means ‘big’ but it can be complex and derives from a variety of sources. We’re currently looking to expand our team with the addition of a data engineer, who will help to bridge the gap between data and platform.

What is the biggest challenge of leading a data science team?

The biggest challenge has been making sure that everyone is working on something they find interesting most of the time. To avoid losing great people, they need to be developing all the time. Sometimes this means bringing forward projects to provide interest and raise morale. Moreover, there are so many developments in the field that its hard to keep track, but attending meetups and interacting with other professionals means that we are always seeking out opportunities to put into practice the new things that we have learned.

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“Open source and public cloud are the most impactful shifts I have seen.” – Interview with Google Cloud Platform’s William Vambenepe https://dataconomy.ru/2015/11/23/open-source-and-public-cloud-are-the-most-impactful-shifts-i-have-seen-interview-with-google-cloud-platforms-william-vambenepe/ https://dataconomy.ru/2015/11/23/open-source-and-public-cloud-are-the-most-impactful-shifts-i-have-seen-interview-with-google-cloud-platforms-william-vambenepe/#comments Mon, 23 Nov 2015 07:30:45 +0000 https://dataconomy.ru/?p=14465 William Vambenepe is the Lead Project Manager for Big Data at Google Cloud Platform. Dataconomy interviewed him about his career path, his current role and how he sees the industry changing. You’ve worked for some of the biggest names in the industry (HP, Oracle, Google), what stands out to you about these companies? All of […]]]>

William Vambenepe is the Lead Project Manager for Big Data at Google Cloud Platform. Dataconomy interviewed him about his career path, his current role and how he sees the industry changing.


You’ve worked for some of the biggest names in the industry (HP, Oracle, Google), what stands out to you about these companies?

All of the places I’ve worked are great, Google really stands out for me because of its appetite to tackle big problems and try hard things. And the technical expertise and infrastructure are available to actually make it happen.

You studied Computer Science at Ecole Centrale de Paris and University of Cambridge, then switched to Industrial Engineering at Stanford. What have been the biggest influences from your academic life?

Ecole Centrale is where I really learned to code (mostly as part of the student computer club, VIA) and where I discovered the Internet and the then-nascent WWW. In Cambridge, I learned some of the CS theory behind what I was already doing. At Stanford I discovered how anyone could potentially have a huge impact by using Computer Science. That’s why I decided to stay in the Bay Area.

What type of problems do you focus on solving at Google?

The main focus of my work in Google Cloud Platform is empowering users by giving them access to super-productive tools which don’t require complex setup or a long-term commitment. Anyone with an idea can implement it quickly and easily, try it out, and iterate on it. If the idea doesn’t work out, the sunk cost is very low (a few hours of coding and a few dollars of infrastructure). If it works, the resulting system is not a throwaway prototype. It is a cloud application which is already able to scale infinitely and support production workloads.

In 17 years of working as a technologist and product manager, what are the significant shifts you have seen in the industry?

Open source and public cloud are the most impactful shifts I have seen. Both have redefined the dynamics of the industry, and in both cases they have done that in a way which levels the playing field between incumbents and new entrants. Actually, it might have even given the new entrants a leg up in many ways over the most nimble incumbents. It has resulted in a burst of innovation and creativity.

If you could give one piece of advice to aspiring tech product managers, what would it be?

First of all to be sure they really want to go this way. Most PMs I work with, including myself, started as software engineers. And while becoming a PM doesn’t mean you’re giving up on coding, it does mean you’re giving up on being able to focus on solving deep technical problems. There’s a lot of satisfaction in a good software engineering career and it’s very hard to go back, so think about it carefully and don’t rush into a transition.

Once embarked on the PM journey, my main advice is to work hard to keep yourself intellectually honest. There are many ways you can rationalize taking the obvious or easy way (going with the flow, building what the engineers want to build because it’s technically interesting, copying the competition, etc…) but that’s not what a PMs job should be. The role of the PM is to focus on finding and injecting the non-obvious into the product strategy.

Of the trends you see in the technology market today, do any particularly excite or trouble you?

Recent advances in Machine Learning are the most exciting development in the industry right now. On the big data side, there’s still a lot of work to make everything completely easy, cheap, integrated and reliable, but the problem of storing and processing data at large scale is more or less solved.The next challenge is how best to use this data for human progress; Machine Learning, especially its Deep Learning branch is the most promising direction to solve this.

If you could tackle any technology-solvable challenge existing today, which would it be – and why?

There are few challenges which are only technological. For example, the health care system (especially in the US) could benefit from a huge boost from applying technology in the way technology has propelled so many other sectors forward. There are obviously technical issues involved, but they are not the blocker. I’d love to help make progress there.

What have been the most valuable resources for you to develop your career and professional ability?

Other than coffee, presumably? It would have to be my colleagues. I’ve been fortunate to work closely and for long periods of time with a few truly exceptional colleagues. By the way, that’s one reason why I tend to advise against too frequent job-hopping. The quality of the professional relationships that you build have a strong dependency on how closely and how long you work with someone. You don’t learn from someone by seeing them from afar or having “mentoring sessions” (at least in my experience). It’s much more or a two-way street and it only works if you are working intently on hard problems together for sustained periods of time.

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“Spend time learning the basics” – An Interview with Data Scientist Thomas Wiecki https://dataconomy.ru/2015/11/16/spend-time-learning-the-basics-an-interview-with-a-data-scientist-thomas-wiecki/ https://dataconomy.ru/2015/11/16/spend-time-learning-the-basics-an-interview-with-a-data-scientist-thomas-wiecki/#comments Mon, 16 Nov 2015 08:30:33 +0000 https://dataconomy.ru/?p=14412 Thomas is Data Science Lead at Quantopian Inc which is a crowd-sourced hedge fund and algotrading platform. Thomas is a cool guy and came to give a great talk in Luxembourg last year – which I found so fascinating that I decided to learn some PyMC3 Follow Peadar’s series of interviews with data scientists here. […]]]>

Thomas is Data Science Lead at Quantopian Inc which is a crowd-sourced hedge fund and algotrading platform. Thomas is a cool guy and came to give a great talk in Luxembourg last year – which I found so fascinating that I decided to learn some PyMC3

Follow Peadar’s series of interviews with data scientists here.


What project have you worked on do you wish you could go back to, and do better?

While I was doing my masters in CS I got a stipend to develop an object recognition framework. This was before deep learning dominated every benchmark data set and bag-of-features was the way to go. I am proud of the resulting software, called Pynopticon, even though it never gained any traction. I spent a lot of time developing a streamed data piping mechanism that was pretty general and flexible. This was in anticipation of the large size of data sets. In retrospect though it was overkill and I should have spent less time coming up with the best solution and instead spend time improving usability! Resources are limited and a great core is not worth a whole lot if the software is difficult to use. The lesson I learned is to make something useful first, place it into the hands of users, and then worry about performance.

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

Spend time learning the basics. This will make more advanced concepts much easier to understand as it’s merely an extension of core principles and integrates much better into an existing mental framework. Moreover, things like math and stats, at least for me, require time and continuous focus to dive in deep. The benefit of taking that time, however, is a more intuitive understanding of the concepts. So if possible, I would advise people to study these things while still in school as that’s where you have the time and mental space. Things like the new data science tools or languages are much easier to learn and have a greater risk of being ‘out-of-date’ soon. More concretely, I’d start with Linear Algebra (the Strang lectures are a great resource) and Statistics (for something applied I recommend Kruschke’s Doing Bayesian Analysis, for fundamentals “The Elements of Statistical Learning” is a classic).

What do you wish you knew earlier about being a data scientist?

How important non-technical skills are. Communication is key, but so are understanding business requirements and constraints. Academia does a pretty good job of training you for the former (verbal and written), although mostly it is assumed that communicate to an expert audience. This certainly will not be the case in industry where you have to communicate your results (as well as how you obtained them) to people with much more diverse backgrounds. This I find very challenging.

[bctt tweet=”Communication is key, but so are understanding business requirements and constraints.”]

As to general business skills, the best way to learn is probably to just start doing it. That’s why my advice for grad-students who are looking to move to industry would be to not obsess over their technical skills (or their Kaggle score) but rather try to get some real-world experience.

How do you respond when you hear the phrase ‘big data’?

As has been said before, it’s quite an overloaded term. On one side, it’s a buzzword in business where I think the best interpretation is that ‘big data’ actually means that data is a ‘big deal’ — i.e. the fact that more and more people realize that by analyzing their data they can have an edge over the competition and make more money.

Then there’s the more technical interpretation where it means that data increases in size and some data sets do not fit into RAM anymore. I’m still undecided of whether this is actually more of a data engineering problem (i.e. the infrastructure to store the data, like hadoop) or an actual data science problem (i.e. how to actually perform analyses on large data). A lot of times, as a data scientist I think you can get by by sub-sampling the data (Andreas Müller has a great talk of how to do ML on large data sets, here).

Then again, more data also has the potential to allow us to build more complex models that capture reality more accurately, but I don’t think we are there yet. Currently, if you have little data, you can only do very simple things. If you have medium data, you are in the sweet spot where you can do more complex analyses like Probabilistic Programming. However, with “big data”, the advanced inference algorithms fail to scale so you’re back to doing very simple things. This “big data needs big models” narrative is expressed in a talk by Michael Betancourt, here.

What is the most exciting thing about your field?

The fast pace the field is moving. It seems like every week there is another cool tool announced. Personally I’m very excited about the blaze ecosystem including dask which has a very elegant approach to distributed analytics which relies on existing functionality in well established packages like pandas, instead of trying to reinvent the wheel. But also data visualization is coming along quite nicely where the current frontier seems to be interactive web-based plots and dashboards as worked on by bokeh, plotly and pyxley.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

I try to keep the loop between data analysis and communication to consumers very tight. This also extends to any software to perform certain analyses which I try to place into the hands of others even if it’s not perfect yet. That way there is little chance to ween off track too far and there is a clearer sense of how usable something is. I suppose it’s borrowing from the agile approach and applying it to data science.


(image credit: Lauren Manning, CC2.0)

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“Justify the business value behind your work.” – Interview with Data Scientist Ian Ozsvald https://dataconomy.ru/2015/11/13/justify-the-business-value-behind-your-work-interview-with-data-scientist-ian-ozsvald/ https://dataconomy.ru/2015/11/13/justify-the-business-value-behind-your-work-interview-with-data-scientist-ian-ozsvald/#respond Fri, 13 Nov 2015 08:30:12 +0000 https://dataconomy.ru/?p=14406 Ian is an Entrepreneurial Geek, 30-late-ish, living in London (after 10 years in Brighton and a year in Latin America). Ian is the owner of an Artificial Intelligence consultancy and author of ‘The Artificial Intelligence Cookbook’which teaches you how to add clever algorithms to your software to make it smarter! One of his mobile products […]]]>

Ian is an Entrepreneurial Geek, 30-late-ish, living in London (after 10 years in Brighton and a year in Latin America).

Ian is the owner of an Artificial Intelligence consultancy and author of ‘The Artificial Intelligence Cookbook’which teaches you how to add clever algorithms to your software to make it smarter! One of his mobile products is SocialTies (built with RadicalRobot).

Follow Peadar’s series of interviews with data scientists here.


What project have you worked on do you wish you could go back to, and do better?

My most frustrating project was (thankfully) many years ago. A client gave me a classification task for a large number of ecommerce products involving NLP. We defined an early task to derisk the project and the client provided representative data, according to the specification that I’d laid out. I built a set of classifiers that performed as well as a human and we felt that the project was derisked sufficiently to push on. Upon receiving the next data set I threw up my arms in horror – as a human I couldn’t solve the task on this new, very messy data – I couldn’t imagine how the machine would solve it. The client explained that they wanted the first task to succeed so they gave me the best data they could find and since we’d solved that problem, now I could work on the harder stuff. I tried my best to explain the requirements of the derisking project but fear I didn’t give a deep enough explanation to why I needed fully-representative dirty data rather than cherry-picked good data. After this I got *really* tough when explaining the needs for a derisking phase.

What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

You probably want an equal understanding of statistics, linear algebra and engineering, with multiple platforms and languages plus visualisation skills. You probably want 5+ years experience in each industrial domain you’ll work in. None of this however is realistic. Instead focus on some areas that interest you and that pay well-enough and deepen your skills so that you’re valuable. Next go to open source conferences and speak, talk at meetups and generally try to share your knowledge – this is a great way of firming up all the dodgy corners of your knowledge. By speaking at open source events you’ll be contributing back to the ecosystem that’s provided you with lots of high quality free tools. For me I speak, teach and keynote at conferences like PyDatas, PyCons, EuroSciPys and EuroPythons around the world and co-run London’s most active data community at PyDataLondon. Also get involved in supporting the projects you use – by answering questions and submitting new code you’ll massively improve the quality of your knowledge.

What do you wish you knew earlier about being a data scientist?

I wish I knew how much I’d miss not paying attention to classes in statistics and linear algebra! I also wish I’d appreciated how much easier conversations with clients were if you have lots of diagrams from past projects and projects related to their data – people tend to think visually, they don’t work well from lists of numbers.

How do you respond when you hear the phrase ‘big data’?

Most clients don’t have a Big Data problem and even if they’re storing huge volumes of logs, once you subselect the relevant data you can generally store it on a single machine and probably you can represent it in RAM. For many small and medium sized companies this is definitely the case (and it is definitely-not-the-case for a company like Facebook!). With a bit of thought about the underlying data and its representation you can do things like use sparse arrays in place of dense arrays, use probabilistic counting and hashes in place of reversible data structures and strip out much of the unnecessary data. Cluster-sized data problems can be made to fit into the RAM of a laptop and if the original data already fits on just 1 hard-drive then it almost certainly only needs a single machine for analysis. I co-wrote O’Reilly’s High Performance Python and one of the goals of the book was to show that many number-crunching problems work well using just 1 machine and Python, without the complexity and support-cost of a cluster.

What is the most exciting thing about your field?

We’re stuck in a world of messy, human-created data. Cleaning it and joining it is currently a human-level activity, I strongly suspect that we can make this task machine-powered using some supervised approaches so less human time is spent crafting regular expressions and data transformations. Once we start to automate data cleaning and joining I suspect we’ll see a new explosion in the breadth of data science projects people can tackle.

How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

To my mind the trick is figuring out a) how good the client’s data is and b) how valuable it could be to their business if put to work. You can justify any project if the value is high enough but first you have to derisk it and you want to do that as quickly and cheaply as possible. With 10 years of gut-feel experience I have some idea about how to do this but it feels more like art than science for the time being. Always design milestones that let you deliver lumps of value, this helps everyone stay confident when you hit the inevitable problems.

You spent sometime as a Consultant in Data Analytics. How did you manage cultural challenges, dealing with stakeholders and executives? What advice do you have for new starters about this?

Justify the business value behind your work and make lots of diagrams (stick them on the wall!) so that others can appreciate what you’re doing. Make bits of it easy to understand and explain why it is valuable and people will buy into it. Don’t hide behind your models, instead speak to domain experts and learn about their expertise and use your models to backup and automate their judgement, you’ll want them on your side.

[bctt tweet=”Justify the business value behind your work. #datascience”]

You have a cool startup can you comment on how important it is as a CEO to make a company such as that data-driven or data-informed?

My consultancy (ModelInsight.io) helps companies to exploit their data so we’re entirely data-driven! If a company has figured out that it has a lot of data and it could steal a march on its competitors by exploiting this data, that’s where we step in. A part of the reason I speak internationally is to help companies think about the value in their data based on the projects we’ve worked on previously.


(image credit: Carmen Escobar Carrio, CC2.0)

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