Narrative Science has been a regular feature on Dataconomy over the past year, from Chief Scientist Kris Hammond’s post about the impact of artificial intelligence on banking, to the launch of their Quill Connect application for processing unstructured text data from social media.

I think for AI in general, the goal is not to make the machine smarter and destroy us, but to make machines smarter and as a result, put us in a position where we no longer have to deal with the machine, as an unintelligent device which requires frequent input and supervision. We can deal with the machine as a partner, whose job is to make us smarter. We get smarter because it gets smarter. Because who in the world wants to actually look at a spreadsheet, or figure out what’s going on in the visualization, or go to massive textual data to get the answer to a question? No one wants to do that. As the machine takes more and more of that on, our lives become more human.

– Kris Hammond, “Why AI Isn’t Going to Kill You or Take Your Job“

As a company that specializes in using Big Data and machine learning algorithms to form narratives, who better to tell the story of how artificial intelligence has transformed various industries over the last 65 years? They were kind enough to share the infographic below with us:

(Image credit: Luis Vidal, CC2.0)

Only a month into the year, and already several of our expert’s predictions for 2015 in big data are coming into fruition. 2015 is certainly looking like the year of AI & automation, with all three of this week’s most-shared news pieces below focusing around prediction and AI. 2015 may, too, be the year of the economists; both Kris Hammond of Narrative Science and Gabriel Lowy of Tech-Tonics Advisors published excellent pieces on the huge big data opportunities for the financial industry.

TOP DATACONOMY ARTICLES

10 Internet of Things Influencers You Need to Know

“It is a truth (almost) universally acknowledged that the Internet of Things is going to revolutionise how we live, work and think. Although broaching this field can be daunting, it is certainly worth looking in to the fascinating applications and technology associated with this field- if you’re looking for the latest insights into how IoT will shape our future, this list is a great place to start.”

3 Reasons Why Banks Can’t Afford to Ignore AI

“While companies use many approaches to extract value from data they capture, organize and store, it is only recently that we have seen a new class of technologies emerge providing real impact — those powered by Artificial Intelligence, aka AI.”  
 

Aerospike’s New CEO John Dillon on the 2015 Roadmap for Database Tech: Less Hype, Better Technology

Yesterday, NoSQL innovators Aerospike announced that Silicon Valley veteran John Dillon will be stepping into the breach as their new CEO. He’s got big plans for the company- largely revolving around less marketing buzz, and more love for developers. His ambitious roadmap is definitely worth a read.

TOP DATACONOMY NEWS

Eve Stumbles Upon a Possible Cure for Malaria. Eve is a Robot Scientist.

A laboratory automation system that harnesses artificial intelligence (AI) techniques in order to glean and understand scientific data constant experimentation is called a Robot Scientist. Eve is such a Robot Scientist and if certain U.K. researchers have it right, then Eve might have stumbled upon a possible fighting chance against malaria.

This Programme Knows If Your Startup Will Be Successful

The startup landscape is a minefield, where even the most brilliant ideas can fall by the wayside without proper implementation and business acumen. This is where Thomas Thurston comes in. Thurston believes he has developed a predictive algorithm which can mitigate some of the risk involved with starting and investing in a new business.

Am I Going Down? Uses Flight Data to Calculate Odds of Your Next Flight Crashing

“A new iOS app Am I Going Down? refutes the claim that there’s a “one in a million chance” of your plane crashing. In fact, if you’re on a Boeing 747-700 flight from San Francisco to Dallas, there’s a 1 in 4,593,011 chance you’ll crash.”  

TOP UPCOMING EVENTS

23-24 February, 2015- Eleventh International Conference on Technology, Knowledge, and Society, CA

Conference themes this year include: Technologies for Human Use, Technologies in Community, Technologies for Learning and Technologies for Common Knowledge.  
 

12-13 February, 2015- Apache Hadoop Innovation Summit, San Diego CA

“Hadoop, a huge piece of the puzzle, continues to present both exciting opportunities and engineering challenges. Can you become cloud native? What new alternative paradigms are available with Hadoop? What are the limitations of sole Hadoop use? How can you use it for machine learning. What about Integration? Corporate Accessibility? Ethics? These burning issues are what the summit looks to address.”

TOP DATACONOMY JOBS

Sr. Data Engineer (m/f) –NumberFour AG   

“As our Sr. Data Engineer you are responsible for the planning and implementing of scalable, stable and high-performance scoring systems.”

Physicist / Mathematician / Computer Scientist as Data Scientist, Blue Yonder

If you would like to be part of a highly innovative, challenging and extremely future-oriented software market, and a young and highly motivated team, then please send us your detailed application.

The promise of Big Data and our ability to use it was a lofty one. Unfortunately, many Big Data efforts have turned out to be extremely costly, both in terms of the investment and the human capital required to draw insight out of it.

While companies use many approaches to extract value from data they capture, organize and store, it is only recently that we have seen a new class of technologies emerge providing real impact — those powered by Artificial Intelligence, aka AI.

No matter what you call it — “cognitive computing,” “machine learning,” “cognitive analysis,” “smart machines” or even “intelligent automation” — AI is enjoying a renaissance now, not simply because of the promise it holds for the future but because of the impact it is having on businesses today.

In banking in particular, where data drives decision-making, reporting and customer communication, AI applications are already transforming the way business is getting done.  The three areas where this impact is most recognizable are reporting, advising and alerting.

Communication and reporting has always been a focus for banks.  They employ significant resources to report on the performance of portfolios, client investment strategies, market conditions, fraud and compliance. Generating such reports is a time-consuming effort even for the most skilled and experienced analysts  — and that is time that could be better spent elsewhere.

When it comes to reporting, we are seeing tremendous impact from AI in the form of systems that can instantaneously analyze data, reason about what that analysis means and then generate natural language reports that inform many audiences.  Built on a combination of data analytics and reasoning systems, these new AI engines are able to bridge gaps between data that machines need for analysis and the language-based communication that humans need to understand it and take action.

Money laundering, fraud and compliance reporting have always taxed banking resources, and to mitigate costs banks have come to rely more on technology-based fraud detection systems.  The issues with such systems are twofold – 1) they have to be responsive to new approaches fraudsters are taking to hide irregularities, and 2) they need to be able to recognize patterns of irregularity using both structured and unstructured data.

For the former, AI systems based on machine learning techniques can predict irregular activity.  In much the same way that machine learning has been used for years to improve algorithmic trading systems, it is now being used to protect our financial system.

For the latter, natural language processing (NLP) systems are uncovering subtle cues in transactions that might indicate behaviors not seen in the numbers but seen in text.  So, here the AI is aimed at understanding what the text means and transforming it into structured data.  Although in the early stages, it is already improving organizations’ ability to recognize and respond to problems at scale.

We have also seen AI systems offering client-facing advice.  On the small side, wallet.AI is beginning to provide advice to end users based on an analysis of their personal finance data combined with an understanding of goals.  Advice is driven by the system having a “conceptual awareness” of client goals and needs.  On a larger scale, USAA is deploying an investment advisory system for veterans using IBM’s Watson.  Based on a combination of large scale data repositories and Watson’s “cognitive computing” model, USAA will be using the systems to give veterans financial advice related to their transition from military to civilian life.

Both of these models are based on the concept that some advice is a product of data and best practice rather than deep expertise.  For wallet.AI, the system analyzes personal financial data to create a user profile.  Combined with other information, that profile is used to drive a best practice advisory engine.  USAA has a data set consisting of best practice documents, and Watson is the intelligent access engine that maps information about a client to the advice that best serves them.

The world of data and our ability to deal with it at scale has always been highly mechanical, which has limited our ability to extract all of the value we need.  We are now entering an age where we can combine the human skills of interpretation, communication and language understanding with the machine’s ability to scale, resulting in powerful systems that allow us to focus on more strategic work and provide meaningful information to any audience.

In addition to being Narrative Science’s Chief Scientist, Kris Hammond is a professor of Computer Science and Journalism at Northwestern University. Prior to joining the faculty at Northwestern, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has been primarily focused on artificial intelligence, machine-generated content and context-driven information systems. Kris currently sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). He received his PhD from Yale.

Follow @DataconomyMedia

(Image credit: Pixabay)

2014 was a fantastic year for data science. Funding rounds were huge, the mergers and acquistions space was active all year, data science skills proved to be the hottest of the year. But will data science continue to flourish in 2015? We asked four industry experts- working in AI, big data strategy, Hadoop and data transformation respectively- to share their thoughts on how big data will progress in 2015.

1. Data Scientists Not So Sexy in 2015

“In 2015, CEOs will demand more from their data than the elusive “big insight” that data scientists keep promising but haven’t been able to deliver.They will decrease investments in human-powered data science and adopt scalable automation solutions that understand data, unlock insights trapped in it and then provide answers to ongoing problems of understanding performance, logistics, provisioning and HR just to name a few.”

Kris Hammond, Chief Scientist for Narrative Science
Read our interview with Kris here.

2. Big Data Goes Mainstream in the Enterprise

In 2014 one of the things that we noticed changing rapidly in Big Data was its increasing enterprise focus. Adoption of open source platforms like Hadoop was originally limited to specific applications within early adopters like ad-tech and global web properties. But today, more and more mainstream companies view Big Data as a must-have. Manufacturing companies, for example, are now able to combine reliability and performance data from the field with testing data from the factory to help design and build better and more profitable products. Expect to see Big Data make major impacts on the competitive landscape in 2015. Companies which effectively embrace and deploy these solutions will expand their market and profit shares at the expense of lagging competitors.

Ron Bodkin, Founder of ThinkBig
Read all of Ron’s predictions here.

3. Self-Service Big Data Goes Mainstream

In 2015, IT will embrace self-service Big Data to allow business users self service to big data. Self-service empowers developers, data scientists and data analysts to conduct data exploration directly. Previously, IT would be required to establish centralized data structures. This is a time consuming and expensive step. Hadoop has made the enterprise comfortable with structure-on-read for some use cases. Advanced organizations will move to data bindings on execution and away from a central structure to fulfill ongoing requirements. This self service speeds organizations in their ability to leverage new data sources and respond to opportunities and threats.

John Schroeder, CEO of MapR

4. Data Science Will Belong to the Economists

We will start to see data science (to the extent that it operates as a coherent entity) increasingly rely on the domain expertise of economists. The early days of data science were very math, statistics and programming oriented. Then there was the rise of the “computational social scientist,” which added sociology to the mix.

Many trend setting data science places are finding that sociology, and similar disciplines, tend to be retrospective, while other fields, like economics, offer simulation and auction modeling and other techniques to get more proactive and predictive with data. Of course, most economists don’t have the programming chops to land most data science jobs, but I think we’ll see that start to change significantly.

Tye Rattenbury, Data Scientist at Trifacta & Former Data Scientist at Facebook
Read our interview with Tye here.

Follow @DataconomyMedia

(Image credit: “Happy New Year” by Peter Thoeny)

We recently had the opportunity to sit down with Kris Hammond, the Chief Scientist for Narrative Science. Narrative Science focuses around automating text generated from data, turning raw data into insightful accounts. Hammond has spent over 20 years working in and developing the AI labs at the University of Chicago and Northwestern University, making him uniquely placed to offer perspectives on the past, present and future of AI. In the first part of our discussion, we discussed the technologies which will shape the future of machine learning; in this installment, Hammond discusses the future of AI, and whether or not robots could actually wipe out humanity and steal our jobs.

When we talk about AI, almost anyone you talk with will say that they think that AI image- the genuine artificial intelligence that is building a system as intelligent, if not more intelligent than a human being- is simply not feasible or possible. Unless we start talking about machines killing us, and then the response is “Oh my god, we have to be terrified of this”.

I think the reality is that we have complete flexibility in terms of building the things that we’re going to build. In order to be a true AI, the future of AI is going to have a goal structure associated with it. Really, all you need to do is make sure that one of the higher priority goals is don’t kill everybody. I know Elon Musk is a very present figure, very smart man, but what I’m worried about existential threats, I’m actually a little more worried about New York being underwater in 30 years. That worries me alot more than the vague possibility of AI which decides to hunt us down and kill us.  In fact from a Narrative Science point of view, we look at what we do when we think, what’s Quill going to do? Explain someone to death? Because  that’s what it does: explaining things.

So I think when we get a little further down the line, and we get closer and closer to what looks like a genuine, complete AI systems, that’s when it’s time to consider, “Okay, what are the constraints going to be?” But the notion that we should start regulating now, as Musk suggests? I think that’s absurdist. There is no point in regulating something that is a glint at this point in people’s eyes. Now, I actually do believe that we will have complete AI. I believe that people are causal beings and that AI and computers live in the same causal environment, and we will have machines that are as- if not more- intelligent than we are. Maybe in my lifetime.

But it’s not time to worry about killing sprees quite yet. Although my concern is that right now a third of the marriages in United States at least, were the result of online dating. Which means that there are algorithms out there that are actually determining the breeding habits of people in the United States. If I were an AI, I wouldn’t blow everyone up. I’d just insert myself into that process and just make sure that system matched up people who were nice and calm, and make the entire species calm for the  rest of time.

For a lot of people historically, AI has meant ‘killer robots’. I understand that. But nowadays, there seems to be this huge focus on AI stepping in and taking over jobs, and automation in general. And most for most of us, there’s still a focus on the blue collar side, but I think that there’s a growing awareness of the white collar side.

I think the reality is that AI is not going to take over jobs; it’s going to take over work. If you look at the work that Watson’s taking on, that Narrative Science is taking on, it’s the work that’s not particularly interesting or enjoyable for people. Having Narrative Science step in to look at the data and do the reporting means that the people who were doing that reporting can step away from doing commodity work and they can actually start working on what a data scientist or an analyst should be doing. They can focus on more speculative work, more discovery work, exploratory work against that data, to find new things instead of reporting on the things they have already found.

I think for AI in general, the goal is not to make the machine smarter and destroy us, but to make machines smarter and as a result, put us in a position where we no longer have to deal with the machine, as an unintelligent device which requires frequent input and supervision. We can deal with the machine as a partner, whose job is to make us smarter. We get smarter because it gets smarter. Because who in the world wants to actually look at a spreadsheet, or figure out what’s going on in the visualization, or go to massive textual data to get the answer to a question? No one wants to do that. As the machine takes more and more of that on, our lives become more human.

And so, AI moving forward is part of the process of actually more deeply humanizing us in our work, in our lives, in our thinking. I think there will be a moment where we embrace that finally, but I wish we could get to it. Understand the excitement of having intelligent partners, whose job is to help us and help move us forward, and to give us more of what it means to be human.

Follow @DataconomyMedia

(Image credit: Saad Faruque)

We recently had the opportunity to sit down with Kris Hammond, the Chief Scientist for Narrative Science. Narrative Science focuses around automating text generated from data, turning raw data into insightful accounts. Hammond has spent over 20 years working in and developing the AI labs at the University of Chicago and Northwestern University, making him uniquely placed to offer perspectives on the past, present and future of AI. In this first part of our discussion, Hammond discusses three game-changing technologies that he considers to be key in the future of machine learning.

In the near future, I think what we’re going to see is the acceleration of certain ideas that will actually have a commercial and societal impact. The three areas where I think we’re going to see amazing accelerations are everything associated with automated driving and auxiliary uses of the same technology. So an automated factory that can control automated robotics. We’re going to see an explosion of that.

With the Watson model, I think IBM has to get a handle on two things: One, how to quickly configure Watson for new domains, which is still a bottleneck for them, and how to actually present their results not just as answers, but the answers and explanations. I think that once those two nuts are cracked, we’re going to see a massive flow of decision support systems that are based upon the Watson technology. I think, for lack of a better term, disruptive in the white collar world, in a way that we can’t imagine. People think that the white collar world is somehow protected, but I don’t see it as being protected from the incursion of highly intelligent systems.

Third is the communications layer (and that is me, talking about my company). I think that one of the things that hold people back in terms of understanding the world is an inability to look at data directly and understand what’s happening in the world based upon the data alone. What we’re going to see is a rise of applications based upon Narrative Science technology that allow people to understand everything that’s going on in the world that the machine has been capturing for all these years, and how it keeps expanding in terms of capturing that information, and understand that not because they’re data scientists, not because they know how to do analysis, not because they know how to read a spreadsheet, or look at a visualisation, they’re going to be able to understand it because they can read something that has been indirectly making them understand it. And that thing will be written by Quill.

So I think that super organisation around the process of moving things around, the cars, the further development of Watson technology to really push into the white collar world of decision-making, and then communication coming from the data layer that has been interpreted by Quill or other similar or identical technologies.

Follow @DataconomyMedia

(Image credit: Pascal)

Quill is an automated narrative generation platform for the enterprise that  creates perfectly written narratives to convey meaning for any intended audience. It was invented by Chicago based tech firm Narrative Science.

“So, how does Quill work in the world of social media, where “data” is unstructured text?  What can a system that lives and breathes structured data do with language that can be sophisticated, ambiguous and messy?”

Twitter proved to be a viable source of such unstructured data. Narrative Science utilised Quill to provide tweeters – from causal tweeters to “VITs” (Very Important Tweeters) – with their personal stories, which includes information about their sphere of influence they can act on.

“We quickly realised that a small amount of work would allow us to extract enough data from the texts we were processing to generate a story.  In other words, we could do for Quill what Quill does for others – give it the information in a form most natural for it – structured data – so that it could provide us with the information in a form most natural for us – plain English language,” explains a news release about the problem of machine not understanding the massive data available on the social networking site.

“We decided to look at a mass of tweets and pull out two main data points for each one: the general topic area (e.g., politics, education, business, technology, etc.) and a sentiment score associated with the tweet (positive or negative),” it further says.

Narrative Science created a public application called QuillConnect, for people to use as the stories Quill generated were “interesting and actionable.” The stories generated by Quill Connect focus on how you and your followers are similar, how you are different and how to better engage with your followers.

Narrative Science believes it to be milestone; firstly because Quill can generate narratives using social media data and secondly, the ease of accessibility of personalized Twitter story that provides useful information about their influence and their followers.

Try QuillConnect for yourself here.

Follow @DataconomyMedia

(Image credit: Luis Vidal)

Narrative Science, an automated narrative generation innovator, has struck up a software licensing agreement with United Services Automobile Association. Their subsidiary also headed a fresh funding round along with existing investors like Sapphire Ventures (formerly SAP Ventures), Jump Capital and Battery Ventures, totally $10 million.

“USAA is a leader when it comes to providing its members with financial services and is always looking for ways to increase the effectiveness of the advice it provides,” explained Stuart Frankel, CEO at Narrative Science. “Our relationship with USAA will allow both companies to deliver highly-scalable solutions that will turn mountains of financial data into information that can be easily understood and acted on by millions of people.”

Using Artificial Intelligence technology, the Narrative Science Quill platform carries out data assimilation from disparate sources and creates written narratives, based on what might be significant to the end user. These narratives convey meaning from the data for any intended consumer or business audience, at unlimited scale.

Early on, it used its proprietary tech on ‘newspaper articles based solely on box scores.’ However, it has started mining enterprise software, finding usage in IT architectures within financial services and government agencies, Frankel told VentureBeat.

Narrative Science has raised $32 million in funding so far. Founded in 2010 in Chicago with 60 employees, its customers include organizations such as American Century Investments, MasterCard Advisors, the U.S. intelligence community and National Health Services (NHS) of England. USAA, provider of insurance, banking, investments, and retirement products to the U.S. Military and their families, utilizes the Quill platform to enhance the financial advice it provides to its members.

Read more here.

Follow @DataconomyMedia

(Image credit: Narrative Science)