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 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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With six top IT events under one roof, 300+ exhibitors and 300+ free seminar sessions, IP EXPO Europe is a must-attend IT event for CIOs, heads of IT, security specialists, heads of insight and tech experts. The event will take place on 7-8 October 2015, at ExCel, London.

The event showcases brand new exclusive content and senior level insights from across the industry, as well as unveiling the latest developments in IT. IP EXPO Europe now incorporates Cloud and Infrastructure Europe,Cyber Security Europe, Data Centre Europe, Data Analytics Europe, DevOps Europe and Unified Communications Europe.

As part of this outstanding event, Dataconomy will be hosting its fifth edition of Data Natives on the 7th of October, from 5PM until 8PM. It will be an evening of exciting talks from Data Science industry leaders, followed by enough time for beers, food and networking.

The first presenter is John Easton, IBM Distinguished Engineer, Lead Cloud Advisor for the United Kingdom, Ireland & Nordics, IBM Cloud Member and IBM Academy of Technology. His talk, ‘’Why Infrastructure matters for Big Data and Analytics’’ will look at how different analytical approaches can have very different platform requirements, showing attendees why their choice of infrastructure really matters for big data and analytics. By understanding that different analytical problems have very different infrastructure needs to deliver real business value, organisations can set themselves up better by building a solid analytical infrastructure that is fit to meet any challenge that this rapidly changing space can throw at them.

John is internationally known for his work helping commercial clients exploit large scale distributed computing infrastructures, particularly those utilising new and emerging technologies. He has worked with clients in a wide range of industries with a particular focus on banks and financial markets firms. He also has significant experience in the telecommunications sector. Previous to his current role, John led a pan-European team building infrastructures to support big data and advanced analytical workloads. During his time at IBM, John has led initiatives around hybrid systems, computational acceleration, grid computing, energy efficiency and mission-critical systems.

The second presenter is Rick Farnell, SVP and Co-Founder of Think Big, a Teradata Company. His talk, ‘’Big Data 2015: What we’ve learned in 5 years’’ will discuss how Think Big, a pioneer in big data services with Hadoop, is helping a global organization utilize multi-petabyte Hadoop clusters to drive tangible business results. Highlights in this presentation will include how this company is using best practices for data ingestion across multiple manufacturing facilities in Asia and the US, utilizing robust patterns for handling data quality, metadata management, pipelining, buffering, and security to establish Hadoop as an enterprise data lake. Working with more than 100 clients on Hadoop projects, Think Big, a Teradata Company, will describe other enterprise design patterns and successful organizational models used by their clients, and how to avoid the pitfalls associated with designing, managing, and scaling your enterprise data lake.

As Co-Founder and President of Think Big, Rick brings 20 years of management experience in scaling technology consulting organizations in North America, EMEA and APAC and is responsible for Think Big’s sales and services business. Previously, Rick directed a global division within Sun Microsystems and in 2009, he led Sun’s cloud open source software go to market strategy with Amazon Web Services.

The third presenter is Matthias Korn, Technical Consultant at datavirtuality. His talk, ‘’Beyond the Data Lake’’ will talk about the shift in digital era, which harnesses large amounts of data to make astute business decisions and improve operations, which is now an imperative. While our ability to generate data still far outstrips our ability to analyze it, we are making strides. Exciting new approaches are merging big data solutions with traditional enterprise data strategies. Logical data warehouses, in which there is no single data repository, hold enormous promise. By offering an ecosystem of multiple best-fit repositories, technologies, and tools, business can effectively analyze data for powerful insight.

datavirtuality enables companies to instantly connect internal and external data sources. The solution revolutionizes the technological concept of data virtualization and data integration and builds an enterprise wide database from relational and nonrelational data sources within a few hours. With 100+ connectors, the data can be processed immediately in analysis, planning and statistics solutions and also written back whenever needed in the source systems. The self-learning database automatically adapts to changes in the IT environment and user behavior. By using datavirtuality, companies achieve maximum flexibility and speed with data integration with a minimal administrative effort.

Finally, the fourth presenter is Francisco Webber, Inventor and Co-Founder of Cortical.io, whose talk is going to be about “Spark Implementation Facing Big Text Data”. Cortical.io’s approach is inspired by the latest findings on the way the human cortex works. Their technology, the Semantic Folding Engine, breaks with traditional methods based on pure word count statistics or linguistic rule engines. Its central component, the Cortical.io Retina, converts text data into a Semantic Fingerprint, that contains all associated contexts. The system “understands” the relatedness of two items by measuring the overlap of their fingerprints. Because of their small size, Cortial.io’s fingerprints require a fraction of the computing power normally required to perform complex NLP operations. As a result, the Cortical.io Retina is very fast, reliable and easy to implement – a breakthrough technology that leverages the intelligence of the brain to enable the Natural Language Processing of Big Text Data.

We are looking forward to seeing you at Data Natives, London, on the 7th of October, at 5PM! Don’t forget to register on the IP EXPO website (http://www.ipexpo.co.uk/) until the 6th of October, at 7PM, in order to have free access to our meetup!

More info About Data Natives, London here:

http://www.meetup.com/Data-Natives-London/events/225069543/

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Cortical.io, an Austrian startup whose tech mimics brain function to process language more accurately and natively, have just announced an impressive new funding round. Reventon (NL) is a venture capital firm responsible for this considerable boost to Cortical.io’s coffers. With the new funding, cortical.io already have grand plans on how to bring their game-changing technology to a wider audience.

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Cortical.io, an Austrian startup whose tech mimics brain function to process language more accurately and natively, have just announced an impressive new funding round. Reventon (NL) is a venture capital firm responsible for this considerable boost to Cortical.io’s coffers. With the new funding, cortical.io already have grand plans on how to bring their game-changing technology to a wider audience.

Discussing the business opportunities their signature technology unlocks for enterprises, Cortical.io’s Founder Francisco de Sousa Webber stated: “The beauty of Semantic Fingerprinting is that it is simple and intuitive, while working for any language. With cortical.io’s Retina, the language barriers disappear: anything that can be expressed with words like product descriptions, LinkedIn profiles, Twitter messages, news or business documents can be seamlessly compared, filtered, classified, based on its meaning. People can be matched with products and with other people, content can be augmented automatically with rich meta-information and the amount of double work in enterprises can be limited by spotting existing information resources. This opens a new era of truly intelligent applications in domains as different as Enterprise Search, Media Analytics, Business Intelligence, e-commerce, Social Networking or even Intelligence and Security. “

Their plans with the new growth capital are threefold. First, they are launching an offering on Amazon Marketplace within the next two weeks, allowing businesses to incorporate the technology into their cloud environments. They will also pilot subscription packages in their public cloud early next year.

Second, they are expanding their cross-language capabilities. Their technology can currently compare nine languages, including Spanish, Russian, Arabic and Mandarin, and they have an additional fourteen languages in the pipeline, with Dutch, Japanese and Czech coming next.

Third, they will be partnering with Numenta- whose work on sparse distributed representation sparked the genesis of cortical.io– to continue the development of groundbreaking NLP products, based on the research of the two companies. The fusion of the two technologies will enable applications for sentiment analysis, automatic abstracting and AI dialogue systems.

Cortical.io’s plans to bring their groundbreaking research to wider public attention are certainly ambitious; we’re excited to see what comes next for the young and bold venture.

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(Image credit: Cortical.io)

Since Microsoft began working with deep learning neural networks in 2009, we’ve seen huge improvements in the way algorithms can detect our language and dialogue. IBM have continued to pour money and resources into the development of Watson; Apple have moved the development of Siri in-house, to improve its NLP capabilities; we’ll soon see a version of Skype which can translate the spoken word on the fly.

But Francisco Webber, co-founder of cortical.io, noticed a grey area in the realm of natural language processing. Most of it is heavily based in statistical analysis. “The problem with statistics”, he says, “is that it’s always right in principle but it’s not right in the sense that you can’t use it to create an NLP performance that is even close to what a human’s doing.”

“Normally in science, you use statistics if you don’t understand or don’t know the actual function,” he continues. “Then you observe and create statistics and it lets you make good guesses.”

Webber saw similarities between the state of NLP today, and the history of quantum physics. “In the beginning, quantum physics was an extremely statistical science,” he explains. “But since they have found out quarks, and up-spin & down-spin particles, they have become pretty good in predicting how this whole model basically works. I think this is what we have been lacking in NLP, and I think for the main reason for this is because we did not come up with a proper representation of data that would allow us to do this.”

So, Webber embarked on an academic journey to find a proper way of representing and modelling language, one which would take him years. Ultimately, he was drawn to the work of Palm-Pilot-inventor-turned-neuroscientist Jeff Hawkins; it was this line of inquiry which would turn out to be his “Eureka” moment. Jeff Hawkins, with his work at Numenta, has been working on understanding brain function, and developing algorithms which mimic these processes- such as his work with hierarchical temporal memory, which we reported on back in May, as well as fixed-sparsity distributed representations.

Sparse distributed representation, as Webber explains, is “the language in which the brain encodes information if it wants to store it. This gave me the theoretical breakthrough in saying ‘Okay if all data that is processed in the brain has to be in the SDR format, what we need to do is convert language into this SDR format’. The fundamental property of SDRs is that they are large binary vectors which are only very sparsely filled and that the set bits, if you want, are distributed over the space that is represented by the vector.”

One of the key elements of SDRs is that if words have similar meanings, their SDRs are similar- meaning this model represents a way of mapping words which semantically resemble each other. Webber and his team set about using sparse distributed representation to create “semantic fingerprints”, two-dimensional vectors which represent 16,000 semantic features of words.

To build these semantic maps, Webber and co. put their algorithm to work unsupervised on Wikipedia. “It turned out,” Webber remarks, “that if you just convert words into these SDRs, there are plenty of things—plenty of problems I would even say—that we have faced typically in NLP that we can now solve more easily, even without using any neural network back end.”

One of the findings was that you can semantically fingerprint documents, as well as words. “By using the rule of union, we can create a semantic fingerprint of a document by adding up all the fingerprints of the constituent word fingerprints. What’s great is that the document fingerprint behaves in the same way as the word fingerprint. So you can also compare two documents on how similar they are semantically by comparing the two fingerprints and by calculating the overlap between the two.”

“And there have been even more things that we found out. You can disambiguate terms computationally instead of using a thesaurus or dictionary by simply analyzing the fingerprint and using the similarity function. Recursively you can find all the meanings that are captured within a word. Of course this is based on the training data but as in our case, we have used Wikipedia. We can claim that we have found the more general ambiguities that you can find with words.”

Several intriguing use cases have already arisen. A leading German-English teaching service is using it to tailor learning material for its students; if a student is interested in, say, motorsports, they’ll be supplied with educational texts about formula one. There’s also interest from companies in the domains of rank analysis and medical documentation analysis.

What’s next for cortical.io? Expanding into different languages. “The algorithm is supposed to work on any language, on any material you provide it as long as it’s sufficient material and as long as it’s evenly spread across the domains you want to cover,” Webber explains. “So we are about to prepare a Spanish, French, German, Dutch and several others if there are enough Wikipedia documents available.”

In the realm of NLP, the work of Hawkins, Webber and cortical.io could represent a dramatic shift away from using statistical analysis to detect patterns, towards fundamentally understanding how we can computationally model language.

This post has been sponsored by cortical.io.

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Eileen McNulty-Holmes – Editor

Eileen has five years’ experience in journalism and editing for a range of online publications. She has a degree in English Literature from the University of Exeter, and is particularly interested in big data’s application in humanities. She is a native of Shropshire, United Kingdom.

Email: eileen@dataconomy.ru

(Featured image credit: cortical.io)