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Forward Thinking Industry Leaders

Speaker 5

Tom Mitchell

Professor of AI and Learning
Carnegie Mellon University

Speaker 1

Ramanathan V. Guha


Speaker 4

Chris Welty

Research Scientist
IBM T.J. Watson Research Center

Speaker 1

Subutai Ahmad

VP Research
Numenta, Inc.

Speaker 1

Steve Ardire

‘Merchant of Light’ and Advisor
Software Startups

Speaker 1

Lora Aroyo

Associate Professor
VU University of Amsterdam

Speaker 2

Joshua Bloom

CTO, Chariman & Founder

Speaker 2

Adrian Bowles

STORM Insights, inc.

Speaker 2

Ted Dunning

Chief Application Architect

Speaker 2

Benjamin Grosof

CTO, CEO, and Co-Founder
Coherent Knowledge Systems

Speaker 4

Roger Grosse

Postdoctoral Fellow
University of Toronto

Speaker 5

Kristian Hammond

Chief Scientist
Narrative Science

Speaker 3

Robin Hanson

Chief Scientist
Consensus Point

Speaker 4

Paul Hofmann

Chief Technology Officer
Saffron Technology

Speaker 4

Pentti Kanerva

Project Scientist
UC Berkeley's Redwood Center

Speaker 4

James Kobielus

Big Data Evangelist

Speaker 4

Patrick Lilley

Emerald Logic

Speaker 6

Vivienne Ming

Chief Scientist

Speaker 6

Nick Pendar

NLP Data Scientist

Speaker 6

Raul Valdes-Perez

CEO, Co-founder

Speaker 4

Mark Sagar

Lab. for Animate Technologies

Speaker 4

Matt Sanchez

CTO, Founder
Cognitive Scale

Speaker 4

Nilo Sarraf

Neuro Information Science

Speaker 4

Tony Sarris

Founder and Principal

Speaker 6

Karl Schroeder

Science Fiction

Speaker 4

Dave Sullivan

Primary Architect
Ersatz Labs

  • Cognitive Computing and the Future

    Chris Welty
    Research Scientist, IBM

    At WWW 2011, soon after the notable performance of Watson on Jeopardy, I laid out the skeleton of a new computing paradigm, which IBM has since dubbed "Cognitive Computing". Over the three years since then, cognitive computing is indeed proving to be a radical shift in software and information technology, that disrupts previously understood terms like "performance", "feature", "debugging", and even "truth". In this talk I will give a personal perspective on how cognitive computing has progressed, and is re-shaping the software solution business and ecosystem, and our very expectations of what computers are capable of.

  • Practicable Machine Intelligence

    Joshua Bloom

    Putting machine intelligence into practice is emerging as an imperative across science and industry. I will show how our work in creating autonomous thinking telescopes and real-time machine-learning discovery engines led to seminal advances in astrophysics. Creating robust, fault-tolerant frameworks capable of surfacing actionable insight in real-time will become the point of departure for a discussion about putting into practice machine intelligence within enterprise. Apart from accuracy needs, interpretability and implementability are crucial requirements for the broad adoption—if not the democratization—of machine learning and other predictive tools. I will touch on the efforts of our company,, to bring machine-learning driven applications to enliven customer experience and improve customer lifetime value.

  • Break

  • Deep Neural Networks in Practice

    Dave Sullivan
    Primary Architect, Ersatz Labs

    Deep learning has recently achieved impressive results in several areas of machine learning. However, practical advice on how to use neural networks continues to be difficult to find. This talk is geared towards practitioners that would like to avoid the theory of how deep neural networks work and focus more on how and why they might use them in practice.

  • Cognitive Computing Is High Dimensional and Holographic

    Pentti Kanerva
    Project Scientist, UC Berkeley's Redwood Center of Theoretical Neuroscience

    The brain's circuits suggest that the basic unit to compute with be a large pattern or a multicomponent vector (e.g., a 10,000-bit word) rather than a number, and that information encoded into a vector be distributed over all its components. Neural nets and deep learning are a step in that direction but need to be complemented with operations on multicomponent vectors that make fully general computing possible.

  • Lightning Talks: Ideas at the Speed of Thought

    Matt Sanchez
    The Power of Cognitive Clouds
    Raul Valdes-Perez
    A Sentence is Worth 1,000 Data

    Nilo Sarraf
    Smart Affective (Neuro) Search: Brain Waves to Help Improve Search Results

    Benjamin Grosof
    Bringing Coherence to Cognition: Flexible Semantics, Deep Reasoning, and Explanations

    Nick Pendar
    Machine Learning Techniques for Analyzing Unstructured Business Data

    The field of Cognitive Computing is rich with ideas and innovation, so in this session we’re inviting both established ventures and startups to share their ideas and tell us about their approaches in a series of 5-minute lightning talks.

    Contact Tony Shaw at if you’re interested in applying for a presentation slot.

  • Lunch

    Lunch will be provided for registrants.
  • The Future of Communication: Human Insight at Machine Scale

    Kristian Hammond
    Chief Scientist, Narrative Science

    We have entered a new era with regard to our relationship with the machine. Computers have become integrated with our everyday lives, especially with the advent of mobile and multi-device technology, and they finally know more than we do about many of the things we care about. Unfortunately, this now leaves us with massive amounts of data that is exponentially growing as companies and organizations meter and monitor everything – every transaction and every interaction. Surprisingly, however, the machine’s ability to communicate the insights contained in that data has been strikingly limited. Data in its raw form is overwhelming. Tables and spreadsheets require calculation and correlation. Charts and graphs require interpretation. What people really need is the story. In this talk, we will look at Narrative Science’s Quill™, an Artificial Intelligence engine that understands data and uses it to craft narratives that explain what that data mean in clear, concise English language. Based on a foundation of Narrative Analytics, the technology can analyze any data set, identify important insights and then automatically generate a communication for a variety of audiences, skill levels and delivery formats. Put simply, Quill provides human understanding and insight at machine scale.

  • Cognitive Computing with Associative Memories: Reasoning by Similarity

    Paul Hofmann
    CTO, Saffron Technology

    We combine two very powerful ideas, Associative Memories and Kolmogorov Complexity for Cognitive Computing in order to make meaning from huge data sets in real time. Associative Memories mimic how humans learn and think but much faster and more powerfully. The Associative Memory functions as a universal NoSQL graph representation of structured and unstructured data. The universal cognitive distance based on Kolmogorov Complexity is used for reasoning by similarity on top of the NoSQL store. We’ll show use cases from health care @Mt Sinai Hospital in NY - automatic diagnosis of echocardiograms in real time, from global risk @The Bill and Melinda Gates Foundation - real time threat scoring reading incoming emails, and from maintenance and repair @Boeing - predicting before a part breaks. Additional Info: This presentation shows real world applications (Boeing, The Gates Foundation and Mtn Sinai) for finding pattern in large data sets combining a NoSQL graph representation (Associative Memories) with state of the art machine learning.

  • Break

  • Understanding Cortical Principles and Building Intelligent Machines

    Subutai Ahmad
    VP Research , Numenta, Inc.

    At Numenta we aim to understand the computational principles underlying the neocortex, and build intelligent machines based on those principles. At its most basic level the cortex takes in a stream of sensory data, builds a sensorimotor model of the world and outputs a stream of motor actions. In this talk I will describe the cortical principles behind these functions, and how we can translate them into a working system. The core ideas have been validated in commercial streaming analytics applications. An optimized implementation is available in the open source project NuPIC. Although we still have much work to do, this work forms a solid foundation for building biologically inspired intelligent machines.

  • Expressive Machines

    Mark Sagar
    Director, Laboratory for Animate Technologies at the Auckland Bioengineering Institute

    Can we make machines express themselves? Can we give computers a face? Enhancing the ability for computers to responsively communicate and learn naturally opens many new possibilities for richer human-computer interaction. The Laboratory for Animate Technologies research is pioneering biologically based methods to give computers the power of expression and naturally intelligent interaction. Our research combines face simulation with computational neuroscience models to create interactive self-animated expressive Avatars which learn through interaction. An example of the approach we are taking is embodied in BabyX, an autonomous experimental computer generated psychobiological simulation of an infant combining models of the facial motor system and theoretical computational models of basic neural systems involved in interactive behaviour and learning. Exploring the fundamental mechanisms of early communication will help lay the groundwork for human computer interfaces of the future.

  • Augmented Attention: First Step to an Artificial Unconscious?

    Karl Schroeder
    Science Fiction Author

    Science fiction narratives privilege consciousness in computer interactions. Characters “jack in” to “higher states of consciousness” in blissful union with the machine. But what if it were better for us to go the other way? Technological culture is enabling us to drive many previously manual, conscious activities into a kind of technologically-mediated unconscious. A banal but important example is cell-phone roaming, where actions of connection and disconnection that used to be deliberate and consciously undertaken are now automatic, invisible, and in large part unchosen. At the same time we can for the first time choose to make ourselves aware of previously unconscious or inaccessible interactions. Eagle Cams and facial recognition systems for animals promise to socialize previously opaque relationships with other entities in our natural environment (you can potentially know your neighborhood racoons as individuals). What is emerging is a broadening spectrum of preferences, or presets, that allow us to customize our personal and collective umwelt to a degree inconceivable to previous generations. We may be approaching a decision point where we will need to declare what about ourselves and our world we will be aware of, and what we will deliberately consign to a new, technologically mediated version of the Unconscious.

  • Search, Structure and Knowledge on the Web

    RV Guha
    Fellow, Google

    A significant fraction of the pages on the web are generated from structured databases. A longstanding goal of the semantic web initiative is to get webmasters to make this structured data directly available on the web. The path towards this objective has been rocky at best. While there have been some notable wins (such as RSS and FOAF), many of the other initiatives have seen little industry adoption. Learning from these earlier attempts has guided the development of, which appears to have altered the trajectory. Three years after its launch over 5 million Internet domains are using markup. Google has leveraged structured data in delivering its Knowledge Graph, and users are able to start asking more complex questions and find more relevant information more quickly than ever before. In this talk, we recount the history behind the early efforts and try to understand why some of them succeeded while others failed. We will also discuss some of the interesting research problems being addressed in the context of current efforts.

  • Never-Ending Language Learning

    Tom Mitchell
    Professor, Carnegie Mellon University

    We will never really understand learning by machines or by people until we can build machines that learn many different things, over years, and become better learners over time. We describe our research to build a Never-Ending Language Learner (NELL) that runs 24 hours per day, forever, learning to read the web. Each day NELL extracts (reads) more facts from the web, into its growing knowledge base of beliefs. Each day NELL also learns to read better than the day before. NELL has been running 24 hours/day for over four years now. The result so far is a collection of 70 million interconnected beliefs (e.g., servedWtih(coffee, applePie)), NELL is considering at different levels of confidence, along with millions of learned phrasings, morphological features, and web page structures that NELL uses to extract beliefs from the web. NELL is also learning to reason over its extracted knowledge, and to automatically extend its ontology. Track NELL's progress at, or follow it on Twitter at @CMUNELL.

  • Compositional Model Selection

    Roger Grosse
    Fellow, University of Toronto

    In Bayesian machine learning, models are often built by composing simpler motifs, such as clustering, factor analysis, and binary attributes. This compositional structure has allowed probabilistic models to be tailored to domains as diverse as vision, language, and medicine. Unfortunately, it also presents a challenge: identifying the right model and developing effective inference algorithms both require considerable human time and expertise. I’ll present a grammar of matrix decomposition models whose production rules correspond to simple probabilistic modeling motifs. The compositional structure of the grammar enables generic algorithms for posterior inference and model scoring across thousands of model structures. A greedy search over the grammar automatically identifies sensible models for datasets as diverse as image patches, motion capture, 20 Questions, and U.S. Senate votes, all using exactly the same code.

  • Break

  • The Promise of Prediction Markets

    Robin Hanson
    Chief Scientist, Consensus Point

    The world is full of organizations that make bad decisions, because the people with relevant info don’t have incentive to admit or reveal that info. Prediction markets have shown a consistent ability to introduce better incentives, allowing more accurate estimates, for better decisions. We review the mechanisms that enable prediction markets, data on their use, and many practical problems with building and fielding them.

  • Predicting Models of Human Performance

    Vivienne Ming
    Chief Scientist, Gild

    The dark art of identifying talented professionals for recruitment and promotion has become an obsession in the talent wars of the tech industry. Respected companies such as Google have apply enormous resources to predicting the best developers and managers, and yet the also periodically acknowledge the short comings of their existing methodology (e.g., no more brainteasers). A growing number of companies have begun "testing" candidates by giving them short-term contracts and real problems to solve as a part of the existing team. Of course, very few working professionals can take days off from an existing job for such assessments. An alternative is to build predictive models based on the true subject of interest: the real work of a subject's actual career. For students this means using unobtrusive technology to turn their learning experiences into rich assessments, building cognitive models using unstructured data and ubiquitous sensors. For professionals it means consuming the data of an entire career to make recommendations for hiring, promotion, and team building. We will discuss the concept of continuous passive formative assessment applied both learners and professionals, from kindergärtners to (future) CEOs.

  • Cognitive Computing on Hadoop, Low Tech and High Tech Approaches

    Ted Dunning
    Chief Application Architect, MapR

    With the advent of cost-effective big data technologies, it has become practical for businesses and research group of all sizes to have access to enormous volumes of data as well as to the computational resources required to process them. This has changed the way that computers interact with humans, particularly in the way that computers now can emulate human performance and competence in a number of tasks in ways that were only previously possible in science fiction stories. What is not well known is that there is an broad collection of methods for building these systems which range from extremely simple to highly sophisticated. I will describe several of these approaches to cognitive computing that exhibit different trade-offs in terms of implementation complexity and advanced capabilities and will include results and demonstrations of these techniques. The big news in this presentation is that many of these techniques are now practical even if you don't have a sophisticated data science team packed with PhD's. If you understand your data, you can start building some of these systems right away.

  • Lunch

    Lunch will be provided for registrants.
  • Seven Myths about Human Annotation

    Lora Aroyo
    Associative Professor, VU University Amsterdam

    Human annotation is a critical part of big data semantics, but it is based on an antiquated ideal of a single correct truth. This presentation exposes seven myths about human annotation and dispels the myth of a single truth with examples from research. A new theory of truth, Crowd Truth, is based on subjective human interpretation on the same objects (in our examples, sentences) across a crowd, providing a representation of subjectivity and a range of reasonable interpretations. Crowd Truth has allowed us to identify the myths of human annotation and paint a more accurate picture of human performance of semantic interpretation for machines to attain.

  • Using Artificial Imagination to get Big Answers

    Patrick Lilley
    CEO, Emerald Logic

    Brilliant experts are working on problems that matter: curing diseases, predicting natural disasters, discovering laws of physics. But expertise is bias – the more you know, the less you look around. Further, the world is not made of straight lines. Biology, human behavior, and financial markets are driven by indirect, nonlinear relationships that cannot be discovered by traditional means. It’s imperative that we move beyond statistics and pretty visualizations, to assist experts with machines that can reverse engineer real-world systems on their own, with no preconceptions or limiting assumptions. We’ll show how this is possible, with real-world cases from a variety of domains.

  • Break

  • Panel: Understanding the New World of Cognitive Computing

    Steve Ardire, Adrian Bowles, James Kobielus, Matt Sanchez and Tony Sarris

    Following two days of presentations and networking, our panel of experts in Cognitive Computing will discuss the key outcomes and conclusions, and offer their predictions on where this technology is going.

  • Close


In recent years, numerous technological advancements have combined to give machines a greater ability to understand information, and to learn, to reason, and act upon it. These advancements have reached such sophistication that in some cases machines may even appear to think. As a result, the broad term used to describe this emerging capability is Cognitive Computing.

Cognitive Computing systems may include the following ostensible characteristics:

    • Natural Language Processing
    • Machine Learning
    • Algorithms that learn and adapt
    • Vision-based sensing and image recognition
    • Spatial and contextual awareness
    • Reasoning and decision automation
    • Sophisticated pattern recognition
    • Neural Networks
    • Semantic Understanding
    • Noise Filtering
    • Common Sense
    • Robotic Control
    • Emotional Intelligence

Who is Attending

Join some of your colleagues who have already registered:

  • Managing Director, Technology R&D, Accenture
  • Principal Scientist, Adobe Research
  • Head of Tech. Strategy, American Institute of Physics Publishing
  • Senior Scientist, Boeing
  • Research Scientist, Bosch Research
  • CEO,
  • CTO, Catalina Marketing
  • VP Cloud and Big Data, DOCOMO Innovations, Inc.
  • Director, Statistical Consulting, Dun & Bradstreet
  • Advisory Consultant, EMC
  • Principal Analyst, Forrester Research
  • Senior Risk Analyst, HSB
  • Big Data Evangelist, IBM
  • Distinguished Engineer, IBM Watson Group
  • Editor, Institute for Global Futures
  • Software Engineering Manager, Intel
  • Chief Experience Officer, Microsoft
  • Chief Scientist, Energy Analytics, National Renewable Energy Lab
  • CTO, OneShield Inc.
  • Research Manager, PARC
  • Director, Presidio Ventures
  • Research Senior Director, Samsung Research
  • Director, SAP
  • Professor of Economics and Statistics, Seattle University
  • Founder, Tagasauris
  • A.I. Engineering Lead, The MITRE Corporation
  • Senior Software Engineer, YP


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Numenta, Inc., was founded in 2005 to be a catalyst in the emerging field of machine intelligence. Its biologically inspired machine learning technology is based on a theory of the neocortex first described in co-founder Jeff Hawkins’ book, On Intelligence. The technology can be applied to anomaly detection in servers and applications, human behavior, and geo-spatial tracking data, and to the predication and classification of natural language. In addition, Numenta has created NuPIC (Numenta Platform for Intelligent Computing) as an open source project.

Cognitive Scale powers massively scalable Cognitive Clouds that weave insights and advice into the fabric of your business. Cognitive clouds understand natural language and securely source and fuse multi-structured data to automatically deliver contextualized insights across any channel.

Cognitive clouds are a new class of data interpretation and learning systems that accelerate value from Big Data by combining it with previously inaccessible Dark Data i.e. data that is not collected, neglected, or underutilized. Examples include consumers looking for inspired travel or shopping advice or healthcare providers looking to manage high risk populations by combining clinical, social and lifecycle data.

Cognitive Scale is headquartered in Austin, Texas and has strategic partnerships with IBM Watson, Deloitte and Avention.

If you would like information about sponsorship, or opportunities to promote your products and services, please contact Tony Shaw at


  • Who attends?

    The first Cognitive Computing Forum (C2F) will be attended by approximately 150-senior development, engineering, technical, scientific and product design executives from Silicon Valley and across North America.
  • What airport do I fly in to?

    C2F is being held at the Sainte Claire Hotel in San Jose, California. The nearest airport is Norman Y. Mineta San Jose International Airport but you can also fly into either San Francisco International or Oakland International. Both are about 60 mins away from San Jose and offer various modes of transportation.
  • What if I need to cancel my registration?

    All registration is Risk Free before July 18th. This means that should you have to cancel anytime before the deadline you will receive a full refund on your registration costs.
  • Is there parking at the hotel?

    There is parking at the hotel and overnight parking across the street at the Marriot and San Jose Convention Center
  • What else is there to do in San Jose?

    Close to the venue you will find a number of the Bay Area's best restaurants and shops. The Sainte Claire is also centrally located so that if you want to take the train to San Francisco for the day or night the train station is right there.