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Future of AI

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Future of AI

  1. 1. Cognitive Computing and The Future of AI Dr. Michael Karasick VP, Cognitive Computing IBM Research October 2016 © 2016 International Business Machines Corporation
  2. 2. “ a” By 2018 half of all consumers will regularly interact with services based on cognitive - IDC FutureScape 2© 2016 International Business Machines Corporation
  3. 3. I am hiking in Ushuaia next April. Get me a screwdriver. How do ManufacturerCo’s products overlap with ours? Which regulations apply? Are you looking for the elevator? Your medication is on the coffee table. 3© 2016 International Business Machines Corporation
  4. 4. 4
  5. 5. Early AI Systems Reason Create Teach 5© 2016 International Business Machines Corporation
  6. 6. Games Provide a Laboratory for Reasoning © 2016 International Business Machines Corporation 6
  7. 7. Winning A Game Based on Natural Language 7© 2016 International Business Machines Corporation
  8. 8. Watson Developer Cloud Services 8 http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/© 2016 International Business Machines Corporation Watson Developer Cloud Services
  9. 9. 9 Sensors & Devices VoIP Enterprise Data Social Media © 2016 International Business Machines Corporation
  10. 10. An AI Renaissance Cloud Deep Learning Probabilistic Reasoning Logic Probability Learning © 2016 International Business Machines Corporation 10
  11. 11. 11
  12. 12. Interpreting Medical Imagery 0 5 10 15 20 25 30 2010 2011 2012 2013 2014 ErrorRate(%) Human Error © 2016 International Business Machines Corporation 12
  13. 13. Recognizing Speech 0 5 10 15 20 25 2000 2002 2004 2006 2008 2010 2012 2014 2016 ErrorRate(%) Human Error © 2016 International Business Machines Corporation 13
  14. 14. Cognitive Workloads Put New Demands on Computing COMPUTATIONCost Graph Analytics Clustering Dimensionality ReductionSimple DB queries Information Retrieval Uncertainty Quantification DATAVolume DNN Training Complexity of Task © 2016 International Business Machines Corporation 14
  15. 15. Exponential Growth in Linked Open Data 2009 ~6 Billion Triples 2015 667 Billion Triples clouhttp://lod-d.net/ http://stats.lod2.eu/ 2014 ~64 Billion Triples © 2016 International Business Machines Corporation 15
  16. 16. Energy Efficient Architectures Critical For Scale 1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01 CPU CPU+GPU CPU+FPGA IBM SyNAPSE BitsRecognized/nanoJoule 100,000 X More efficient © 2016 International Business Machines Corporation 16
  17. 17. Cognitive Computing Research at IBM INFRASTRUCTURE COGNITIVE SERVICES APPLICATIONS FRAMEWORKS © 2016 International Business Machines Corporation 17
  18. 18. Signal Comprehension: Speech, Image, Video, Text Process & Understand Content Create Fast Accurate Dynamic (Un)Supervised Train © 2016 International Business Machines Corporation 18
  19. 19. 20102009 2011 2012 2013 2014 2015 50 0 25 75 100 125 150 Financial Documents Ingest “Show me revenues for Citibank between 2009 and 2015” © 2016 International Business Machines Corporation 19
  20. 20. Cognitive Computing (AI) Technologies Decision Support People Insights Cognitive Software and Data Life Cycle Reasoning and Planning Human Computer Interaction Conversation Query and Retrieval Knowledge Extraction and Representation Learning Natural Language & Text Understanding Visual Comprehension Speech and Audio Embodied Cognition Cognitive Computing Platform Infrastructure Signal Comprehension Reasoning About Domains Interaction Systems Trust and Security © 2016 International Business Machines Corporation 20
  21. 21. Learning Domains and Reasoning Learn Extract Knowledge Decide Query & Retrieve Reason © 2016 International Business Machines Corporation 21
  22. 22. Extract Knowledge Decide Query & Retrieve ReasonLearn • Scale: Models, Training Data • Less Data • Hybrid Deep Learning • Causality © 2016 International Business Machines Corporation 22
  23. 23. Learn Decide Query & Retrieve Reason Extract Knowledge • Integrate: • Symbolic inference • Approximate/probabilistic reasoning • Learned Knowledge Modeling © 2016 International Business Machines Corporation 23
  24. 24. Learned Semantic Document Representation © 2016 International Business Machines Corporation 24
  25. 25. Learn Extract Knowledge DecideReason Query & Retrieve • Fusion • Learning on the job © 2016 International Business Machines Corporation 25
  26. 26. Learn Extract Knowledge Decide Query & Retrieve Reason & Plan • Symbolic Reasoning • Textual Reasoning • Integrated Reasoning • Hypothesis Planning © 2016 International Business Machines Corporation 26
  27. 27. With a paymentDuration of loadDuration and a $$$ down payment, how much is the periodicPayment payment? Policy & Product documentsCLIENT RECORD © 2016 International Business Machines Corporation 27
  28. 28. Learn Extract Knowledge Query & Retrieve Reason Decide • Recommendation • Collaboration • Industry Use Cases © 2016 International Business Machines Corporation 28
  29. 29. Computational Argumentation © 2016 International Business Machines Corporation 29
  30. 30. © 2016 International Business Machines Corporation Cognitive Computing (AI) Technologies Decision Support People Insights Cognitive Software and Data Life Cycle Reasoning and Planning Human Computer Interaction Conversation Query and Retrieval Knowledge Extraction and Representation Learning Natural Language & Text Understanding Visual Comprehension Speech and Audio Embodied Cognition Cognitive Computing Platform Infrastructure Signal Comprehension Reasoning About Domains Interaction Systems Trust and Security 30
  31. 31. People Insights Personality Interests Cultural Background Interests Mental/Physical State © 2016 International Business Machines Corporation 31
  32. 32. Interaction Control Machine assists humansHuman controls machines Sense Advise Converse Request © 2016 International Business Machines Corporation 32
  33. 33. Embodied Cognition Avatars Objects (e.g. IoT devices) Robots Spaces (e.g. rooms) © 2016 International Business Machines Corporation 33
  34. 34. Rules + Task Learning + Context Contextual Understanding Action Planning Learning Sequences Words{ Conversation © 2016 International Business Machines Corporation 34
  35. 35. © 2016 International Business Machines Corporation Cognitive Computing (AI) Technologies Decision Support People Insights Cognitive Software and Data Life Cycle Reasoning and Planning Human Computer Interaction Conversation Query and Retrieval Knowledge Extraction and Representation Learning Natural Language & Text Understanding Visual Comprehension Speech and Audio Embodied Cognition Cognitive Computing Platform Infrastructure Signal Comprehension Reasoning About Domains Interaction Systems Trust and Security 35
  36. 36. Building Computing Systems 36 1900+ 1950+ 2005+ © 2016 International Business Machines Corporation
  37. 37. Cognitive Systems Lifecycle MACHINE MODEL LIFECYCE SOFTWARE LIFECYCLE Operations ACQUIRE DATA CLEANSE DATA TRAIN MODEL DEBUG MODEL IMPLEMENTATION DESIGN DEBUG APPLICATION REQUIREMENTS © 2016 International Business Machines Corporation 37
  38. 38. Cognitive Systems Infrastructure Deep Learning Computing Platform: Big data and the explosion in compute needs of machine/deep learning has made training and inference expensive, time- consuming, and fraught with complexities. + Deep Learning as a Service Accelerators Securing Models and Data © 2016 International Business Machines Corporation 38
  39. 39. Brain-Inspired Systems - SyNAPSE © 2016 International Business Machines Corporation 39
  40. 40. © 2016 International Business Machines Corporation Cognitive Computing (AI) Technologies Decision Support People Insights Cognitive Software and Data Life Cycle Reasoning and Planning Human Computer Interaction Conversation Query and Retrieval Knowledge Extraction and Representation Learning Natural Language & Text Understanding Visual Comprehension Speech and Audio Embodied Cognition Cognitive Computing Platform Infrastructure Signal Comprehension Reasoning About Domains Interaction Systems Trust and Security 40
  41. 41. “Help me replace the broken component” Putting it all together © 2016 International Business Machines Corporation 41
  42. 42. Thank You © 2016 International Business Machines Corporation 42

Notes de l'éditeur

  • Cognitive Systems learn, reason understand, and interat with people – they use AI technologues

    Cognitive transforming every industry where there is a lot of data, and horizontal applications
  • <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
  • SHRDLU: A program for understanding natural language, (Terry Winograd, MIT) in 1968-70 that carried on a simple dialog with a user, about a small world of objects on a display screen. http://hci.stanford.edu/~winograd/shrdlu/


    AARON - The First Artificial Intelligence Creative Artist (Harold Cohen, UCSD) 1973–present) The Aaron system composes and physically paints novel art work. It is a rule-based expert system using a declarative language. http://www.viewingspace.com/genetics_culture/pages_genetics_culture/gc_w05/cohen_h.htm


    Carnegie Learning’s Algebra Tutor (1999–present): This tutor encodes knowledge about algebra as production rules, infers models of students’ knowledge, and provides them with personalized instruction. http://www.carnegielearning.com


  • Arthur Samuel demonstrated (1956) playing Checkers with the IBM 701 on Television. Major publicly visible milestone for Artificial Intelligence – tree searching, learning by playing itself

    Gerald Tesauro (1994) developed a self-teaching backgammon program called TD-Gammon. Learning its strategy almost entirely from self-play, TD-Gammon achieved a human world-champion level of performance.

    On May 11, 1997, IBM’s Deep Blue beat the world chess champion Garry Kasparov in a six-game match: Two wins for Deep Blue, One for Kasparov and Three draws.

    AlphaGo is a computer program developed by Google DeepMind in London to play the board game Go.[1] In October 2015, it became the first Computer Go program to beat a professional human Go player without handicaps on a full-sized 19×19 board.[2][3] In March 2016, it beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicaps.[4] Although it lost to Lee Sedol in the fourth game, Lee resigned the final game, giving a final score of 4 games to 1 in favour of AlphaGo. In recognition of beating Lee Sedol, AlphaGo was awarded an honorary 9-dan by the Korea Baduk Association.




  • D2_John_Kelly_ppt2003_FINAL
  • There is an enormous amount of data in the planet. According to

    44,000,000,000,000,000,000,000 bytes 44 ztabytes by 2020 (by IDC / EMC)
  • Earlier AI Systems Stalled due to
    Reliance on a large number of manually designed rules for specific purposes
    Lack of sufficient computational power
    Trouble scaling to complexities of real applications


    Recent Trends are Driving Change
    Probability and statistics provide a fundamental formalism for AI – probabilistic reasoning, graphical models, and Hidden Markov Models
    More powerful and sophisticated machine learning algorithms
    The availability of huge computing power and vast amounts of data
    Individuals overwhelmed by information overload in private and professional lives

    <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>






  • <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
  • <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
  • <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
  • Talk about today – feature extracting and brittle code

    ML: Speed, Scale, New Models
    Learned Representations and Reasoning – mixing inference and statistics and probability
    New Kinds of Queries
    Reasoning – Mixing
  • ML at Scale (e.g. Comp-Stat Learning and Optimization)
    Non-standard paradigms (e.g. Learning from much less data)
    Deep Learning++ (e.g. hybrid architectures)
    Actionable and interpretable learning (e.g. Learning causal, structural and sparse models)
    ML for Knowledge Extraction, Representation, and Reasoning (e.g. Automated Knowledge Base Construction)
  • Semantic document representation
    Rapid creation of new knowledge bases
    ”Automated” knowledge modeling by domain experts
    Integrated symbolic and learned approximate/probabilistic reasoning
    Learning on the job
  • Enhance Watson R&R with state-of-the-art capabilities for querying and question answering, such as improved ranking, passage retrieval, answer selection/generation, similarity search and more.
    Dynamic query & retrieval models that adapt during the interaction with the user (e.g. search session or dialog)
    Ontology-driven querying of annotated documents and extracted entities.
    Supporting natural language query interfaces as well as programmable (domain-specific) APIS 
    Long Term Goal (< 3 yrs)
    Support for multiple retrieval pipelines
    Answer Generation (NLG)
    Leveraging usage data - Interactive Retrieval, Usage data analysis
    Ontology driven querying
    Personalized Retrieval – personalize according to user profile, intent/task and context
  • Talk about today – feature extraction and brittle code

    ML: Speed, Scale, New Models
    Learned Representations and Reasoning – mixing inference and statistics and probability
    New Kinds of Queries
    Reasoning – Mixing
  • No support for user-specific answers to be synthesized
    No support for extracting quantities, semantic mapping, nor any math
    Requires precise and complete answers with high confidence
    Requires identifying appropriate formula, and semantic mapping of values to variables
    Questions are often ill-posed
    Units and types may be unspecified
    Context and formula inputs required from a variety of sources
    Dialog and explanation expectations
  • Short Term Goal (< 1 yr)
    Services : Recommender Service piloted in WCA / Retail V.A. that is based on Decision Dialog and Voyager
    Solutions: IBM Cognitive Recommender Engine (CoRE) for CAO, M&A, [Boson] Assisting flight crews with diversion scenarios – validated & delivered to client, Decision Agent for Disease Grading and Patient Triaging - validated & delivered to client
    Long Term Goal (< 3 yrs)
    Services: group decision making, decision gisting
    Solutions: Watson Care Manager recommender system for care planning – transferred to Watson Health, Decision Agent for Disease Grading and Patient Triaging – Transferred to Watson Health

  • Goes beyond factual question answering

    Helps humans make decisions and persuade others by automatically constructing pro and con arguments
    Mines huge corpora of textual data. The claims are backed up with relevant evidence

    The distinctive debating technologies developed in this project can have great practical use in industries such as government, legal, finance, healthcare, and sales, to name just a few. For example, automatic argument construction could serve to dramatically enhance business processes and decision making – whether by providing assisted reasoning for which treatment will work best on a patient, or by helping salespeople develop persuasive arguments when working with clients in deal negotiations, or by presenting pro and con arguments in support of or against government policies.

  • Old way:
    User acceptance determined by usability and desirability


    New way
    User acceptance determined by engagement, effective communication and ease of participation
  • Objects aware of those interacting with them: physical and virtual embodiments:

    Model, plan, represent, sense, respond
  • Dialog is between a person and a cognitive system and can be via different interaction modes (e.g. speech, text, gestures, etc.).
    Create an architecture for integrating contextual understanding, various inference engines, language generation, and user modeling such as emotions, personalities, and other important contextual information
  • 1900: TABULATION
    Punched card tabulation
    Scale, automation
    Seeds of future innovation

    1950: PROGRAMMING
    Stored data, instructions
    Languages for computing
    Metrics for computation

    2011: COGNITION
    Massive data scale
    Data for training
    Real-world modalities

    Cognitive Systems learn and interact naturally with people to amplify what either humans or machines could do on their own. They help us solve problems by penetrating the complexity of Big Data.

    <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>

  • Cognitive systems are more challenging to develop, deploy, and manage because a critical component (model) is created from data and requires domain expertise.

    Cognitive systems are more challenging to develop, deploy, and manage because a critical component (model) is created from data and requires domain expertise.

    Models are new kinds of artifacts, then need to be secured, composed, trained in a context – they life in a hostile environment

    Models have a lifeycle

  • Deep Learning Computing Platform: Big data and the explosion in compute needs of machine/deep learning has made training and inference expensive, time-consuming, and fraught with complexities.


    Cloud-based training and inferencing services, with accelerators improve developer and scientific productivity.

    <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
  • Six years ago, IBM and our university partners embarked on a quest—to build a brain-inspired machine—that at the time appeared impossible. Today, in an article published in Science, we deliver on the DARPA SyNAPSE metric of a one million neuron brain-inspired processor. The chip consumes merely 70 milliwatts, and is capable of 46 billion synaptic operations per second, per watt–literally a synaptic supercomputer in your palm.

    Along the we have journeyed from neuroscience to supercomputing, to a new computer architecture, to a new programming language, to algorithms, applications, and now to a new chip—TrueNorth.

    Considering overall energy consumption underscores the divergence between the brain and today’s computers even more starkly. Note that a “human-scale” simulation with 100 trillion synapses (with relatively simple models of neurons and synapses) required 96 Blue Gene/Q racks of the Lawrence Livermore National Lab Sequoia supercomputer—and, yet, the simulation ran 1,500 times slower than real-time. A hypothetical computer to run this simulation in real-time would require 12GW, whereas the human brain consumes merely 20W.

    To support these algorithms at ever increasing scale, TrueNorth chips can be seamlessly tiled to create vast, scalable neuromorphic systems. In fact, we have already built systems with 16 million neurons and 4 billion synapses. Our sights are now set high on the ambitious goal of integrating 4,096 chips in a single rack with 4 billion neurons and 1 trillion synapses while consuming ~4kW of power.

  • Technology support is a labor-intensive business – both diagnosis and field repair.
    There is a large body of prior incident reports and service requests – similar symptoms might have different root causes – server down due to full file system or hardware error
    There are many resolution reports and success indicators
    Diagnosis often conducted iteratively in a dialog, pruning potential causes to the most likely ones

    Knowledge Extraction and Representation:
    Enhance Knowledge Base (with domain vocabulary, instances, constraints and rules) to help current way of working (for Explicit, e.g. dialogue and NLC).
    The input to KB should be from domain experts, input from humans and historical data

    Dialog:
    Implicit: Create an ontology/representation to create coarse representation of concepts in the space together with tasks
    Inferred: Need recorded dialogues and once we have that we can use learning techniques to estimate what happens next in the hardcoded dialogues/automations. This is used to build the ontology

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