Uploaded bySteven Miller
Future of AI
The document discusses cognitive computing and the future of artificial intelligence. It describes how by 2018, half of all consumers will regularly interact with services based on cognitive technologies. It then provides an overview of IBM's research into cognitive computing, including the development of early AI systems, deep learning, natural language processing, machine vision, and brain-inspired systems like SyNAPSE. The document outlines IBM's vision of a future where cognitive systems can assist humans by interpreting medical images, recognizing speech, providing recommendations, and engaging in conversational interactions.
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Future of AI
- 1. Cognitive Computing and The Futureof AI Dr. Michael Karasick VP, Cognitive Computing IBM Research October 2016 © 2016 International Business Machines Corporation
- 2. “ a” By 2018half of all consumers will regularly interact with services based on cognitive - IDC FutureScape 2© 2016 International Business Machines Corporation
- 3. I am hikingin 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.
- 5. Early AI Systems ReasonCreate Teach 5© 2016 International Business Machines Corporation
- 6. Games Provide aLaboratory for Reasoning © 2016 International Business Machines Corporation 6
- 7. Winning A GameBased on Natural Language 7© 2016 International Business Machines Corporation
- 8. Watson Developer CloudServices 8 http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/© 2016 International Business Machines Corporation Watson Developer Cloud Services
- 9. 9 Sensors & Devices VoIP Enterprise Data Social Media © 2016International Business Machines Corporation
- 10. An AI Renaissance CloudDeep Learning Probabilistic Reasoning Logic Probability Learning © 2016 International Business Machines Corporation 10
- 11.
- 12. Interpreting Medical Imagery 0 5 10 15 20 25 30 20102011 2012 2013 2014 ErrorRate(%) Human Error © 2016 International Business Machines Corporation 12
- 13. Recognizing Speech 0 5 10 15 20 25 2000 20022004 2006 2008 2010 2012 2014 2016 ErrorRate(%) Human Error © 2016 International Business Machines Corporation 13
- 14. Cognitive Workloads PutNew 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. Exponential Growth inLinked 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. Energy Efficient ArchitecturesCritical 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. Cognitive Computing Researchat IBM INFRASTRUCTURE COGNITIVE SERVICES APPLICATIONS FRAMEWORKS © 2016 International Business Machines Corporation 17
- 18. Signal Comprehension: Speech,Image, Video, Text Process & Understand Content Create Fast Accurate Dynamic (Un)Supervised Train © 2016 International Business Machines Corporation 18
- 19. 20102009 2011 20122013 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. 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. Learning Domains andReasoning Learn Extract Knowledge Decide Query & Retrieve Reason © 2016 International Business Machines Corporation 21
- 22. Extract Knowledge Decide Query & Retrieve ReasonLearn • Scale:Models, Training Data • Less Data • Hybrid Deep Learning • Causality © 2016 International Business Machines Corporation 22
- 23. Learn Decide Query & Retrieve Reason Extract Knowledge •Integrate: • Symbolic inference • Approximate/probabilistic reasoning • Learned Knowledge Modeling © 2016 International Business Machines Corporation 23
- 24. Learned Semantic DocumentRepresentation © 2016 International Business Machines Corporation 24
- 25. Learn Extract Knowledge DecideReason Query & Retrieve • Fusion •Learning on the job © 2016 International Business Machines Corporation 25
- 26. Learn Extract Knowledge Decide Query & Retrieve Reason & Plan •Symbolic Reasoning • Textual Reasoning • Integrated Reasoning • Hypothesis Planning © 2016 International Business Machines Corporation 26
- 27. With a paymentDurationof loadDuration and a $$$ down payment, how much is the periodicPayment payment? Policy & Product documentsCLIENT RECORD © 2016 International Business Machines Corporation 27
- 28. Learn Extract Knowledge Query & Retrieve Reason Decide •Recommendation • Collaboration • Industry Use Cases © 2016 International Business Machines Corporation 28
- 29. Computational Argumentation © 2016International Business Machines Corporation 29
- 30. © 2016 InternationalBusiness 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. People Insights Personality Interests Cultural Background Interests Mental/Physical State © 2016International Business Machines Corporation 31
- 32. Interaction Control Machine assists humansHumancontrols machines Sense Advise Converse Request © 2016 International Business Machines Corporation 32
- 33. Embodied Cognition Avatars Objects (e.g.IoT devices) Robots Spaces (e.g. rooms) © 2016 International Business Machines Corporation 33
- 34. Rules + TaskLearning + Context Contextual Understanding Action Planning Learning Sequences Words{ Conversation © 2016 International Business Machines Corporation 34
- 35. © 2016 InternationalBusiness 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. Building Computing Systems 36 1900+1950+ 2005+ © 2016 International Business Machines Corporation
- 37. Cognitive Systems Lifecycle MACHINEMODEL LIFECYCE SOFTWARE LIFECYCLE Operations ACQUIRE DATA CLEANSE DATA TRAIN MODEL DEBUG MODEL IMPLEMENTATION DESIGN DEBUG APPLICATION REQUIREMENTS © 2016 International Business Machines Corporation 37
- 38. Cognitive Systems Infrastructure DeepLearning 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. Brain-Inspired Systems -SyNAPSE © 2016 International Business Machines Corporation 39
- 40. © 2016 InternationalBusiness 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. “Help me replacethe broken component” Putting it all together © 2016 International Business Machines Corporation 41
- 42. Thank You © 2016International Business Machines Corporation 42
Editor's Notes
- #3 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
- #5 <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
- #6 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
- #7 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.
- #8 D2_John_Kelly_ppt2003_FINAL
- #10 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)
- #11 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>
- #12 <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
- #15 <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
- #19 <!-- HTML Credit Code for Can Stock Photo--> <a href="http://www.canstockphoto.com">(c) Can Stock Photo</a>
- #22 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
- #23 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)
- #24 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
- #26 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
- #27 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
- #28 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
- #29 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
- #30 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.
- #33 Old way: User acceptance determined by usability and desirability New way User acceptance determined by engagement, effective communication and ease of participation
- #34 Objects aware of those interacting with them: physical and virtual embodiments: Model, plan, represent, sense, respond
- #35 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
- #37 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>
- #38 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
- #39 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>
- #40 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.
- #42 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