Watson DevCon 2016 - From Jeopardy! to the Future
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The document details the evolution and capabilities of IBM Watson, emphasizing its journey from competing in Jeopardy! to its applications in various sectors like healthcare and finance. It discusses the architecture of Watson, known as DeepQA, which utilizes a combination of analytics, evidence scoring, and machine learning to improve its question-answering precision. Lastly, it introduces cognitive computing, focusing on amplifying human cognition and enhancing user interactions through advanced natural language processing and emotion recognition.
Watson DevCon 2016 - From Jeopardy! to the Future
- 1. From Jeopardy! to the Future Eric Brown, PhD Director, Watson Algorithms IBM Watson Health Rob High, Jr. IBM Fellow, Vice President, CTO IBM Watson
- 2. Please note IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice and at IBM’s sole discretion. Information regarding potential future products is intended to outline our general productdirection and it should not be relied on in making a purchasing decision. The information mentionedregarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential futureproducts may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and theworkload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.
- 3. The History of Watson Eric Brown, PhD Director, Watson Algorithms IBM Watson Health
- 4. A Brief History of AI 1945 Memex: Vannevar Bush 1970 1980 1990 2000 2010 Natural Language Processing Information Retrieval Machine Learning Knowledge Representation and Reasoning Question Answering Dartmouth Conference of 1956 State of the Art in Question Answering 1st AI Winter Reasoning as Search Natural Language Understanding Logic Neural Networks 2nd AI Winter Expert Systems Knowledge and Reasoning Text REtrieval Conference Big Data Computational Power
- 5. Real Language is Real Hard Chess – A finite, mathematically well-defined search space – Limited number of moves and states – Grounded in explicit, unambiguous mathematical rules Human Language – Ambiguous, contextual and implicit – Grounded only in human cognition – Seemingly infinite number of ways to express the same meaning
- 6. $200 The juice of this bog fruit is sometimes used to treat urinary tract infections $400 An early name for Google was this type of massage The Jeopardy! Challenge Broad/Open Domain Complex Language High Precision Accurate Confidence High Speed $600 In cell division, mitosis splits the nucleus & cytokinesis splits this liquid cushioning the nucleus $800 Grace Murray Hopper is credited with applying this 3-letter term to a mysterious computer problem $1000 IBM is known informally as Big this A palpable, compelling and notable way to drive the technology of Question Answering along Key Dimensions
- 8. What It Takes to compete against Top Human Jeopardy! Players Our Analysis Reveals the Winner’s Cloud Winning Human Performance 2007 QA Computer System Grand Champion Human Performance Top human players are remarkably good. Each dot – actual historical human Jeopardy! games More Confident Less Confident In 2007, we committed to making a Huge Leap! Computers? Not So Good.
- 9. Large Hand-Crafted Models won’t cut it – Too Slow, Too Narrow, Too brittle, Too Biased – Need to acquire and analyze information from As-Is Knowledge sources Intelligence from the combination of many – Consider Many Hypotheses. Reduce early biases. – Consider Many diverse algorithms. No single one is perfect or complete. – Analyze evidence form different perspectives – Balanced combination is continually learned, tested and refined Architecture to Support Integration and Learning – Facilitate integration of many components – Automatically learn how to combine – Enable scale-out and parallel deployment Drive Performance with Measurement and Analysis – Define appropriate metrics – Measure, analyze, improve, repeat Early Philosophical Commitments
- 10. DeepQA: The architecture underlying Watson Generates many hypotheses, collects a wide range of evidence and balances the combined confidences of over 100 different analytics that analyze the evidence from different dimensions Answer Scoring Models Answer & Confidence Question Evidence Sources Models Models Models Models ModelsPrimary Search Candidate Answer Generation Hypothesis Generation Hypothesis and Evidence Scoring Final Confidence Merging & Ranking Synthesis Answer Sources Question & Topic Analysis Evidence Retrieval Deep Evidence Scoring Learned Models help combine and weigh the Evidence Hypothesis Generation Hypothesis and Evidence Scoring Question Decomposition
- 11. 11 Goal-Oriented Metrics and Incremental Investments – Identify a Target and Technical Approach – Headroom Analysis: Estimate idea’s potential impact on key metrics – Balance long-term & short-term investments. Have the next priority ready. Be Agile. Extreme Collaboration – Implemented “One Room” to optimize team work and communication – Immediate access to the right “expert”, spontaneous discussions, no good idea lost Disciplined Engineering and Evaluation (Regular Blind Data Experiments) – Bi-weekly End-to-End Integration Runs & Evaluations (Large Compute Resources) – >10 GBs of error analysis output made accessible via Web-Based Tool – Positive impact on last run required to get into the next bi-weekly run Rapid Innovation Methodology Emerged >8000 Documented experiments performed in 4 years
- 12. DeepQA: Incremental Progress in Answering Precision on the Jeopardy Challenge: 6/2007-11/2010 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Precision % Answered Baseline 12/06 v0.1 12/07 v0.3 08/08 v0.5 05/09 v0.6 10/09 v0.8 11/10 v0.4 12/08 v0.2 05/08 IBM Watson Playing in the Winners Cloud V0.7 04/10
- 13. With Precision, Accurate Confidence and Speed, the rest was History
- 14. From battling humans at Jeopardy! to transforming how business thinks, acts, and operates Contact Center Healthcare Financial Services Government Diagnostic/treatment assistance, evidenced-based insights, collaborative medicine Investment and retirement planning, institutional trading and decision support Call center and tech support, enterprise knowledge management, consumer insight Public safety, improved information sharing, security
- 15. The Era of Cognitive Computing Rob High, Jr. IBM Fellow, Vice President Chief Technology Officer IBM Watson
- 16. Cognitive systems amplify human cognition.
- 17. Cognitive systems understand human expressions – textual, verbal, visual By reasoning about the actual intention or problem being addressed They learn how to recognize patterns of meaning through examples and feedback And they interact with humans on their own terms, and in a way that inspires people. What is Cognitive Computing? … and do so at enormous scale.
- 18. Watson Reference Model Developer Tooling Platforms as a Service Watson Services Data as a Service Watson Content Application Tooling Maker Tooling Content Tooling Cloud Infrastructure Public Private Crowd Sourced Knowledge Organization Skills Foundational Cognitive Skills Higher Reasoning Skills IBMWatsonMarketplace Skills as a Service Watson Built Apps & Bots Watson Built Skills IBM Built Apps & Bots IBM Built Skills 3rd Party Apps & Bots 3rd Party Skills Software as a Service Watson Apps & Bots Hybrid Client Platform Watson Explorer
- 19. Watson is available as a set of services delivered as APIs in the Cloud Higher Reasoning Skills • Conversation Higher Reasoning Skills Ibm.com/bluemix Can be combinedwith the 100s ofother available services on Bluemix
- 20. • Add a natural language interface to your application to automate interactions with your end users. • Common applications include virtual agents and chat bots that can integrate and communicate on any messaging platform. • Model is trained on user-defined intents, entities and dialogs • Expanded to recognize the emotion of the user and to respond accordingly Conversation
- 21. • Intents classify the kinds of actions or questions that the conversation will respond to • You can begin with as few as just one intent, and then expand as new functionality is added to the virtual agent • It only takes a few examples to train Watson to recognize a wide variety of ways to express an intent • The tooling allows you to test as you train Conversational Intents
- 22. • Conversation can detect entities in an utterance and identify them to clarify the intent • Typically used with Dialogs to condition the response Conversational Entities
- 23. • Dialogs can be created to control the flow of the conversation around specific intents Conversational Dialogs
- 24. Watson is available as a set of services delivered as APIs in the Cloud Watson Foundational Skills are grouped into four categories Ibm.com/bluemix Foundational Cognitive Skills Language • Author • Concepts • Dates • Entities Speech • Speech to Text • Telephony Speech to Text • Keyword Spotting Vision • Image Classification • Face Detection and Attribution • Celebrity Recognition Empathy • Personality Insights • Tone Analyzer • Emotion Analysis • Text to Speech • Expressive Text to Speech • Relations • Typed Relations • Sentiment • Taxonomy • Text Extraction • Feeds • Keywords • Language • Microformats • Publication Date • Visual Text Recognition • Similarity Searching Can be combinedwith the 100s ofother available services on Bluemix
- 25. Tone Analyzer understands and helps fine tune your message Uses psycholinguistics, emotion analysis and language analysis to assess Tone Online Dating Profile I'm a hard working adventurous, very talented man who's been caring and helpful throughout my life, I like to travel, play my guitar, dance, and cook, I love the beach, sailing my boat, and the outdoors. I raised two great kids and now I'm starting a new chapter in my life. Thanks. What I’m doing with my life Working toward a new goal, keeping fit, helping others, and traveling whenever i get a chance. I’m really good at Listening, enjoying the moment, and many other things. The six things I could never do without Family, the ocean, intimacy, friends, adventure, music, love. On a typical Friday night I am Meeting with friends, listening to a band or playing my guitar, dancing or just staying home with someone special and enjoying each other. You should message me if You're looking for a relationship with someone that likes to sail his boat, ride bicycles, travel, swim, go to the beach, listen to music and enjoy everyday pleasures together.
- 26. Emotional Analysis helps build empathetic systems Uses state-of-the-art machine learning models and feature engineering techniques to predict emotion labels
- 28. Watson Visual Recognition Service
- 29. Watson Visual Recognition leveraged to enhance the game of Pokemon Go
- 30. Enables speech that reflects intended tone – Expressive SSML – Voice Transformation SSML Expressive Text to Speech
- 31. Watson is available as a set of services delivered as APIs in the Cloud KnowledgeOrganizationSkills • Watson Knowledge Studio • Document Conversion • Retrieve and Rank Knowledge Organization Skills Ibm.com/bluemix Can be combinedwith the 100s ofother available services on Bluemix
- 32. Enable subject matter experts and developers to teach Watson the linguistic nuances of industries and knowledge domains Watson Knowledge Studio
- 33. Uses machine learning to improve search across documents using natural language questions Retrieve and Rank
- 34. Amplifying Human Cognition Requires that we affect people in a very human way Human can be influenced by cognitive experiences – Something that creates a sense of presence with people – Something that inspires people – Something that embodies intellectual cognition with behavioral cognition Project Intu is a Platform for creating great cognitive experiences avatars In-the-walls devices spaces wearables robotics Go to the Project Intu workshop to get access to the experimental platform and to learn how to program Intu to create great cognitive experiences
- 35. • Amplify human creativity - Inspiring us to new alternatives to decision options - Bringing the breadth of all human knowledge to the tip of our tongue • Learn their behavior through formal and informal training processes • Interact with humans on our terms – in the language of humans • Demonstrate their expertise through trust and depth of character • Evolve strategies of success – adapting to ever changing knowledge and understanding • Establish transformative relationships between humans and machines In 5 years, cognitive systems will be to computing what transaction processing is today
- 36. ibmwatson.com facebook.com/ibmwatson @ibmwatson