IBM Watson in the Cognitive Era
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Armen Pischdotchian from IBM Watson introduces the platform.
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IBM Watson in the Cognitive Era
- 1. IBM Watson in the Cognitive Era Armen Pischdotchian October 23, 2016
- 2. If you had to choose what machine learning problems to solve…. Understanding language or Understanding vision © 2016 International Business Machines Corporation
- 3. Natural language processing – a classification problem © 2016 International Business Machines Corporation
- 4. Here are common implementation patterns seen for cognitive systems High-Level Cognitive Patterns Expert Advisor Trained by domain experts to recommend optimal purchasing decisions for consumers Professional Assistant Help professionals make decision by sifting through large amounts of data. Agent Assist Helps technical/customer support agents rapidly search corporate knowledge for the best resolution for customer complaints. Data Insights Extract features from natural language to surface for data-driven decision making Relative DifficultyEasiest Bleeding Edge Personal Assistant Virtual secretaries to answers simple questions and performs routine tasks. Automation Automatically perform routine tasks based on recognition of specific natural language phrases Moderate Challenging Q&A: still very hard to do! Recommendations Analyze prior purchase behaviors of a customer and similar customers to surface new purchase options. © 2016 International Business Machines Corporation
- 5. First there were websites, then apps now bots One-to-one VS one-to-many For the first time in advertising/marketing history, brands have the opportunity to reach consumers on a personal level, at a pace of play that they (consumers) dictate. In a conversational world, marketers can no longer rely on the “yell & sell” approach and hope for the best. They need to “chat and listen.” Small data VS big data One-to-one communication gives you personal insights and specificity. It gives you snackable data that is actionable. And, for the first time, it creates the perfect platform to sequence messages in a natural, contextual, human way. Always on VS always perfect The rise of the “real and raw” thanks to live apps like Periscope or Snapchat stands as the new social currency Millennials and Generation Z teens live by . Being always on and keeping it real becomes therefore much more important for a brand than being picture or pixel perfect (and thus only sporadically on/always late). Every conversation is logged in so you can keep tracking, iterating and improving the quality of your bot over time. © 2016 International Business Machines Corporation
- 6. Watson is Deep Learning © 2016 International Business Machines Corporation
- 7. Everywhere you look there is an S- curve © 2016 International Business Machines Corporation
- 8. © 2016 International Business Machines Corporation Machine learning camps (The Master Algorithm by Pedro Domingo) Camp Views on machine learning Favorite algorithm Symbolists Connectionists Evolutionaries Bayesians Analogizers …view learning as the inverse of deduction and take ideas from philosophy, psychology and logic. Inverse deduction …reverse engineer the brain and are inspired by neuroscience and physics. Backpropagation …simulate evolution on the computer and draw on genetics and evolutionary biology Genetic programming …believe learning is a form of probablistic inferences and have their roots in statistics. Bayesian inferences …learn by extrapolating from similarity judgments and are influenced by psychology and mathematical optimization. Support vector machine
- 9. © 2016 International Business Machines Corporation Three major AI caliber categories: Artificial Narrow Intelligence (ANI): Sometimes referred to as Weak AI, Artificial Narrow Intelligence is AI that specializes in one area. ANI can beat Jeopardy! World champion. Ask it to figure out a better way to store data on a hard drive, and it’ll look at you blankly. Artificial General Intelligence (AGI): Sometimes referred to as Strong AI, or Human-Level AI, Artificial General Intelligence refers to a computer that is as smart as a human across the board—a machine that can perform any intellectual task that a human being can. Artificial Superintelligence (ASI): Oxford philosopher and leading AI thinker Nick Bostrom defines superintelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” Artificial Superintelligence ranges from a computer that’s just a little smarter than a human to one that’s trillions of times smarter—across the board.
- 10. © 2016 International Business Machines Corporation When will we see AGI and ASI?
- 11. IBM Zurich has built a version of an artificial neuron © 2016 International Business Machines Corporation Evangelos S Eleftheriou, IBM Fellow Germanium antimony telluride is a tiny blob sandwiched between two electrodes and has the property of a phase-change material. It starts of as a disordered blob that lacks any atomic structure and which conducts electricity poorly. If a low voltage electrical jolt is applied, a small portion of the stuff will heat up and rearrange itself into an ordered crystal with much higher conductivity. Apply enough such jolts and most of the blob will become conductive and the neuron fires, just like a neuron. A high voltage current is then applied to melt the crystal and reset the neuron.
- 12. © 2016 International Business Machines Corporation
- 13. © 2016 International Business Machines Corporation “Growth and comfort cannot coexist.” - Ginni Rometty
- 14. Notices and disclaimers Copyright © 2016 by International Business Machines Corporation (IBM). No part of this document may be reproduced or transmitted in any form without written permission from IBM. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM. Information in these presentations (including information relating to products that have not yet been announced by IBM) has been reviewed for accuracy as of the date of initial publication and could include unintentional technical or typographical errors. IBM shall have no responsibility to update this information. THIS DOCUMENT IS DISTRIBUTED "AS IS" WITHOUT ANY WARRANTY, EITHER EXPRESS OR IMPLIED. IN NO EVENT SHALL IBM BE LIABLE FOR ANY DAMAGE ARISING FROM THE USE OF THIS INFORMATION, INCLUDING BUT NOT LIMITED TO, LOSS OF DATA, BUSINESS INTERRUPTION, LOSS OF PROFIT OR LOSS OF OPPORTUNITY. IBM products and services are warranted according to the terms and conditions of the agreements under which they are provided. IBM products are manufactured from new parts or new and used parts. In some cases, a product may not be new and may have been previously installed. Regardless, our warranty terms apply.” Any statements regarding IBM's future direction, intent or product plans are subject to change or withdrawal without notice. Performance data contained herein was generally obtained in a controlled, isolated environments. Customer examples are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual performance, cost, savings or other results in other operating environments may vary. References in this document to IBM products, programs, or services does not imply that IBM intends to make such products, programs or services available in all countries in which IBM operates or does business. Workshops, sessions and associated materials may have been prepared by independent session speakers, and do not necessarily reflect the views of IBM. All materials and discussions are provided for informational purposes only, and are neither intended to, nor shall constitute legal or other guidance or advice to any individual participant or their specific situation. It is the customer’s responsibility to insure its own compliance with legal requirements and to obtain advice of competent legal counsel as to the identification and interpretation of any relevant laws and regulatory requirements that may affect the customer’s business and any actions the customer may need to take to comply with such laws. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the customer is in compliance with any law. 14 12/1/2016World of Watson 2016
- 15. Notices and disclaimers continued Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products in connection with this publication and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. IBM does not warrant the quality of any third-party products, or the ability of any such third- party products to interoperate with IBM’s products. IBM EXPRESSLY DISCLAIMS ALL WARRANTIES, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. The provision of the information contained herein is not intended to, and does not, grant any right or license under any IBM patents, copyrights, trademarks or other intellectual property right. IBM, the IBM logo, ibm.com, Aspera®, Bluemix, Blueworks Live, CICS, Clearcase, Cognos®, DOORS®, Emptoris®, Enterprise Document Management System™, FASP®, FileNet®, Global Business Services ®, Global Technology Services ®, IBM ExperienceOne™, IBM SmartCloud®, IBM Social Business®, Information on Demand, ILOG, Maximo®, MQIntegrator®, MQSeries®, Netcool®, OMEGAMON, OpenPower, PureAnalytics™, PureApplication®, pureCluster™, PureCoverage®, PureData®, PureExperience®, PureFlex®, pureQuery®, pureScale®, PureSystems®, QRadar®, Rational®, Rhapsody®, Smarter Commerce®, SoDA, SPSS, Sterling Commerce®, StoredIQ, Tealeaf®, Tivoli®, Trusteer®, Unica®, urban{code}®, Watson, WebSphere®, Worklight®, X-Force® and System z® Z/OS, are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at "Copyright and trademark information" at: www.ibm.com/legal/copytrade.shtml. 15 12/1/2016World of Watson 2016
- 16. Thank You
Editor's Notes
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- Google recommends the popular Netflix recommends the long tail Data Insights: Customer: Analyze customers to understand purchasing behavior and direct them to new/more purchases Influencer: Analyze social media influencers to have them to send positive recommendations to their audiences Automation: Business Processes: Optimize routine or repetitive tasks by automatically taking action based on defined inputs. E.g. routing hotel guest requests to appropriate team (room service, maintenance, front desk, …) Agent Assist: Call Center Agents: Listen to incoming audio/chats and recommend best replies. Note: In this use case, pre-formatted answers exists and the system rapidly surfaces them. Expert Advisor: Retail: Recommend purchases based on user profiling such as demographics and prior purchase history. Information Retrieval: Guide novices to make more informed decisions about complicated topics. E.g. health, finance, and legal decisions Professional Assistant: Physicians: Recommend treatment and lab tests based on patient family and personal medical history plus lab results. Lawyers: Recommend legal documents based on prior case history and legislation. Financial Advisors: Recommend investments based market analysis Human Resources: Recommend employees for job openings
- Goodbye interruptions, hello conversations.
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- L=f(E) y=f(x) A typical neuron spikes occasionally in the absence of stimulation, spikes more and more frequently as stimulation builds up, and saturates at the fastest spiking rate it can muster, beyond which increases stimulation has no effect. Rather than a logic gate, a neuron is more like a voltage-to-frequency converter. {draw the curve} Starts slowly, then faster and faster until it becomes almost constant again The S-curve is the shape of phase transitions of all kinds: the probability of an electron flipping its spin as a function of the applied field, the magnetization of iron, the writing of a bit of memory to a hard disk, an ion channel opening in a cell, ice melting, the inflationary expansion of the early universe. Joseph Schumpeter said the economy evolves by cracks and leaps---S curves are the shape of creative destruction In Hemingway’s The Sun also Rises, when Mike Campbell is asked how he went bankrupt, he replies: “two ways. Gradually and then suddenly. When you can’t get the temperature in the shower just right—first it’s too cold, and then it quickly shifts to too hot, blame the S curve. Popcorn Many phenomenon we think of as linear are in fact S curves, because nothing can grow without limit. Differentiate an S curve and you get a bell curve.
- Sceba: SymConEvoBayAna Symbolist understand that you can’t learn from scratch; you need some initial knowledge to go with the data. Inverse deduction figures out what knowledge is missing in order to make a deduction go through, then make it as general as possible. Connectionist learning is what the brain does, and so what we need to do is reverse engineer it. The brain learns by adjusting the connections between neurons and the crucial problem is figuring out which connections are to blame for which errors and change them accordingly. In backpropagation, the algorithm compares the system’s output with the desired one and then successively changes the connection in layer after layer of the neurons so as to bring the output closer to what it should be Evolutionaries believe that the mother of all learning is natural selection. The key problem that Evolutionaries solve is learning structure.: not just adjusting parameters, like back propagation does, but creating the brain that those adjustments can then fine-tune. Bayesians are concerned above all with uncertainty. All learned knowledge is uncertain, and learning itself is a form of uncertain inference. The problem then becomes how to deal with noisy, incomplete and even contradictory information without falling apart. The solution is probabilistic inference, their algorithm is Bayes theorem and its derivatives. Bayes Theorem tells us how to incorporate new inference into our beliefs based on new variables, and probabilistic inference do that as efficiently as possible. For Analogizers, the key to learning is recognizing similarities between situations and thereby inferring other similarities. If two patients have similar symptoms, perhaps they have the same disease. The key problem is judging how similar two things are. Their algorithm, SVM figures out which experiences to remember and how to combine them to make new deductions
- There are many different types or forms of AI since AI is a broad concept, the critical categories we need to think about are based on an AI’s caliber. There are three major AI caliber categories: AI Caliber 1) Artificial Narrow Intelligence (ANI): same as above AI Caliber 2) Artificial General Intelligence (AGI): Creating AGI is a much harder task than creating ANI, and we’re yet to do it.” AGI would be able to do all of those things as easily as you can. AI Caliber 3) Artificial Superintelligence (ASI): ASI is the reason the topic of AI is such a spicy meatball and why the words “immortality” and “extinction” will both appear in these posts multiple times. As of now, humans have conquered the lowest caliber of AI—ANI—in many ways, and it’s everywhere. The AI Revolution is the road from ANI, through AGI, to ASI—a road we may or may not survive but that, either way, will change everything.
- germanium antimony telluride is used in optical materials, sputtering target, coating, vacuum spraying materials, solar materials, semiconductor. Phase-change material means it’s physical structure alters as electricity passes through it. Neurons are unpredictable. Fluctuations within a cell mean a given input will not always produce the same output. To an electronic engineer that is anathema. But nature makes clever use of this randomness to let groups of neurons accomplish things that they could not if they were perfectly predictable. They can, for instance, juggle a system out of a mathematical trap called a local minimum where a digital computer’s algorithms might get stuck. Software neurons must have their randomness injected artificially. But since the precise atomic details of crystallization process in IBM’s ersatz neurons differ from cycle to cycle, their behavior is necessarily slightly unpredictable. The next step is linking such neurons to networks. Small versions of these networks could be attached to sensors and tuned to detect anything from, say, unusual temperatures in factory machinery, to worrying electrical rhythms in a patient’s heart, to specific types of trade in financial markets.
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