Cognitive Internet of Things: Making Devices Intelligent

1. Session A3 Cognitive Internet of Things: Making Devices Intelligent Swami Chandrasekaran Executive Architect - CTO Office IBM Watson Innovations swamchan@us.ibm.com @swamichandra

2. Please Note IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products 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 the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. © 2014 IBM Corporation Mobility LIVE! 2014 2

5. Consider the following questions for a moment • How can I communicate and have a dialog with my connected devices using natural language? • Will the devices be able respond back to me with a degree of confidence and contextually? • Can these devices understand the end user at a deeper psychographic segmentation level and alter the resonance of the responses? • How can I discover new insights from my customer / end user interactions in a timely fashion? • Will the wearables based apps learn from new data & observations and get intelligent over time? © 2014 IBM Corporation Mobility LIVE! 2014 5

8. Analytics Russian Doll Cognitive Tell me the best course of action Prescriptive What should I do to for the best outcome? Predictive What could happen? Descriptive What has happened? Business Value © 2014 IBM Corporation Mobility LIVE! 2014 8

9. This figure resembling a droid was purely unintentional J So what are the Characteristics of a Cognitive System Scale in Proportion Engage in a Dialog Generate & Evaluate Hypothesis Understand Natural Language Provide Supporting Evidence Ingest Variety of (Big) Data Respond with Degree of Confidence Learn with Every Interaction Offer Contextual Guidance & Insights Support for Decision Making Understand user at a Deeper level Relate between Terms & Concepts © 2014 IBM Corporation Mobility LIVE! 2014 9

10. Consider this Natural Language Question A restaurant in Chicago? Several critics have raved about Zhivago and what a masterpiece it was. Was it shown in Russia in 2001? Are we talking about Art or Sculpture or Movie or Food? Plain Number (or) a Temporal Reference? Geographic Reference? Keyword search and expert systems are not able to recognize the subtleties, idiosyncrasies, and ambiguities inherent in common human language © 2014 IBM Corporation Mobility LIVE! 2014 10

11. This is how a Cognitive System like IBM Watson would respond with movies related content ingested as Corpus … and other possible answers … and other possible answers With a level of confidence … and Evidence © 2014 IBM Corporation Mobility LIVE! 2014 11

12. Cognitive systems enhance our abilities to observe, evaluate and decide Observe: • Learns from a vast body of (unstructured) content • Looks beyond the surface • Understand Natural Language Interpret & Evaluate: • Generate & Evaluate hypotheses • Finds relationships between terms and concepts • Simplifies complex thinking Decide: • Understands with me at a deeper level • Evaluates pros and cons. Helps me discover new ideas • Lets me be myself & engages with me personally Learning: • Learns from every interaction • Trains with experts and improves with feedback Observe Decide Interpret & Evaluate © 2014 IBM Corporation Mobility LIVE! 2014 12

14. Anatomy of an IoT Solution / Application - Setting Context Systems Integration Applications Cognitive Services Data at Rest Analytics Focus of the session ✔ Data Ingestion & Streaming Analytics Connectivity Management Network Devices / Sensors Platform & Services Connectivity & Devices Users of Things Wizard’s stitching the perfect Composable Apps Platform, Services, IoT Cloud Providers of Connectivity Makers of Things © 2014 IBM Corporation Mobility LIVE! 2014 14

15. Introducing Cognitive Internet of Things (IoT) • Provide capabilities for IoT apps & solutions to have cognition • Allows IoT apps & solutions to exhibit characteristics such as, – Deep natural language understanding – Accurate & evidence based decisions – Relating & linguistic analysis – Maps euphemisms or colloquial terms – Deeper understanding of user intrinsic preferences / characteristics – Communicating with resonance – Knowledge & relationships discovery – Continuous learning © 2014 IBM Corporation Mobility LIVE! 2014 15

19. Question & Answer • Allow end applications users to converse using natural language • Understand a question in NL, generate and evaluate hypothesis and respond with degree of confidence and evidence • Interpret questions & answers user questions directly based on, – unstructured content (PDF, Word, HTML, TXT) – primary data sources (brochures, web pages, manuals, etc.) – selected and gathered into a body of corpus © 2014 IBM Corporation Mobility LIVE! 2014 19

20. Meet Rosy • Recently purchased and installed a smarter thermostat • Very savvy smart phone user • She has a question about restricting only authorized users to be able to configure & access the thermostat • Has a smart phone app that allows her to pose questions in natural language and have a conversational dialog I need to restrict the access to modify certain capabilities in the thermostat. How can set it up? Integrate via API Q&A Dialog Interact using NL Product Corpus Manuals © 2014 IBM Corporation Mobility LIVE! 2014 20

22. Concept Expansion • Allows IoT apps greater insight across multitude of unstructured documents • Map euphemisms or colloquial terms to more commonly understood phrases • Analyze text and interpret its meaning based on usage in other similar contexts • For e.g., a semantic class, such as “drugs” can be expanded to, – start seed terms à motrin, aspirin, keflex – post expansion à allegra, lisinopril, metformin, aspirin, equagesic, cimetidine, fiorinal, vancomycin, avelox, protonix, glimepiride, protonix, verapamil, norco, inderal, hctz, advair • Well suited for expanding where the unstructured source text does not contain well formed language (e.g., social media data, email, helpdesk reports, and other less formal communications) © 2014 IBM Corporation Mobility LIVE! 2014 22

23. Meet Zhang • Remotely monitored patient • Can use an app to interact with his healthcare providers • English not first language • Need for understanding nuances in his less than formal communications • Concept Expansion service returns a ranked list of contextually similar terms • Learned from the provided 'seed list' against the Zhang’s interaction history Interact using NL informally Integrate via API Concept Expansion Interaction Seed List History © 2014 IBM Corporation Mobility LIVE! 2014 23

26. User Modeling • Use linguistic analytics to extract personality and social traits, including Big 5, Values, and Needs, from the way that a person communicates. • Analyze any digital footprint that the user makes available, such as email, text messages, tweets, forum posts, and more. • Leverage cognitive and social characteristics with their corresponding percentile values as the basis for analyzing personality and social traits. • IoT apps can leverage this for targeted customer and end user interaction and acquisition via personality-driven engagements (offers, recommendations etc.) © 2014 IBM Corporation Mobility LIVE! 2014 26

27. Meet Ravi • Very vocal and maintains a strong digital presence • Has a long day at work !! • Tweets with certain emotions and walks to the car • Car has done a psychographic analysis of his digital footprint and alters its response resonance • Recommends or tunes to the “comedy” radio channel • Understands from past behavior, the driver would want to go to the gym Walk to Car Share Location & User Context Modeling Recommended Infotainment Prediction Past Interactions © 2014 IBM Corporation Mobility LIVE! 2014 27

28. Meet Ravi … again • Very vocal and maintains a strong digital presence • Seeks recommendation for happy hour • Based on Ravi’s location fetch digital tweets, blogs, FB updates about relevant places • Extract Big 5, Values, and needs • Provide as input to a a classification ML model to recommend apt place for Ravi Seek recommendation Share Location & User Modeling Context Prediction © 2014 IBM Corporation Mobility LIVE! 2014 28

31. Relationship Extraction • Intelligently finds relationships between sentences components (nouns, verbs, subjects, objects, etc.) in unstructured text • Extract entities such as person, organizations, locations, devices, events, etc., and relationships between them • Relationships help easily understand the meaning & intent of individual sentences and documents • Enables automated processes to understand unstructured content in healthcare, drug discovery, financial reports, news and blog monitoring, etc. © 2014 IBM Corporation Mobility LIVE! 2014 31

34. Message Resonance • Communicate with people with a style & words that suits them • Analyzes content and score how well it is likely to be received by a specific target audience. • Analysis is based on content that’s been written by the target audience itself (team sports fans, new product users, etc) • Add capabilities within the IoT apps (smart TV, personal wellness) to maximize the resonance of your messaging • Enables IoT apps to best engage with end users and facilitate consistent messaging © 2014 IBM Corporation Mobility LIVE! 2014 34

37. How Cognitive Computing will help IoT Solutions & Apps? • Overcome Complexity [1] – Ingestion of 4V (volume, variety, velocity, veracity) data esp., unstructured ones – Uncover hidden relationships (Bad weather à Tweets à Extract Insights) • Getting the Big Picture – Extract & Correlate patterns, concepts & relationships across content – Expansion of concepts • Augment our Senses [1] – Converse in natural language – Deeper understanding of the individual & group – Chip modeled on the brain • Support Discovery & Decision Making – Hypotheses based inferences – Offer contextual insights – Support at point of decision making 1 - Smart Machines: IBM’s Watson and the Era of Cognitive Computing - John E. Kelly III and Steve Hamm © 2014 IBM Corporation Mobility LIVE! 2014 37

39. Some Interesting Links • IBM Watson – http://www.ibmwatson.com • IBM Bluemix – http://www.bluemix.net • Node-RED – http://nodered.org/ • Watson Ecosystem program – http://www.ibm.com/smarterplanet/us/en/ibmwatson/ecosystem.html • Growing social dialogue – Twitter: @IBMWatson • IBM Watson: A Platform for Innovation in the New Era of Computing – http://asmarterplanet.com/blog/2014/04/ibm-watson-ecosystem-platform-innovation.html • IBM unveils a computer that can argue – http://finance.yahoo.com/blogs/the-exchange/ibm-unveils-a-computer-than-can-argue-181228620.html © 2014 IBM Corporation Mobility LIVE! 2014 39

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