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DATA AND AI APPLICATIONS, TOOLS, TECHNOLOGY DIRECTIONS

DATA AND AI
APPLICATIONS, TOOLS, TECHNOLOGY DIRECTIONS presented at HK Observatory

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DATA AND AI APPLICATIONS, TOOLS, TECHNOLOGY DIRECTIONS

  1. 1. Ikhlaq Sidhu, content author IKHLAQ SIDHU Chief Scientist and Founding Director Sutardja Center for Entrepreneurship & Technology IEOR Emerging Area Professor Department of Industrial Engineering & Operations Research, UC Berkeley DATA AND AI APPLICATIONS, TOOLS, TECHNOLOGY DIRECTIONS
  2. 2. Ikhlaq Sidhu, content author SUTARDJA CENTER AT BERKELEY M ETR I C S AT A G LANC E CHALLENGE LAB GLOBAL PROFESSORS NZTV SELF-DRIVING COLLIDER DATA-X SCET IN TAIWAN JOHN BATTELLE FOUNDER WIRED MAGAZINE NEWS COVERAGE Undergraduate: • 12-14 Courses, 1500+ Undergraduates X at Berkeley: Graduate, Labs and Professional • 80+ Grad students • 100+ Executives • Labs: Data-X, Blockchain, Sustainable Food Ecosystem: • 14+ Global Partners • 500+ Executives • 50+ Investors MARRISA MAYER
  3. 3. Ikhlaq Sidhu, content author IEOR135/290AppliedDataScience withVentureApplications SupportedbytheData-XLab
  4. 4. Ikhlaq Sidhu, content author • Detection of fake news • Prediction of long-term energy prices • Automatic recycling through image recognition • AI for crime detection, traffic guidance, medical diagnostics, etc. • A version of Zillow that is recalculated with the effects of AirBnB income • Signal processing and pattern analysis to improve earthquake warning systems • Early Autism Detection • Secure Health Records stored on a Blockchain find many, many more at: www.data-x.blog/projects Data-X Project Examples
  5. 5. Ikhlaq Sidhu, content author • 350 alumni students • 50% avg enrollment increase / semester • 80+ great projects completed • 8+ published research papers • 100+ industry experts in network • 20+ students got employment as data scientists only because they took Data-X Amazing testimonials: I think this class is so awesome because it teaches the tools and concepts that are most commonly used in workplace teams that are involved with data science and applied machine learning. We are building on expertise from Data-X, our highly applied Data Science & AI/ML Lab and Course
  6. 6. Ikhlaq Sidhu, content author Berkeley X-Labs A New Model for Applied Research Labs Future of X: Data, AI, Blockchain Expert team formation Innovation Mindset X Better University to Industry Connection Models ü Projects with Business Results ü Global Teams, Visiting Scholars ü Company Collaborations ü Executive Education and Train Trainer Models ü Self Evaluation Tools ü Startups Acceleration
  7. 7. Ikhlaq Sidhu, content author Data and AI Application
  8. 8. Ikhlaq Sidhu, content author Basic Concept of Working with Data • Data Wrangling • In Production
  9. 9. Ikhlaq Sidhu, content author Real-life Example: ZestCash • “All data is credit data” Online Loan Application Name: JOE SMITH Online Loan Application Name: Joe Smith The data says: greater credit risk! The data says: lesser credit risk! Reference: Shomit Ghose Example: Data and Information is a competitive advantage
  10. 10. Ikhlaq Sidhu, content author Harrah’s Casino: Knowing your customer • Service provider of Gambling and Casinos • Entry Card • Pain points • Intervention Reference: Supercrunchers
  11. 11. Ikhlaq Sidhu, content author Customer Insight/ Engagement Operations: Reliable & Predictable Security & Fraud Financial Firms Network Security Common Applications as of today …
  12. 12. Ikhlaq Sidhu, content author Who Will Control the Automobile? Does car need to buy gas? Gas stations will want to know. You can sell a new car once, but you can sell the data every minute of the day The world’s two largest sources of data • Google? or Ford? – Whoever has the better software and data science team – Winner will get the vast (and incredibly valuable) streams of auto data Who caused the accident? Insurance companies will want to know. Shomit Ghose
  13. 13. Ikhlaq Sidhu, content author Where Does Data Come From?
  14. 14. Ikhlaq Sidhu, content author Where Does Data Come From? Real-life Example: ZestCash • “All data is credit data” Online Loan Application Name: JOE SMITH Online Loan Application Name: Joe Smith The data says: greater credit risk! The data says: lesser credit risk! Your Own Web Site Public Data Sets Stock market, etc. IOT/Sensors Other Web Sites
  15. 15. Ikhlaq Sidhu, content author Web Scraping https://github.com/ikhlaqsidhu/data-x/tree/master/03-tools-webscraping-crawling_api_afo https://github.com/ikhlaqsidhu/data-x
  16. 16. Ikhlaq Sidhu, content author Formatting Data
  17. 17. Ikhlaq Sidhu, content author An ML High Level Framework • Objects • Events / Experiments • People / Customers • Products • Stocks • … In Real Life Features, but also loss of information In Sample Out of Sample Person 1 Person 2 Person 3 . . . Person N - Characteristics - Patterns - Models - Predictions - Similarities - Differences - Distance Some data has observed results
  18. 18. Ikhlaq Sidhu, content author CS: Table Math: Matrix X, with N rows – each person m columns, each feature (age, salary, ..) X = • Objects • Events / Experiments • People / Customers • Products • Stocks • … In Real Life Features, but also loss of information In Sample Out of Sample Person 1 Person 2 Person 3 . . . Person N - Characteristics - Patterns - Models - Predictions - Similarities - Differences - Distance Some data has observed results An ML High Level Framework
  19. 19. Ikhlaq Sidhu, content author Traditionally 2 Tasks: Classification & Predictive Scoring The most famous application has been recommendation: “which other user is most like you” Extracted Data often in Table Format Classification: Cats and Dogs, Speech Recognition Movie Recommendation Scoring: Credit Score, Movie Rating Heath Score, Any Isoquant…
  20. 20. Ikhlaq Sidhu, content author X Y X Y X YML Algorithms Guess this function F(x) We have now switched to Neural Networks as Function Approximators
  21. 21. Ikhlaq Sidhu, content author Neural net results are close t human results
  22. 22. Ikhlaq Sidhu, content author Data and AI Future Directions
  23. 23. Ikhlaq Sidhu, content author Peter Abbeel – Deep Reinforcement Learning Peter Abbeel Professor at UC Berkeley
  24. 24. Ikhlaq Sidhu, content author
  25. 25. Ikhlaq Sidhu, content author Recent AI News Source: Ken Goldberg, CPAR, People and Robotics Initiative
  26. 26. Ikhlaq Sidhu, content author Does this mean AI Can Do Everything Better than Humans
  27. 27. Ikhlaq Sidhu, content author Perfect Information vs. Real World fully observed uncertain discrete multi-agentsingle agent infinite time horizon continuous finite Ken Goldberg UC Berkeley Even then, AI Cannot Solve Real Life Problems Better Than Humans And in fact, AI Can not even Work without Humans Ken Goldberg Leading AI Researcher at Berkeley Professor and Department Chair, IEOR William S. Floyd Jr. Distinguished Chair
  28. 28. Ikhlaq Sidhu, content author Acknowledgement to Ken Goldberg UC Berkeley AI Systems Only Work because of Human are Part of the System Google Operations People Write Web Pages People at Google Tune the Results People Click on What They Want Result Feedback By clicks Massive Data There is no “Intelligence”, “Desire”, or “Existence” in AI without People There are only people who “invest in, design and operate the machines”
  29. 29. Ikhlaq Sidhu, content author 37 faculty At Berkeley, we have a lot of research on “How Machines Will Work as Part of Larger Systems that Work with People”
  30. 30. Ikhlaq Sidhu, content author My Drive Home From Berkeley
  31. 31. Ikhlaq Sidhu, content author Autonomous Driving and Driver-Assist •Communicating intent •Driver-in-the-loop modeling •Two-way learning: knowledge transfer between vehicle and driver •Safety in autonomous and assisted driving Principal investigators: Ken Goldberg UC Berkeley Anca Dragan UC Berkeley Trevor Darrell UC Berkeley Francesco Borrelli UC Berkeley Ruzena Bajcsy UC Berkeley Source: Ken Goldberg, CPAR, People and Robotics Initiative
  32. 32. Ikhlaq Sidhu, content authorSource: Ken Goldberg, CPAR, People and Robotics Initiative Safety in Human-Robot Interaction: Guarantees and Verification Safety-constrained motion planning for efficiency in factory human-robot interaction Learning and prediction for safety in HRI Provably safe human-centric autonomy Masayoshi Tomizuka UC Berkeley Principal investigators: Claire Tomlin UC Berkeley Francesco Borrelli UC Berkeley
  33. 33. Ikhlaq Sidhu, content author • Large-scale machine learning - amounts of data • Deep learning - recognition, classification • Reinforcement learning - time sequence, aided by Neural Networks • Robotics - beyond navigation, to safe interaction • Computer vision - most prominent perception, better than human • Natural Language Processing - interacting with people/dialog • Collaborative systems - autonomous systems w/people + machines using complimentary functions • Crowdsourcing and human computation – harness human intelligence, uses other AI, vision, ML, NLP, … • Algorithmic game theory and computational social choice – systems using social computing, incentives, prediction markets, game theory, peer prediction, scoring rules, no regret learning • Internet of Things (IoT) – using AI to unravel sensory information, interfaces, and protocols • Neuromorphic Computing – new computing fabrics based on biological models http://ai100.stanford.edu/2016-report New Data/AI Systems Most Common Data/AI Research Trends in 2017
  34. 34. Ikhlaq Sidhu, content author Unsupervised Image to Image [CycleGAN: Zhu, Park, Isola & Efros, 2017] Pieter Abbeel -- UC Berkeley | Gradescope | Covariant.AI Generative Models n “What I cannot create, I do not understand.” n Ability to generate data that look real entails some form of understanding [Radford, Metz & Chintala, ICLR 2016] Typical CNN converts image to output vector of features
  35. 35. Ikhlaq Sidhu, content author
  36. 36. Ikhlaq Sidhu, content author Large Investment and Valuations Some Very High Valuations Pieter Abbeel -- UC Berkeley | Gradescope | Covariant.AI Big Investments in China Pieter Abbeel -- UC Berkeley | Gradescope | Covariant.AI Big Investments in China Pieter Abbeel -- UC Berkeley | Gradescope Recent Headlines Pieter Abbeel -- UC B Recent Headlines [Macron (France), March 2018] Pieter Abbeel -- UC Berkeley | Gradescope | Covariant.AI
  37. 37. Ikhlaq Sidhu, content author Contact: Ikhlaq Sidhu Founding Faculty Director, Center for Entrepreneurship & Technology IEOR Emerging Area Professor, UC Berkeley sidhu @berkeley.edu, scet.berkeley.edu

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