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Artificial Intelligence Progress - Tom Dietterich

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Real-world Stories and Long-term Risks and Opportunities.
Tom Dietterich, Ph.D.
Technical and Business Perspectives on the Current and Future Impact of Machine Learning - MLVLC
October 20, 2015

Published in: Data & Analytics
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Artificial Intelligence Progress - Tom Dietterich

  1. 1. Artificial Intelligence: Progress and Challenges Tom Dietterich
  2. 2. Tool AI High-Stakes Autonomy Near-Term Risks ? Technological Unemployment
  3. 3. What is AI? Smart Software  vision, speech, touch  choosing actions to achieve goals  learning  understanding and predicting behavior Credit: Andrej Karpathy, Li Fei-Fei
  4. 4. Exciting Progress: Perception 2013 2014 2015 23% Word Error 8% Google Speech Recognition Credit: Fernando Pereira & Matthew Firestone, Google Google Translate from Images Credit: www.bbc.com “a black and white cat is sitting on a chair." Credit: Jeff Donahue, Trevor Darrell
  5. 5. Exciting Progress: Perception Deep
  6. 6. Exciting Progress: Reasoning (SAT) Credit: Vijay Ganesh
  7. 7. Exciting Progress: Reasoning (Poker) Moore’s Law Heads-Up Limit Texas Hold’em Rhode Island Hold’em Credit: Michael Bowling
  8. 8. Exciting Progress: Chess & Go Monte Carlo Tree Search Credit: Martin Mueller
  9. 9. Exciting Progress: Collaborative Systems Red: Best AI solution Yellow: “Foldit Void Crushers Group” solution Blue: X-Ray Crystal Structure solution Sept 20, 2011
  10. 10. What’s Next? Tool AI • Google, Bing • IBM’s Watson • Siri, Cortana, Google Now • Google Translate • Skype Real-Time Translation Autonomous AI • AI Hedge Funds • High Speed Trading • Self-Driving Cars • Automated Surgical Assistants • Smart Power Grid • Autonomous Weapons
  11. 11. Tool AI
  12. 12. Tool AI: What’s Next? Deeper understanding of video: • What type of play? • Who carried the ball? • Was the pass complete? • Which players made mistakes? • Which players achieved their goals? Credit: Alan Fern
  13. 13. Tool AI: What’s Next? Deeper understanding of text: • Is Yoadimnadji dead? Yes • Is he in Paris? Yes • Is he married? Yes • Where is his wife? In Paris • Where will she be in the future? In Chad “Pascal Yoadimnadji has been evacuated to France on Wednesday after falling ill and slipping into a coma in Chad. His wife, who accompanied him to Paris, will repatriate his body to Chad.”
  14. 14. Tool AI: What’s Next? Linking Big Data to Medicine: • Web search logs can detect adverse drug interaction events better than FDA’s existing Adverse Drug Interaction Reporting Service White, Harpaz, Shah, DuMouchel, Horvitz, 2014
  15. 15. Tool AI: What’s Next? Improved Personal Assistants that combine • Knowledge of recipes (lasagna ingredients) • Wine pairing recommendations (cabernet or pinot noir) • Brother’s home address • Routes to brother’s home address • Stores along that route that have cheap cabernet or pinot in stock to produce a plan Source: Wired August 12, 2014
  16. 16. High-Stakes Autonomy Near-Term Risks
  17. 17. Autonomous AI Systems: What’s Next? • AI Hedge Funds • High Speed Trading • Self-Driving Cars • Automated Surgical Assistants • Smart Power Grid • Autonomous Weapons
  18. 18. The Motivations The Dangers High-Stakes Autonomy  Advances in AI enable exciting applications  There is a great potential to save lives  There is a need to act at lightning speed  Bugs  Cyber Attacks  Mixed Autonomy  Misunderstanding User Commands
  19. 19. Software Quality  Many AI methods give only probabilistic guarantees  Research Question: How can we ensure safe performance of AI-based autonomous systems? "a young boy is holding a baseball bat." Credit: Andrej Karpathy, Li Fei-Fei
  20. 20. Cyber Attacks  High-stakes systems  large and enticing “attack surface”
  21. 21. Cyber Attacks on AI Systems Training Set Poisoning:  Make yourself invisible to a computer vision system  Make yourself look “normal” to an anomaly detection system  Bias the behavior of the system  Bid slightly higher on certain stocks  Prefer to show certain advertisements Credit: Katherine Hannah
  22. 22. Mixed Autonomy  Auto-pilot unexpected hand-off to pilots  Pilots lack situational awareness and make poor decisions  Question: How can we make imperfect autonomous systems safe? AF447 Pilots Final Conversation Pilot 1: “What the...how is it we are going down like this?” Pilot 2: “See what you can do with the commands up there, the primaries and so on...Climb climb, climb, climb Credit: www.aviationlawmonitor.com
  23. 23. Incomplete/incorrect Commands  “Fetch me some water”  Question: How can the computer reliably infer what the user intended? Credit: www.disney.com
  24. 24. Trustable AI for Autonomy Many research teams are at work Verification and Self-Monitoring Knowledge of Desirable and Acceptable Behavior Improved User Interaction
  25. 25. Technological Unemployment
  26. 26. 1980 1990 2000 2010 40 80 120 160 200 240 280 320 US Industrial Production US Manufacturing Employment Index:01.1972=100 Similar trends are seen worldwide Source: Andrew McAfee / US Federal Reserve US is Producing More with Fewer Employees
  27. 27. As AI advances, many existing jobs have the potential to be automated Source: Frey & Osborne, 2014: “The future of employment: How susceptible are jobs to computerisation?”
  28. 28. Three Reasons to be Optimistic  AI often complements human intelligence rather than replacing it  Increases in productivity increases in wealth increases in consumption demand for new goods and experiences  new kinds of jobs  A focus on current jobs, underestimates the creativity of people to develop new products and services, new industries, etc.
  29. 29. What will be the new jobs?  Jobs involving hard-to-automate skills  high levels of social skills  deep understanding of human experience and emotion  high levels of creativity  Jobs where human + machine is better than either alone  Augmented Cognition  combining strategic thinking with detailed tactics: “Centaur Chess”  combining physical dexterity with information access: augmented reality vehicle maintenance Source: Tartajubow.blogspot.com Source: metaio / designboom.com
  30. 30. Historical and cultural artifacts become more valuable  AI cannot produce another Roman Empire, Greek Civilization, or Al Andaluz  AI + Augmented Reality may make tourism even more compelling and meaningful Credit: http://www.hellovisitspain.com
  31. 31. Life-Long Personal AI Assistant?  Human-machine pairs work best when they know each other very well  strengths and weaknesses  easy communication  preferred ways of sharing tasks  A vision:  Student enrolls in technical college  Student is given an AI personal assistant  The student and the assistant train together  Learn how to solve problems jointly and efficiently  Employer hires the pair together source: starwars.wikia.com
  32. 32. Tool AI High-Stakes Autonomy Near-Term Risks ? Technological Unemployment
  33. 33. Some people are very afraid December 2, 2014 October 27, 2014
  34. 34. AI Misconceptions Intelligence is Not a Threshold Phenomenon  Progress in AI is the accumulation of thousands of incremental improvements  Robots will not “wake up” one day and be “truly intelligent” or “superintelligent” or “conscious” or “sentient”  “Tool AI” systems are already smarter than people along many dimensions
  35. 35. AI Misconceptions Autonomy Will Not Happen Spontaneously  There is no threshold above which AI systems suddenly have free will  Systems need to be designed and built to be autonomous  They must be given access to resources (money, power, materials, generalized task markets, communications with people)
  36. 36. The danger of “Autonomous AI” is not “AI” but “Autonomy” An autonomous system can be dangerous for many reasons:  It could consume vast resources  It could injure or kill people  It could apply AI to help it do these things
  37. 37. How Can We Maintain Control of Autonomous Systems? Case 1: Decisions are made at human time scales Examples:  Aircraft autopilot  Most factory robots  Surgical robots Humans can monitor and respond to problems  Similar to supervision in a human organization
  38. 38. Case 2: Very High Speed Decision Making Examples:  stock market  power grid  self-driving cars  ≥18,520 market crashes and spikes have been detected 2006-2011 below the 950ms level Flash Crash (10.5.2010)
  39. 39. Automated Monitoring  Signals might predict or detect flash crashes  Example: Inventory of High- Speed Traders Normal market Flash crash Source: Vuorenmaa, Tommi; Wang, Liang, 2013
  40. 40. When Monitoring Detects A Problem: Then What? Stock market:  Halt trading  Unwind transactions Self-driving car:  Slow down and pull over in a safe spot Smart Electric Grid:  ??
  41. 41. We Should Never Create a Fully Independent Autonomous System By definition, a fully independent autonomous system is a system over which we have no control
  42. 42. Summary  AI has been making steady progress  Exciting applications of Tool AI are coming soon  Potential applications of Autonomous AI are being proposed  Many of these involve high-stakes decision making  There are many risks that must be addressed before it will be safe to field such systems  Information technology—including AI—may be contributing to unemployment but the picture is far from clear  Tool AI will not spontaneously become autonomous  We must find ways to control autonomous AI systems

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