1. WHY I AM OPTIMISTIC Patrick H. Winston MIT Artificial Intelligence Laboratory

2. The salients Applications side: We have already won Science side: We are bound to win

3. The Applications

4. We have expanded our frontiers Lots of people Lots of good

5. A little good for a lot of people

7. A lot of good for a few people

8. A lot of good for a lot of people

9. We have exemplars of all kinds Large software companies Large entertainment companies Companies with huge IPOs Multidimensional multinationals A multitude of small companies

10. We were a one-horse field Rule chaining Inheritance

11. Now we ride many horses Rule chaining Generate and test Search Tree building Agents Neural nets Constraint propagation Inheritance Genetic algorithms Bayes nets Learning

12. And not just reasoning horses Vision Language and speech Infrastructure

15. We had a Pyrrhic victory IO Memory Cables Power Tapes Disk Network

16. We learned negative lessons Nobody cares about saving money Using cutting edge technology To replace expensive experts

17. We learned positive lessons Everybody cares about New revenues Saving a mountain of money Increasing competitiveness

18. We changed the business model Replaces Expensive People Saves Mountains Of Money Creates New Revenue Ferrets Blunder stoppers Novices Experts

19. The critic and the billionaire

20. What’s next: connections People Global Net Physical World Computers Enhanced Reality Intelligent Structures Useful robots Human Computer Interaction Information Access

21. The click-in phenomenon The fax machine The world wide web

22. The Science

23. Shrobe’s point Applications drive science Unless they all look alike

24. Atkeson’s point We could move to the center But, we might be kidding ourselves

25. My point AI is applied computer science Much energy wonderfully used But consequently diverted

26. A 100 year enterprise Molecular Biology Artificial Intelligence 1950200020501900

27. Why we are the way we are Powerful Ideas Models of Thinking Reflection…Biology…Psychology Turing Minsky

28. The Intelligent Reasoner LanguageVision Input/Output Channels The standard paradigm

29. The dawn age

30. What went wrong? We think with our eyes We think with our mouths We think with our hands Each faculty helps the others

31. What is the evidence? Armchair psychology Clues from the brain

32. Armchair psychology Hillis’s observation on the value of talking to yourself Everyone’s observation on the value of drawing a sketch.

33. From brain scanning

34. Intelligence is in the I/O The Explanation Motor Reasoner Linguistic Reasoner Visual Reasoner

35. Language is first among equals Language reasons Language tells vision how to see Language tells motor how to act

36. Is it time to start over? An I/O oriented paradigm Essentially free computation Important, inspiring allies

37. From brain rewiring

38. From watching infants

39. Is it time to start over? An I/O oriented paradigm Essentially free computation Important, inspiring allies Accumulation of powerful ideas

40. Six powerful ideas Recreated condition One-shot learning Memory is cheap Change matters Survival of the smallest Bi-directional search

41. Recreated condition: Minsky P / \ P P / \ P P / \ / \ P P P P / \ / \ / \ / \ P P P P P P P P / \ / \ / \ / \ / \ / \ / \ / \ P P P P P P P P *---------------------------------- | | K-line | *--------------> | *--------->

42. One-shot: Yip and Sussman aepl Time Word Memory Rule Memory

43. One-shot: Yip and Sussman aeplz Time Rule Memory Word Memory

44. One-shot: Yip and Sussman 100% Trials 500 Accuracy

45. Memory is cheap: Atkeson

46. Atkeson’s practice tables

47. Atkeson’s practice results One stored trajectory Feedback only Three stored trajectories

48. Change matters: Borchardt A D Appear Disappear Change Decrease Increase A D Appear Disappear Change Decrease Increase

49. Borchardt’s ladder diagrams DA DA AAA Distance Speed Contact T1T2T4T3

50. Survival of the smallest: Kirby

51. Kirby’s phase transitions Time Coverage

52. Bi-directional search: Ullman Model Image

53. Joyous inferences Powerful ideas Marvelous engineering Essential alliances

54. What about … Bayes and Markov Neural nets and connectionism Logic

55. WHAT WE MUST NOT DO

56. Loose our faith It will take 300 years All the low hanging fruit is gone We shouldn’t make predictions

57. Waste time arguing Is it possible? Is it successful? Is it really AI?

58. Squander our capital One thousand people 10% interested in the science side 10% actually working on it 10% of the time

59. WHAT WE SHOULD DO

60. Human Intelligence Enterprise Vision, language, motor Free hardware Clues from the brain Powerful ideas Conceive and test models The Human Intelligence Enterprise Visual Reasoner Linguistic Reasoner Motor Reasoner

61. Why we should do it It can only be done once Revolutionary applications The Human Intelligence Enterprise Visual Reasoner Linguistic Reasoner Motor Reasoner

62. What we should ask Why do we have discrete words? What do our inner agents say? How do they learn what to say? Do we see what chimps see? How did our faculties evolve? Why can’t we all play the piano? The Human Intelligence Enterprise Visual Reasoner Linguistic Reasoner Motor Reasoner

63. So, here is why I’m optimistic Nothing could possibly be more fun, exciting, rewarding, and glorious, than … Applications that really matter Figuring out our own intelligence The Human Intelligence Enterprise Visual Reasoner Linguistic Reasoner Motor Reasoner