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ARTIFICIAL INTELLIGENCE: THE MAIN IDEAS. OLLI COURSE SCI 102 Tuesdays, 11:00 a.m. – 12:30 p.m. Winter Quarter, 2013 Higher Education Center, Medford Room 226. Nils J. Nilsson. http:// /. Course Web Page:

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    1. ARTIFICIAL INTELLIGENCE:THE MAIN IDEAS OLLI COURSE SCI 102 Tuesdays, 11:00 a.m. – 12:30 p.m. Winter Quarter, 2013 Higher Education Center, Medford Room 226 Nils J. Nilsson Course Web Page: For Information about parking near the HEC, go to: There are links on that page to parking rules and maps

    2. AI in the News?

    3. PART ONE(Continued)REACTIVE AGENTS Perception Action Selection Memory

    4. Summary:Neural Networks Have Many Applications

    5. But Some Are Not Very User-Friendly Fair Isaac Experience

    6. Models of the Cortex Using Deep, Hierarchical Neural Networks All connections are bi-directional

    7. The Neocortex

    8. Two Pioneers in Using Networks to Model the Cortex Hierarchical Temporal Memory Jeff Hawkins Geoffrey Hinton

    9. More About Jeff Hawkins’s Ideas

    10. Dileep George’s Hierarchical Temporal Memory (HTM) Model A “Convolutional” Network George is a founder of startup, Vicarious

    11. A “Mini-Column” of the Neo-Cortex From: “HIERARCHICAL TEMPORAL MEMORY”

    12. Figure 10. Columnar organization of the microcircuit. George, Dileepand Hawkins, Jeff: (2009) Towards a Mathematical Theory of Cortical Micro-circuits. PLoSComputBiol 5(10): e1000532. doi:10.1371/journal.pcbi.1000532

    13. Figure 9. A laminar biological instantiation of the Bayesian belief propagation equations used in the HTM nodes. George D, Hawkins J (2009) Towards a Mathematical Theory of Cortical Micro-circuits. PLoS Comput Biol 5(10): e1000532. doi:10.1371/journal.pcbi.1000532

    14. Ray Kurzweil’s New Book

    15. Unsupervised Learning

    16. Letting Networks “Adapt” to Their Inputs All connections are bi-directional Massive number of inputs Weight Values Become Those For Extracting “Features” of Inputs HonglakLee,et al., “Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations,” Proceedings of the 26th Annual International Conference on Machine Learning, 2009

    17. Hubel & Wiesel’s “Detector Neurons” David Hubel, Torsten Wiesel Short bar of light projected onto a cat’s retina Response of a single neuron in the cat’s visual cortex (as detected by a micro-electrode in the anaesthetized cat)

    18. Use of Deep Networks With Unsupervised Learning All connections are bi-directional First Layer Learns “Building-Block” Features Common to Many Images

    19. Second Layer Learns Features Common Just to Cars, Faces, Motorbikesand Airplanes cars, faces, motorbikes, airplanes

    20. Third Layer Learns How to Combine the Features of the Second Layer Into aRepresentation of the Input cars, faces, motorbikes, airplanes

    21. Output Layer Can be Used to Make a Decision CAR

    22. The Net Can Make Predictions About Unseen Parts of the Input

    23. “Building High-level Features Using Large Scale Unsupervised Learning” Quoc V. Lee,et al. (Google and Stanford) 1,000 Google Computers, 1,000,000,000 Connections

    24. Large Scale Unsupervised Learning (Continued) Recognizes 22,000 object categories Unsupervised learning for three days 10 million 200x200 pixel images downloaded from the Internet (stills from YouTube) a “cat neuron” a “face neuron”

    25. One Result 81.7% accuracy in detecting faces out of 13,026 faces in a test set For more information about these experiments at Google/Stanford, see:

    26. Using Models (i.e., Memory) Can Make Agents Even More Intelligent Perception Action Selection Model of World (e.g., a map)

    27. Types of Models Maps Memory of Previous States List of State-Action Pairs

    28. Models can be pre-installed or learned

    29. Learning and Using Maps where am I? where is everything else? Neato Robot Vacuum

    30. Neato RoboticsMapping System


    32. Action Selection Perception S-R Rules Using “State” of the Agent determines the“state”of the world Library of States and Actions (Memory) IF state1, THEN actiona IF state2, THEN actionb . . .

    33. Lists of numbers, such as (1,7,3,4,6) Arrays, such as “Statements,” such as Color(Walls, LightBlue) Shape(Rectangular) . . . Ways to Represent States

    34. (1,7,3,4,6) a (1,6,2,8,7) b (4,5,1,8,5) c . . . (7,4,8,9,2) k Library of States & Actions (1,5,2,8,6) Input (present state) Closest Match

    35. Example: Face Recognition Using a large database containing many, many images of faces, a small set of “building-block” faces is computed: The average of all faces:

    36. Familiar Uses of “Building Blocks” A Musical Tone Consists of “Harmonics”

    37. Library of Known Faces (Represented as composites of the building-block faces) Sam Joe (2,2,-2,0,0,1,2,2,-1,2,2,-1,,0,2,0) (0,0,1,0,0,-2,-2,0,-1,-2,-2,-1,2,-1,0) Plus Thousands More Sue Mike (-3,2,1,1,-2,1,-2,3,0,0,0,-4,-3,2,-2) (4,1,3,-1,4,0,4,4,1,4,4,-4,4,-4,-4)

    38. Face Recognition Library of Known Faces Query Face • Represented as a composite of the building-block faces • (present state) (0,0,1,0,0,-2,-2,0,-1,-2,-2,-1,2,-1,0) Sam Joe Mike Sue (2,2,-2,0,0,1,2,2,-1,,2,2,-1,,0,2,0) (-3,2,1,1,-2,1,-2,3,0,0,0,-4,-3,2,-2) (-2,2,1,1,-2,1,-2,3,1,0,0,-4,-3,2,-2) (4,1,3,-1,4,0,4,4,1,4,4,-4,4,-4,-4) Sue is the Closest Match

    39. A table of states and actions and “values” Another Kind of Model

    40. Why have values for multiple actions instead of just noting the best action? Because the values in the table can be changed (learned) depending on experience! REINFORCEMENT LEARNING (Another Point of Contact with Brains)

    41. Pioneers in the Use of Reinforcement Learning in AI Andy Barto Rich Sutton Chris Watkins

    42. An Example:Learning a Maze

    43. But the Mouse Doesn’t Have a Map of the Maze (Like We Do)Instead it remembers the states it visits and assigns their actions random initial values

    44. It Can Change the Values in the TableThe First Step (state1, up) gets initial random value 3

    45. There is only one action possible (up), and the mouse ends up in state2 state2, has 3 actions, each with initial random values

    46. Now the mouse updates the value of (state1, up) in its table 5 value propagates backward (possibly with some loss)

    47. Sooner or later, the mouse stumbles into the goal and gets a “reward”

    48. The reward value is propagated backward 99 value propagates backward (with some loss)

    49. And So On . . .With a Lot of Exploration, the Mouse Learns the Maze