1. The Foundations of Artificial Intelligence

3. Our Working Definition of AI Artificial intelligence is the study of how to make computers do things that people are better at or would be better at if: they could extend what they do to a World Wide Web-sized amount of data and not make mistakes.

4. Why AI? "AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind." - Herb Simon

5. A Time Line View the time linetime line

6. The Dartmouth Conference and the Name Artificial Intelligence J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon. August 31, 1955. "We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

7. The Origins of AI Hype 1950 Turing predicted that in about fifty years "an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning". 1957 Newell and Simon predicted that "Within ten years a computer will be the world's chess champion, unless the rules bar it from competition."

8. Symbolic vs. Subsymbolic AI Subsymbolic AI: Model intelligence at a level similar to the neuron. Let such things as knowledge and planning emerge. Symbolic AI: Model such things as knowledge and planning in data structures that make sense to the programmers that build them. (blueberry (isa fruit) (shape round) (color purple) (size.4 inch))

9. The Origins of Subsymbolic AI 1943 McCulloch and Pitts A Logical Calculus of the Ideas Immanent in Nervous Activity “Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic”

10. The Origins of Symbolic AI Games Theorem proving

11. Knowledge Acquisition Hand CraftedMachine Learning Symbolic Subsymbolic

12. What Are the Components of Intelligence?

13. Image Perception 424d961d0300000000003e00000028000000b2030000a50600000100010000 000000581d0300232e0000232e0000020000000000000000000000ffffff00e0a 288208a38a388a08a00a2880080380288200a38a0082080380380a00a00a28 8038a380380000000a00a0080380280a00a00a008008a380280a00a00a0000 000000000a00a00a0000000000000000000a0000380380380eb8e00e380e80 e38e38abf8e00e38aab8e380380a80a38a388abfe3fffffc000e1c71c71c775c71 c71c701c71c01c074071c700775e01c71c0740700700701c71c01c774070000 0001e01c01c0740700700701e01c01c7740700700701e00000000000007007 01e0000000000000000001e00001c074070071c701c700700775c71c7fc701c 71c71c7740700700775c71c71ff7fffffc000e0820820822082082082008208008 0200208200220a008208020020020020082080082200200000000a00800802 00200200200a0080082200200200200a0000000000000200200a0000000000 000000000a0000080200200208200020020022002082a02008208208220020 0200220820820aa2aaaabc000e11110111011111011110111100100100110101 1111010110010010010110111140111100100000001501001001001001011011 010011110010010110110000000000000101101100000000000000000011000 010010010011000001001010100110150001011011001001001010100110111 0151511c000e00000000000000000000000000000000000000000000000000 00000000000000000000000000000000000000000000000000000000000000 00000000000000000000000000000000000000000000000000000000000000 000000000000000000000000000000

14. Image Perception

15. But We’re Still Ahead http://www.captcha.net/

16. But We’re Still Ahead

18. Reasoning We can describe reasoning as search in a space of possible situations.

19. Recall the 8-Puzzle What are the states? http://www.javaonthebrain.com/java/puzz15/ Start state Goal state

20. Hotel Maid States: Start state: Operators: Goal state:

21. The British Museum Algorithm A simple algorithm: Generate and test But suppose that each time we end a path, we start over at the top and choose the next path randomly. If we try this long enough, we may eventually hit a solution. We’ll call this The British Museum Algorithm or The Monkeys and Typewriters Algorithm http://www.arn.org/docs2/news/monkeysandtypewriters051103.htm

22. Branch and Bound Consider the problem of planning a ski vacation. Fly to A $600Fly to B $800Fly to C $2000 Stay D $200 (800) Stay E $250 (850) Total cost (1200)

23. Problem Reduction Goal: Acquire TV Steal TVEarn MoneyBuy TV Or another one: Theorem proving in which we reason backwards from the theorem we’re trying to prove.

24. What is a Heuristic?

25. Example From the initial state, move A to the table. Three choices for what to do next. A local heuristic function: Add one point for every block that is resting on the thing it is supposed to be resting on. Subtract one point for every block that is sitting on the wrong thing.

26. A New Heuristic From the initial state, move A to the table. Three choices for what to do next. A global heuristic function: For each block that has the correct support structure (i. e., the complete structure underneath it is exactly as it should be), add one point for every block in the support structure. For each block that has an incorrect support structure, subtract one point for every block in the existing support structure.

27. Hill Climbing – Another Example Problem: You have just arrived in Washington, D.C. You’re in your car, trying to get downtown to the Washington Monument.

28. Hill Climbing – Some Problems

29. Hill Climbing – Is Close Good Enough? A B Is A good enough? Choose winning lottery numbers

30. Hill Climbing – Is Close Good Enough? A B Is A good enough? Choose winning lottery numbers Get the cheapest travel itinerary Clean the house

31. The Silver Bullet? Is there an “intelligence algorithm”? 1957GPS (General Problem Solver) Start Goal

32. The Silver Bullet? Is there an “intelligence algorithm”? 1957GPS (General Problem Solver) Start Goal What we think now: Probably not

33. But What About Knowledge? Why do we need it? How can we represent it and use it? How can we acquire it? Find me stuff about dogs who save people’s lives.

34. But What About Knowledge? Why do we need it? How can we represent it and use it? How can we acquire it? Find me stuff about dogs who save people’s lives. Two beagles spot a fire. Their barking alerts neighbors, who call 911.

35. Expert Systems Expert knowledge in many domains can be captured as rules. Dendral (1965 – 1975) If: The spectrum for the molecule has two peaks at masses x 1 and x 2 such that: x 1 + x 2 = molecular weight + 28, x 1 -28 is a high peak, x 2 – 28 is a high peak, and at least one of x 1 or x 2 is high, Then: the molecule contains a ketone group.

36. To Interpret the Rule Mass spectometry Ketone group:

37. Expert Systems in Medicine 1975Mycin attached probability-like numbers to rules: If: (1) the stain of the organism is gram-positive, and (2) the morphology of the organism is coccus, and (3) the growth conformation of the organism is clumps Then: there is suggestive evidence (0.7) that the identity of the organism is stphylococcus.

38. Watson How does Watson win? http://www.youtube.com/watch?v=d_yXV22O6n4http://www.youtube.com/watch?v=d_yXV22O6n4 Watch a sample round: http://www.youtube.com/watch?v=WFR3lOm_xhEhttp://www.youtube.com/watch?v=WFR3lOm_xhE From Day 1 of the real match: http://www.youtube.com/watch?v=seNkjYyG3gIhttp://www.youtube.com/watch?v=seNkjYyG3gI Introduction: http://www.youtube.com/watch?v=FC3IryWr4c8http://www.youtube.com/watch?v=FC3IryWr4c8 IBM’s site: http://www-03.ibm.com/innovation/us/watson/what-is-watson/index.htmlhttp://www-03.ibm.com/innovation/us/watson/what-is-watson/index.html Bad Final Jeopardy: http://www.youtube.com/watch?v=mwkoabTl3vM&feature=relmfu http://www.youtube.com/watch?v=mwkoabTl3vM&feature=relmfu Explanation: http://thenumerati.net/?postID=726http://thenumerati.net/?postID=726

39. Expert Systems – Today: Medicine Expert systems work in all these areas: arrhythmia recognition from electrocardiograms coronary heart disease risk group detection monitoring the prescription of restricted use antibiotics early melanoma diagnosis gene expression data analysis of human lymphoma breast cancer diagnosis

40. Dr. Watson http://www.wired.com/wiredscience/2012/10/watson-for-medicine/ A machine like that is like 500,000 of me sitting at Google and Pubmed.

41. But What About Things That All of Us Know?