1. The Future of Artificial Intelligence John Paxton Montana State University August 14, 2003

2. Bannack

3. What makes AI difficult? Different problems have inherently different complexities to solve.

4. The Sorting Problem Input: 2 4 6 7 5 3 1 Output: 1 2 3 4 5 6 7

5. Selection Sort Step 1: 2 4 6 7 5 3 1 Step 2: 2 4 6 1 5 3 7 Step 3: 2 4 3 1 5 6 7 Step 4: 2 4 3 1 5 6 7 Step 5: 2 1 3 4 5 6 7 Step 6: 2 1 3 4 5 6 7 Step 7: 1 2 3 4 5 6 7

6. Selection Sort If there are n items to sort, selection sort takes O(n 2 ) time What does this mean? If we double the size of the input, we can expect the algorithm to take four times as long.

7. Quicksort O(n log 2 n) 2 4 6 7 5 3 1 1 4 6 7 5 3 3 6 7 5 5 7

8. Quicksort nn log 2 nn2n2 1033.22100 66.4410000 100099.661,000,000 10000132.88100,000,000

9. Sorting It can be proven that sorting n numbers based on comparisons has a best case of O(n log n). Thus, the inherent complexity of sorting is O(n log n), even though worse algorithms such as selection sort exist.

10. The Class P P = Polynomial Any problem whose inherent complexity is O(n p ) where p is a constant is in the class P. Problems that are in P typically are practical to solve on computers.

11. Travelling Salesperson Problem Starting in City A, what is the shortest circuit that visits cities B, C, and D? A – B – C – D – A A – B – D – C – A A – C – B – D – A A – C – D – B – A A – D – B – C – A A – D – C – B - A

12. TSP In the preceding problem, there were 4 cities and 3! possible solutions In general, if there are n cities, one must consider (n-1)! possibilities. (n-1)! is not O(n p ) for any fixed p. (n-1)! is in the EXP class. Each problem in the EXP class is O(p n ) for some fixed p.

13. Comparison nn2n2 (n-1)! 52524 10100362,880 152258.7E10 204001.2E17

14. The Class EXP As you can see from the preceding table, problems that are in the class EXP do not have practical solutions on computers

15. Relevance to AI Unfortunately, many interesting problems in AI are in the class EXP. For example, the TSP problem.

16. Satisficing What can be done? Instead of settling for the optimal answer, look for a “pretty good” solution instead. This technique is also known as satisficing.

17. Satisficing Example

18. Heuristics A “heuristic” is a rule-of-thumb that works in practice, but has no guarantee of being optimal.

19. Water Jug Problem Place 6 liters of water in the 8 gallon jug in as few steps as possible 8 3

20. Water Jug Problem Place 8 liters of water in the 10 gallon jug in as few steps as possible 10 4

21. Water Jug Problem Place 10 liters of water in the 15 gallon jug in as few steps as possible 15 5

22. Past AI Predictions Game Playing. Researchers thought that AI chess playing programs would beat the best humans by 1970. Machine Translation. –The spirit is willing, but the flesh is weak. –The whisky is strong, but the meat is rotten.

23. Objections to AI Theology Heads-in-the-Sand Mathematical Self Awareness Capability X is lacking (e.g. enjoy ice cream) Lady Lovelace’s objection Continuity of nervous system Informality of behavior (no rules) ESP

24. The Future Consumer Robots

25. The Future Gastrobots (University of South Florida) Sustain themselves by eating naturally occurring foods

26. The Future COG, a robot at MIT Track eye movement Recognize faces Grab objects Hear a rhythm, play it back on drums

27. The Future Art – Raymond Kurzweil’s screensaver program, Aaron Poetry Music

28. The Future Natural Language Charles Schwab incorporates iPhrase at its web site to allow users to use natural language to ask questions. For example, “Which of these stocks has the highest revenues?”

29. The Future Products that do one thing well. For example, Continental Divide Robotics has developed a system based on GPS that can locate any person or any object anywhere in the world and notify a user if it is “out of bounds”. This could help a parent monitor a child, for example.

30. The Future Companionship At Microsoft, a product is under development that learns about you. Who is important to you? Are you busy? The product can then monitor incoming e-mails and phone calls.

31. The Future Virtual Reality Haptek, People Putty Create your own 3-D interactive characters

32. The Future Computers will get faster Software will get better AI will creep closer to human capabilities (search, learning, knowledge representation)

33. The Future There are lots of potential benefits! There are certainly some potential drawbacks! Most AI researchers believe humans will stay in control

34. Questions?