Artificial Intelligence and Nature .

1. mostly... Artificial Intelligenceand Nature . and a few musings on... (the) (of Intelligence) Professor Marie desJardinsmariedj@cs.umbc.eduHonors ForumMonday, September 18, 2006

2. Overview • What is AI? (and why is it so cool?) • AI: Past and Present • History of AI • AI Today • The Skeptics Speak • Complexity and Simplicity

3. What is AI?

4. AI: A Vision • Could an intelligent agent living on your home computer manage your email, coordinate your work and social activities, help plan your vacations…… even watch your house while you take those well planned vacations?

5. Main Goals of AI • Represent knowledge • Reason about knowledge • Behave intelligently in complex environments • Develop interesting and useful applications • Interact with people, agents, and the environment

6. Foundations of AI Computer Science & Engineering Mathematics Philosophy AI Economics Biology Psychology Linguistics Cognitive Science

7. Big Questions • Can machines think? • If so, how? • If not, why not? • What does this say about human beings? • What does this say about the mind? • And if we can make machines think, should we?

8. History of AI

9. History

10. Early AI: Eliza • ELIZA: A program that simulated a psychotherapist interacting with a patient and successfully passed the Turing Test • Implemented at MIT during 1964-1966 by Joel Weizenbaum • First script was DOCTOR • Simple pattern-matching • Each pattern had an associated reply which might include bits of the input (after simple transformations (my  your) • Weizenbaum was shocked at reactions • Psychiatrists thought it had potential • People unequivocally anthropomorphized • Let me tell you my problems... • Many thought it solved the natural language problem!!

11. Eliza Speaks • I am the psychotherapist. Please, describe your problems. • i'm having a hard time with my students • Why do you say that? • well, they keep asking me for money • Maybe your plans have something to do with this. • what plans? • Is it because of your life that you say what plans? • No, it's because I didn't know what you were talking about • Is it because you didn't know what I was talking about that you came to me? • no, it's because i wanted to see if you could pass the turing test • Does it bother you that you wanted to see if I could really pass the turing test? • ….. • How do you reconcile problems at school? • This is a waste of time. Good bye • My secretary will send you a bill.

12. Early AI: Parry • Kenneth Colby modeled a paranoid using the same techniques as Eliza, around 1968 • PARRY has basic emotions. If it gets angry, its replies become more hostile • In a Turing test, expert psychiatrists were unable to distinguish PARRY’s ramblings from those of real paranoids

13. PARRY Speaks • Good afternoon. My name is Doctor Jones. • Pleased to meet you. • How are you? • All right I guess. • Could you tell me your name? • Pat Smith. • I though you might have some problems. • I've been upset lately. • By what? • People get on my nerves sometimes. • Tell me more. • Do you know anything about bookies? • ...

14. Please tell me *this* isn't AI! P: I don't understand your motives. E: Why don't you understand my motives? P: Shouldn't I be? E: Why do you say that just now? P: That's just the way it is. E: Suppose that's the way it were not. P: So? E: I am not sure I understand you fully. P: You harp on the same questions too much. E: What does that similarity suggest to you? P: I don't get you. E: Do you wish that you did get I?

15. Turing Test • Three rooms contain a person, a computer, and an interrogator • The interrogator can communicate with the other two by teleprinter • The interrogator tries to determine which is the person and which is the machine • The machine tries to fool the interrogator into believing that it is the person • If the machine succeeds, then we conclude that the machine can think

16. The Loebner Contest • A modern version of the Turing Test, held annually, with a $100,000 cash prize • Hugh Loebner was once director of UMBC’s Academic Computing Services (née UCS, lately OIT) • http://www.loebner.net/Prizef/loebner-prize.html • Participants include a set of humans, a set of computers, and a set of judges • Scoring • Rank from least human to most human • Highest median rank wins $2000 • If better than a human, win $100,000 (Nobody yet…)

17. What’s Easy and What’s Hard? • It’s been easier to mechanize many of the high-level tasks we usually associate with “intelligence” in people • e.g., symbolic integration, proving theorems, playing chess, medical diagnosis • It’s been very hard to mechanize tasks that lots of animals can do • walking around without running into things • catching prey and avoiding predators • interpreting complex sensory information (e.g., visual, aural, …) • modeling the internal states of other animals from their behavior • working as a team (e.g., with pack animals) • Is there a fundamental difference between the two categories?

18. AI Today

19. Who Does AI? • Academic researchers (perhaps the most Ph.D.-generating area of computer science in recent years) • Some of the top AI schools: CMU, Stanford, Berkeley, MIT, UIUC, UMd, U Alberta, UT Austin, ... (and, of course, UMBC!) • Government and private research labs • NASA, NRL, NIST, IBM, AT&T, SRI, ISI, MERL, ... • Lots of companies! • Google, Microsoft, Honeywell, Teknowledge, SAIC, MITRE, Fujitsu, Global InfoTek, BodyMedia, ...

20. Applications • A sample from the 2006 International Conference on Innovative Applications of AI: • Customer support for servicing consumer appliances • Prediction of electricity distribution failures • Machine translation for auto assembly instructions • Electronic commerce for supply chain management • Medical ethics advising • School redistricting (yours truly) • Food safety monitoring • Personalized route planning • Expressive music performance

21. Robotics • SRI: Shakey / planning sri-Shakey.ram • SRI: Flakey / planning & control sri-Flakey • UMass: Thing / learning & controlumass_thing_irreg.mpegumass_thing_quest.mpegumass-can-roll.mpeg • MIT: Cog / reactive behaviormit-cog-saw-30.movmit-cog-drum-close-15.mov • MIT: Kismet / affect & interactionmit-kismet.movmit-kismet-expressions-dl.mov • CMU: RoboCup Soccer / teamwork & coordinationcmu_vs_gatech.mpeg

22. DARPA Grand Challenge • Completely autonomous vehicles (no human guidance) • Several hundred miles over varied terrain • First challenge (2004) – 142 miles • “winner” traveled seven(!) miles • Second challenge (2005) – 131 miles • Winning team (Stanford) completed the course in under 7 hours • Three other teams completed the course in just over 7 hours • Onwards and upwards (2007) • Urban Challenge • Traffic laws, merging, trafficcircles, busy intersections...

23. Art: NEvAr • Use genetic algorithms to evolve aesthetically interesting pictures • See http://eden.dei.uc.pt/~machado/NEvAr

24. ALife: Evolutionary Optimization • MERL: evolving ‘bots

25. Human-Computer Interaction: Sketching • Step 1: Typing • Step 2: Constrained handwriting • Step 3: Handwriting recognition • Step 4: Sketch recognition (doodling)! • MIT sketch tablet

26. Driving: Adaptive Cruise Control • Adaptive cruise control and pre-crash safety system (ACC/PCS) • Offered on high-end LS430 model for £2,100 • Determines appropriate speed for traffic conditions • Senses impending collisions and reacts (brakes, seatbelts)

27. AxonX • Smoke and fire monitoring system

28. Rocket Review • Automated SAT grading system

29. What Can AI Systems Do Now? Here are some example applications: • Computer vision: face recognition from a large set • Robotics: autonomous (mostly) automobile • Natural language processing: simple machine translation • Expert systems: medical diagnosis in a narrow domain • Spoken language systems: ~1000 word continuous speech • Planning and scheduling: Hubble Telescope experiments • Learning: text categorization into ~1000 topics • User modeling: Bayesian reasoning in Windows help (the infamous paper clip…) • Games: Grand Master level in chess (world champion), checkers, etc.

30. What Can’t AI Systems Do (Yet)? • Understand natural language robustly (e.g., read and understand articles in a newspaper) • Surf the web (or a wave) • Interpret an arbitrary visual scene • Learn a natural language • Play Go well • Construct plans in dynamic real-time domains • Refocus attention in complex environments • Perform life-long learning Exhibit true autonomy and intelligence!

31. The Skeptics Speak

32. Mind and Consciousness • Many philosophers have wrestled with the question: • Is Artificial Intelligence possible? • John Searle: most famous AI skeptic • Chinese Room argument • Is this really intelligence? ? !

33. What Searle Argues • People have beliefs; computers and machines don’t. • People have “intentionality”; computers and machines don’t. • Brains have “causal properties”; computers and machines don’t. • Brains have a particular biological and chemical structure; computers and machines don’t. • (Philosophers can make claims like “People have intentionality” without ever really saying what “intentionality” is, except (in effect) “the stuff that people have and computers don’t.”)

34. Let’s Introspect For a Moment... • Have you ever learned something by rote that you didn’t really understand? • Were you able to get a good grade on an essay where you didn’t really know what you were talking about? • Have you ever convinced somebody you know a lot about something you really don’t? • Are you a Chinese room?? • What does “understanding” really mean? • What is intentionality? Are human beings the only entities that can ever have it? • What is consciousness? Why do we have it and other animals and inanimate objects don’t? (Or do they?)

35. Complexity and Simplicity Is Nature simple or complex? Is Intelligence complex or simple? Is Artificial Intelligence possible??

36. Just You Wait... Give us another 10 years! or 20... or 30... or 50...

37. Thank You! Any Questions?