1. Artificial Intelligence: Fact or Fiction? Professor Marie desJardins mariedj@cs.umbc.edu UMBC Family Weekend Saturday, October 23, 2004 mariedj@cs.umbc.edu

2. Overview  What is AI? (and why is it so cool?)  AI: Past, Present, and Future  History of AI  AI Today  AI Tomorrow??

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

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

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?

8. Why AI?  Engineering: To get machines to do a wider variety of useful things  e.g., understand spoken natural language, recognize individual people in visual scenes, find the best travel plan for your vacation, etc.  Cognitive Science: As a way to understand how natural minds and mental phenomena work  e.g., visual perception, memory, learning, language, etc.  Philosophy: As a way to explore some basic and interesting (and important) philosophical questions  e.g., the mind body problem, what is consciousness, etc.

9. History of AI

10. History

11.  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!! Early AI: Eliza

12. 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.

13. 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

14. 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? ...

15. 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?

16. 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

17. 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)  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…)

18. 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?

19. AI Today

20. 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,...

21.  A sample from the 2003 International Conference on Innovative Applications of AI:  Scheduling train crews  Automated student essay evaluation  Packet scheduling in network routers  Broadcast news understanding  Vehicle diagnosis  Robot photography  Relational pattern matching Applications

22. Robotics  SRI: Shakey / planning sri-Shakey.ramsri-Shakey.ram  SRI: Flakey / planning & control sri-Flakeysri-Flakey  UMass: Thing / learning & control umass_thing_irreg.mpeg umass_thing_quest.mpeg umass-can-roll.mpeg umass_thing_irreg.mpeg umass_thing_quest.mpeg umass-can-roll.mpeg  MIT: Cog / reactive behavior mit-cog-saw-30.mov mit-cog-drum-close-15.mov mit-cog-saw-30.mov mit-cog-drum-close-15.mov  MIT: Kismet / affect & interaction mit-kismet.mov mit-kismet-expressions-dl.mov mit-kismet.mov mit-kismet-expressions-dl.mov  CMU: RoboCup Soccer / teamwork & coordination cmu_vs_gatech.mpeg cmu_vs_gatech.mpeg

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. Bioinformatics: Protein Folding  Human Genome Project  Intelligent drug design, genetic therapy  MERL: constraint-based approach

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

27. 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)

28. AxonX  Smoke and fire monitoring system

29. Rocket Review  Automated SAT grading system

30. AI Tomorrow(??)

31. Research in UMBC’s MAPLE Lab  Themes:  Integrated AI: Planning, learning, and interaction with other agents (human and machine)  Mixed-initiative (interactive) AI systems  Incorporating and modeling different types of knowledge Abstractions, qualitative models, background knowledge, preference functions, …  Organizational learning in multi-agent societies  Lifelong systems, embedded in real-world environments

32. KEDS: Knowledge-Enhanced Discovery System  NSF ITR funding  Focus: incorporate background knowledge into classification and clustering  Active clustering with “why” queries (*)  Incorporating background knowledge into spectral clustering  Classification with instance-space misclassification costs  Representing and learning preferences over sets  Cost-sensitive learning (*)  (*) Undergraduate research projects!

33. VisARD: Visualization for the Analysis of Relational Data  ARDA/NGA funding  AI research areas:  Semantically aware interactive graph layout (*)  Visualization of uncertainty  Visualization of time-varying data  Interactive PCA-based layout  (*) Undergraduate research project!

34. Other Research Projects  Stable team formation strategies and protocols  Agent positioning in networked multi-agent systems or mobile sensor networks  Bioinformatics (clustering of genomic data and supporting knowledge)  Interactive, multiattribute graph partitioning  Application domains: school/political redistricting, zoning and planning, resource positioning  Organizational learning in multi-agent systems

35. 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. What Can AI Systems Do Now?

36. 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

37. Just You Wait...

38. Thank You!