2. Artificial Intelligence: Human vs. Machine Professor Marie desJardins CMSC 100 November 5, 2009

3. 2 Memory is at the Core (Literally)  Remember Hal?  “Open the pod bay door, Hal.”  “My mind is going...”  Memory is at the core of our being (and a computer’s) ...but our memories look very different! The first magnetic core memory [www.columbia.edu/acis/history] thebrain.mcgill.ca

4. 3 Overview  What is AI? (and why is it so cool?)  AI: Past and Present  History of AI  AI Today  Computational vs. Biological Memory  The Skeptics Speak

5. What is AI?

6. 5 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?

7. 6 RR epresent and store knowledge RR etrieve and reason about knowledge BB ehave intelligently in complex environments DD evelop interesting and useful applications II nteract with people, agents, and the environment Main Goals of AI

8. 7 Computer Science & Engineering AI Mathematics Cognitive Science Philosophy PsychologyLinguistics Biology Economics Foundations of AI

9. 8 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?

10. 9 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., memory, learning, language, visual perception, 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.

11. History of AI

12. 11 History

13. 12  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

14. 13 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.

15. 14 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

16. 15 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? ...

17. 16 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?

18. 17 Turing Test  Three rooms contain a person, a computer, and an interrogator  The interrogator can communicate with the other two by “teleprinter” (or, say, AIM)  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

19. 18 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 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…)  2008 winner: ElbotElbot

20. 19 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?

21. AI Today

22. 21 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!

23. 22  A sample from the 2008 International Conference on Innovative Applications of AI:  Event management (for Olympic equestrian competition)  Language and culture instruction  Public school choice (for parents)  Turbulence prediction (for air traffic safety)  Heart wall abnormality diagnosis  Epilepsy treatment planning  Personalization of telecommunications services  Earth observation flight planning (for science data)  Crop selection (for optimal soil planning) Applications

24. 23 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: ~2000 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, backgammon, etc. Breaking news (8/7/08) - MoGo beats professional Go playerMoGo beats professional Go player What Can AI Systems Do Now?

25. 24 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

26. 25 DARPA Grand Challenge  Completely autonomous vehicles (no human guidance)  Several hundred miles over varied terrainvaried 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, traffic circles, busy intersections...  Six finishers (best time: 2.8 miles in 4+ hours)

27. 26 Art: NEvAr  Use genetic algorithms to evolve aesthetically interesting pictures  See http://eden.dei.uc.pt/~machado/NEvArhttp://eden.dei.uc.pt/~machado/NEvAr

28. 27 ALife: Evolutionary Optimization  MERL: evolving ‘bots

29. 28 Human-Computer Interaction: Sketching  Step 1: Typing  Step 2: Constrained handwriting  Step 3: Handwriting recognition  Step 4: Sketch recognition (doodling)!  MIT sketch tablet

30. 29 Driving: Adaptive Cruise Control  Adaptive cruise control and pre- crash safety system (ACC/PCS)  Offered by dozens of makers, mostly as an option (~$1500) on high-end models  Determines appropriate speed for traffic conditions  Senses impending collisions and reacts (brakes, seatbelts)  Latest AI technology: automatic parallel parking!

31. 30 AxonX  Smoke and fire monitoring system

32. 31 Rocket Review  Automated SAT essay grading system

33. 32 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

34. Computational vs. Biological Memory

35. 34 How Does It Work? (Humans)  Basic idea:  Chemical traces in the neurons of the brain  Types of memory:  Primary (short-term)  Secondary (long-term)  Factors in memory quality:  Distractions  Emotional cues  Repetition

36. 35 How Does It Work? (Computers)  Basic idea:  Store information as “bits” using physical processes (stable electronic states, capacitors, magnetic polarity,...)  One bit = “yes or no”  Types of computer storage:  Primary storage (RAM or just “memory”)  Secondary storage (hard disks)  Tertiary storage (optical jukeboxes)  Off-line storage (flash drives)  Factors in memory quality:  Power source (for RAM)  Avoiding extreme temperatures Size Speed

37. 36 Measuring Memory  Remember that one yes/no “bit” is the basic unit  Eight (2 3 ) bits = one byte  1,024 (2 10 ) bytes = one kilobyte (1K) *  1,024K (2 20 bytes) = one megabyte (1M)  1,024K (2 30 bytes) = one gigabyte (1G)  1,024 (2 40 bytes) = one terabyte (1T)  1,024 (2 50 bytes) = one petabyte (1P) ... 2 80 bytes = one yottabyte (1Y?) * Note that external storage is usually measured in decimal rather than binary (1000 bytes = 1K, and so on)

38. 37 What Was It Like Then?  The PDP-11/70s we used in college had 64K of RAM, with hard disks that held less than 1M of memory ... and we had to walk five miles, uphill, in the snow, every day! And we had to live in a cardboard box in the middle of the road!

39. 38 What Is It Like Now?  The PDP-11/70s we used in college had 64K of RAM, with hard disks that held less than 1M of memory  The cheapest Dell Inspiron laptop has 2G of RAM and up to 80G of hard drive storage.... ...a factor of 10 18 more RAM and 10 12 more disk space ...and your iPod nano has 8G of blindingly fast storage ...so don’t come whining to me about how slow your computer is!

40. 39 Moore’s Law  Computer memory (and processing speed, resolution, and just about everything else) increases exponentially

41. 40 Showdown  Computer capacity:  Primary storage: 64GB  Secondary storage: 750GB (~10 12 )  Tertiary storage: 1PB? (10 15 )  Computer retrieval speed:  Primary: 10 -7 sec.  Secondary: 10 -5 sec.  Computing capacity: 1 petaflop (10 15 floating-point instructions per second), very special purpose  Digital  Extremely reliable  Not (usually) parallel  Human capacity:  Primary storage: 7 ± 2 “chunks”  Secondary storage: 10 8432 bits?? (or maybe 10 9 bits?)  Human retrieval speed:  Primary: 10 -2 sec  Secondary: 10 -2 sec  Computing capacity: possibly 100 petaflops, very general purpose  Analog  Moderately reliable  Highly parallel More at movementarian.com

42. 41 It’s Not Just What You “Know”  Storage  Indexing  Retrieval  Inference  Semantics  Synthesis ...So far, computers are good at storage, OK at indexing and retrieval, and humans win on pretty much all of the other dimensions ...but we’re just getting started  Electronic computers were only invented 60 years ago!  Homo sapiens has had a few hundred thousand years to evolve...

43. The Skeptics Speak

44. 43 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? ? !

45. 44 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.”)

46. 45 Let’s Introspect For a Moment... HH ave you ever learned something by rote that you didn’t really understand? WW ere you able to get a good grade on an essay where you didn’t really know what you were talking about? HH ave you ever convinced somebody you know a lot about something you really don’t? AA re you a Chinese room?? WW hat does “understanding” really mean? WW hat is intentionality? Are human beings the only entities that can ever have it? WW hat is consciousness? Why do we have it and other animals and inanimate objects don’t? (Or do they?)

47. 46 Just You Wait...

48. 47 Thank You!