Artificial Intelligence: Human vs. Machine

1. Artificial Intelligence:Human vs. Machine Professor Adam Anthony CMSC 180 Fall 2010 Slides Provided by and Adapted From: Dr. Marie desJardins

2. BEFORE WE START! • DON’T FORGET ABOUT MINI-PROJECT 1! • Implementation: • Make sure that whatever you submit works, even if it is incomplete! • Test Report: • 2-3 pages verifying that you tested each feature of your program • How you tested it • How that test confirmed it was working correctly

3. Memory is at the Core (Literally) • 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. 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. Main Goals of AI • Represent and store knowledge • Retrieve and reason about knowledge • Behave intelligently in complex environments • Develop interesting and useful applications • Interact with people, agents, and the environment

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

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?

9. Architecture of an Intelligent Agent • An Intelligent Agent has: • Sensors for inputting data • Actuators for performing actions in the “world” • Robotic tools in the real world • Software routines in a virtual world • A level of Response: • Reflex-based: only does exactly what it’s told (remote-controlled) • Knowledge-based: able to follow semi-complex instructions • Baseball throwing example • Goal-based: No instructions, just a goal to achieve • Makes its own instruction set • What types of things can be considered agents??? • Skip to Philosophy Section

10. History of AI

11. History

12. 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!!

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.

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

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? • ...

16. 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?

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

18. The Loebner Contest • A modern version of the Turing Test, held annually, with a $100,000 cash prize • 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: Elbot

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?

20. AI Today

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, UMBC • Government and private research labs • NASA, NRL, NIST, IBM, AT&T, SRI, ISI, MERL, ... • Lots of companies!

22. Applications • 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)

23. 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: ~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 player

24. Robotics

25. 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... • Six finishers (best time: 2.8 miles in 4+ hours)

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

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

28. 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!

29. 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!

30. Computational vs. Biological Memory

31. 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

32. 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 (tape drives) • Factors in memory quality: • Power source (for RAM) • Avoiding extreme temperatures Speed Size

33. Measuring Memory • Remember that one yes/no “bit” is the basic unit • Eight (23) bits = one byte • 1,024 (210) bytes = one kilobyte (1K)* • 1,024K (220 bytes) = one megabyte (1M) • 1,024K (230 bytes) = one gigabyte (1G) • 1,024 (240 bytes) = one terabyte (1T) • 1,024 (250 bytes) = one petabyte (1P) • ... 280 bytes = one yottabyte (1Y?) * Note that external storage is usually measured in decimal rather than binary (1000 bytes = 1K, and so on)

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

35. Computer capacity: Primary storage: 64GB Secondary storage: 750GB (~1012) Tertiary storage: 1PB? (1015) Computer retrieval speed: Primary: 10-7 sec. Secondary: 10-5 sec. Computing capacity: 1 petaflop (1015 floating-point instructions per second), very special purpose Digital Extremely reliable Not (usually) parallel Human capacity: Primary storage: 7 ± 2 “chunks” Secondary storage: 108432 bits?? (or maybe 109 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 Showdown ???? More at movementarian.com

36. 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...

37. The Skeptics Speak

38. 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? ? !

39. 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.”)

40. 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?)