CMPT 420 / CMPG 720 Artificial Intelligence

1. CMPT 420 / CMPG 720Artificial Intelligence

2. What is Artificial Intelligence? • Essential English Dictionary, Collins, London, 1990: • Someone’s intelligenceis their ability to understand and learn things. • Intelligenceis the ability to think and understand instead of doing things by instinct or automatically. • Thinkingis the activity of using your brain to consider a problem or to create an idea. • We can define intelligence as ‘the ability to learn and understand, to solve problems and to make decisions’.

3. What is Artificial Intelligence? • Psychological approach: an intelligent system is a model of human intelligence • Engineering approach: an intelligent system solves a sufficiently difficult problem in a generalizable way

4. What is Artificial Intelligence? (again)

5. Thinking Humanly • 1960’s cognitive revolution • Requires scientific theories of internal activities of the brain • What level of abstraction? “Knowledge” or “Circuits” • How to validate? • Predicting and testing behavior of human subjects (top-down) • Direct identification from neurological data (bottom-up) • Cognitive Science & Cognitive Neuroscience • Now distinct from AI

6. Acting Humanly: The Turing Test • Alan Turing, British mathematician (1912-1954) • “Computing machinery and intelligence” paper in 1950 • Can machines think?

7. The Turing Test (a.k.a. Turing imitation game) • Alan Turing suggested an imitation game. • A person C questions two other “agents” A and B over a computer terminal. • The person C cannot see or hear A and B. • Both A and B claim they are humans. • But one of them is lying. • If C cannot detect that B is a computer, that means that B is for all practical purposes “intelligent.” Human ? Human Interrogator AI System

8. Acting Humanly: The Turing Test • The Turing Test (a.k.a. Turing imitation game) • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes

9. Loebner Prize • The Loebner Prize is an annual competition for AI programs. • https://www.aisb.org.uk/events/loebner-prize • Crown Industries of East Orange, NJ • $100,000 and a Gold Medal for the first computer that passes the Turing Test. • Each year $4000 and a bronze medal is awarded to the most human-like computer.

10. The Turing Test • Natural language processing • Knowledge representation • Automatic reasoning • Machine learning • Total Turing Test: computer vision and robotics

11. Thinking Rationally • Aristotle: What are correct arguments / thought processes? • “Socrates is a man; all men are mortal; therefore, Socrates is mortal.” • Logic notation and rules for derivation for thoughts • Problems: • Not all knowledge can be transformed to logic, especially when it is less than 100% certain • Problems with a few hundred facts can exhaust computational resources

12. Acting Rationally • Rational behavior • Doing the right thing • What is the “right thing” • That which is expected to maximize goal achievement, given available information • We do many (“right”) things without thinking • e.g., blinking reflex • Thinking should be in the service of rational action • The textbook advocates "acting rationally"

13. Rational agents • An agentis an entity that perceives and acts. • This course is about designing rational agents. • Abstractly, an agent is a function from percept histories to actions: [f: P*A] • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance.

14. Strong AI vs. Weak AI • Strong AI is artificial intelligence that matches or exceeds human intelligence. • “Artificial general intelligence” • The weak AI hypothesis: machines can demonstrate intelligence, but do not necessarily have a mind, mental states or consciousness.

15. Chinese Room Argument • John Searle • “Chinese room” setup • Searle answers questions given to him in Chinese (though he does not know any Chinese) “恭喜发财” “谢谢”

16. Chinese Room Argument • Book: instructions to answer Chinese questions • Searle: just applies algorithm given in book • Does Searle now "understand" Chinese? • Observation: every computer can be described on paper

17. Chinese Room Argument • Strong AI advocates: it's Searle + room + book that understands! • reply: suppose Searle memorizes algorithm in his head: does he understand now? • executing an algorithm does not constitute thinking • the terms "understand" and "think" are not something we can apply to inanimate objects • "Minds, Brains, and Programs" by John R. Searle (The Behavioral and Brain Sciences, Vol 3 (1980))

18. History of AI • Warren McCulloch & Walter Pitts (1943): • Research on the human central nervous system led to a model of neurons of the brain • Birth of Artificial Neural Networks (ANN) • Binary model • Non-linear model • John von Neumann • ENIAC, EDVAC, etc.

19. History of AI • Claude Shannon, MIT, Bell Labs (1950): • Computers playing chess • Chess game involved about 10120 possible moves! • Even examining one move per microsecond would require 3 x 10106 years to make its first move • Need to incorporate intelligence via heuristics

20. History of AI • John McCarthy, Dartmouth, MIT (1950s): • Defined LISP • Only two years after FORTRAN • LISP is based on formal logic • “Programs with Common Sense” paper (1958) • Marvin Minsky, Princeton, MIT: • Anti-logical approach to knowledge representation and reasoning called frames (1975)

22. History of AI • Great expectations during 1950s and 1960s • But very limited success • Researchers focused too much on all-purpose intelligent machines with goals to learn and reason with human-scale knowledge (and beyond) • Refocus on specific problem domains (1970s) • Domain-specific expert systems with facts, rules, etc. • Analyze chemicals, medical diagnoses, etc.

23. History of AI • Evolutionary computation (1970s-today): • Natural intelligence is a product of evolution • Can we solve problems by simulatingbiological evolution? • Survival of the fittest • Genetic programming • Evolutionary computing

24. History of AI • Rebirth of neural networks (1980s-today): • Adaptive resonance theory (Grossberg, 1980) incorporated self-organization principles • Hopfield networks (Hopfield, 1982) introduced neural networks with feedback loops • Back-propagation learning algorithm (Rumelhart & McClelland, 1986) for training multilayer perceptrons

25. History of AI • Knowledge engineering (1980s-today): • Fuzzy set theory (Zadeh) associates words with degrees of truth or value • Rule-based knowledge systems • Combine information from multiple experts • Semantic Web • Numerous hybrid approaches exist

26. Semantic Web • Sir Tim Berners-Lee

27. Abridged History of AI • 1943 McCulloch & Pitts: Boolean circuit model of brain • 1950 Turing's "Computing Machinery and Intelligence" • 1956 Dartmouth meeting: "Artificial Intelligence" adopted • 1952—69 Look, Ma, no hands! • 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine • 1965 Robinson's complete algorithm for logical reasoning • 1966—73 AI discovers computational complexity Neural network research almost disappears • 1969—79 Early development of knowledge-based systems • 1980-- AI becomes an industry • 1986-- Neural networks return to popularity • 1987-- AI becomes a science • 1995-- The emergence of intelligent agents

28. State of the Art • Game playing: IBM’s Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997. • Speech recognition: A traveler calling United Airlines to book a flight can have the entire conversation guided by an automatic speech recognition system. • Robotic vehicles: No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) • Logistics planning: During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people. • Robotics: The iRobot Corporation has sold over two million Roomba robotic vacuum cleaners for home use.

29. Less Successful Areas of AI • Sadly the Loebner Gold Medal still has not been awarded. • Natural Language Processing is still mostly an unresolved problem. • Ninety/Ten Rule: Can do 90% of the translation with 10% time, but 10% work takes 90% time

30. Can this be solved by computers? • Playing a decent game of table tennis (Ping-Pong).

31. Can this be solved by computers? • Playing a decent game of table tennis (Ping-Pong). • A reasonable level of proficiency was achieved by Andersson’s robot (Andersson,1988).

32. Can this be solved by computers? • Driving in the center of Cairo, Egypt.

33. Can this be solved by computers? • Driving in the center of Cairo, Egypt. • No. Although there has been a lot of progress in automated driving, all such systems currently rely on certain relatively constant clues: that the road has shoulders and a center line, that the car ahead will travel a predictable course, that cars will keep to their side of the road, and so on. Some lane changes and turns can be made on clearly marked roads in light to moderate traffic. Driving in downtown Cairo is too unpredictable for any of these to work.

34. Can this be solved by computers? • Buying a week’s worth of groceries at the market.

35. Can this be solved by computers? • Buying a week’s worth of groceries at the market. • No. No robot can currently put together the tasks of moving in a crowded environment, using vision to identify a wide variety of objects, and grasping the objects (including squishable vegetables) without damaging them. The component pieces are nearly able to handle the individual tasks, but it would take a major integration effort to put it all together.

36. Can this be solved by computers? • Buying a week’s worth of groceries on the Web.

37. Can this be solved by computers? • Buying a week’s worth of groceries on the Web. • Yes. Software robots are capable of handling such tasks, particularly if the design of the web grocery shopping site does not change radically over time.

38. Can this be solved by computers? • Writing an intentionally funny story.

39. Can this be solved by computers? • Writing an intentionally funny story. • No. While some computer-generated prose and poetry is hysterically funny, this is invariably unintentional, except in the case of programs that echo back prose that they have memorized.

40. Unintentionally Funny Stories • One day Joe Bear was hungry. He asked his friend Irving Bird where some honey was. Irving told him there was a beehive in the oak tree. Joe threatened to hit Irving if he didn't tell him where some honey was. The End. • Henry Squirrel was thirsty. He walked over to the river bank where his good friend Bill Bird was sitting. Henry slipped and fell in the river. Gravity drowned. The End.

41. Can this be solved by computers? • Giving competent legal advice in a specialized area of law.

42. Can this be solved by computers? • Giving competent legal advice in a specialized area of law. • Yes, in some cases. AI has a long history of research into applications of automated legal reasoning. One example is the Prolog-based expert systems used in the UK to guide members of the public in dealing with the intricacies of the social security and nationality laws. However, extension into more complex areas such as contract law awaits a satisfactory encoding of the vast web of common-sense knowledge pertaining to commercial transactions and agreement and business practices.

43. Can this be solved by computers? • Translating spoken English into spoken Swedish in real time.

44. Can this be solved by computers? • Translating spoken English into spoken Swedish in real time. • Yes. In a limited way, this is already being done.

45. Can this be solved by computers? • Performing a complex surgical operation.

46. Can this be solved by computers? • Performing a complex surgical operation. • Yes. Robots are increasingly being used for surgery, although always under the command of a doctor. Robotic skills demonstrated at superhuman levels include drilling holes in bone to insert artificial joints, suturing, and knot-tying. They are not yet capable of planning and carrying out a complex operation autonomously from start to finish.

47. Reading • AIMA Chap 2 • "Computing Machinery and Intelligence" by Alan Turing (Mind, Vol. LIX, No. 236 (1950)). • "Minds, Brains, and Programs" by John R. Searle (The Behavioral and Brain Sciences, Vol 3 (1980))