INTRODUCTION TO ARTIFICIAL INTELLIGENCE

1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE Massimo PoesioLECTURE 1: Intro to the course, History of AI

2. ARTIFICIAL INTELLIGENCE: A DEFINITION • The branch of Computer Science whose aim is to develop machines able to display intelligent behavior • Strong AI: developing machines all but undistinguishable from human beings • Weak AI: developing SIMULATIONS of human intelligence

3. AI BY EXAMPLE: GAMES

4. AI BY EXAMPLE: ROBOCUP

5. AI BY EXAMPLE: LANGUAGE

6. AI BY EXAMPLE: LANGUAGE

7. A BRIEF HISTORY OF AI • Forerunners, I: logic and ontologies • Forerunners, II: mechanical machines / robots • The beginning of AI: Turing, Dartmouth, Games, Search • The role of Knowledge in Human Intelligence • The role of Learning • Modern AI

8. ARISTOTLE • Aristotle developed the first theory of knowledge and reasoning – his ideas eventually developed into modern • LOGIC • ONTOLOGIES

9. ARISTOTLE: SYLLOGISM(Prior Analytics) • The first attempt to develop a precise method for reasoning about knowledge: identify VALID REASONING PATTERNS, or SYLLOGISMS • BARBARA:A: All animals are mortal • A: All men are animals. • A: Therefore, all men are mortal. • DARII:A: All students in Fil., Logica & Informatica take Intro to AI • I: Some students in Filosofia take Fil., Logica & Informatica. • I: Therefore, some student in Filosofia takes Intro to AI.

10. FIRST ONTOLOGIES

11. LOGIC: BEYOND ARISTOTLE • Ramon Lull (13th Century): first mechanical devices for automatic reasoning (Lull’s disks) • Leibniz (17th Century): Encoding for syllogisms • Boole (19th Century): Boolean Algebra • Frege (1879): Predicate calculus

12. BOOLEAN ALGEBRA

13. FORERUNNERS, 2

14. TURING • Alan Turing is the father of AI • He was the first to imagine machines able of intelligent behavior • And devised an intelligence test, the TURING TEST: replace the question “Can a machine be endowed with intelligence” with the question: • Can a machine display such human-like behavior to convince a human observer that it is a human being?

15. DARTMOUTH • In 1956 a group of researchers including J. McCarthy, M. Minsky, C. Shannon, N. Rochester organized a workshop at Dartmouth to study the possibility of developing machine intelligence

16. THE BEGINNINGS OF AI (1956-1966) • Early AI researchers identified intelligence with the kind of behavior that would be considered intelligent when displayed by a human, and tried to develop programs that reproduced that behavior • Examples: • Chess • Theorem proving

17. HEURISTIC SEARCH • This early research focused on the development of SEARCH ALGORITHMS (A*) that would allow computers to explore a huge number of alternatives very efficiently

18. THE SUCCESS OF EARLY AI In 1997, the chess-playing program DEEP BLUE developed by IBM researchers led by Feng-hsiung Hsu, beat the chess world champion Gary Kasparov over six games

19. EARLY AI RUNS INTO TROUBLE (1966-1973) • Soon however researchers realized that these methods could not be applied to all problems requiring intelligence, and that there were a number of ‘simple’ problems that could not be handled with these methods at all • Example of the first: machine translation (the ALPAC report) • Example of the second: natural language, vision

20. COMMONSENSE KNOWLEDGE IN LANGUAGE UNDERSTANDING • Winograd (1974): • The city council refused the women a permit because they feared violence. • The city council refused the women a permit because they advocated violence

21. AI KEY DISCOVERIES, 1 • Performing even apparently simple tasks like understanding natural language requires lots of knowledge and reasoning

22. THE ‘KNOWLEDGE YEARS’ (1969-1985) • Development of knowledge representation techniques • Development of EXPERT SYSTEMS • Development of knowledge-based techniques for • Natural Language Understanding • Vision

23. KNOWLEDGE REPRESENTATION METHODS • Logic is the older formalization of reasoning • It was natural to think of logic as providing the tools to develop theories of knowledge and its use in natural language comprehension and other tasks • Great success in developing theorem provers • But AI researchers quickly realized that the form of logic required was not valid deduction

24. FROM LOGIC TO AUTOMATED REASONING • Starting from the ‘50s AI researchers developed techniques for automatic theorem proving • These techniques are still being developed and have been used to prove non-trivial theorems

25. RESOLUTION THEOREM PROVING • All Cretans are islanders. • All islanders are liars. • Therefore all Cretans are liars. • ∀X C(X) implies I(X) • ∀X I(X) implies L(X) • Therefore, ∀X C(X) implies L(X) • ¬C(X) ∨ I(X) • ¬I(Y) ∨ L(Y) • ¬C(X) ∨ L(Y)

26. HIGH PERFORMANCE THEOREM PROVING • There are now a number of very efficient theorem provers that can be used to demonstrate sophisticated mathematical theorems • Otter • Donner & Blitzen

27. THE FOUR-COLOR PROBLEM • Conjecture: given a plane divided in regions, it is possible to color the regiones in such a way that two adjacent regions are always of different colors using no more than 4 colors • This conjecture was demonstrated by an automatic theorem prover in 1997

28. AI KEY DISCOVERIES, 2 • Neither commonsense nor ‘expert’ reasoning involve only valid inferences from certain premisses: • Commonsense reasoning often involves jumping to plausible conclusions • Expert reasoning involves making decisions with uncertainty

29. COMMONSENSE KNOWLEDGE IN LANGUAGE UNDERSTANDING • Winograd (1974): • The city council refused the women a permit because they feared violence. • The city council refused the women a permit because they advocated violence

30. DIAGNOSTIC REASONING IN EXPERT SYSTEMS IF patient has loss of weight AND anorexia AND spleen is palpable AND fever THEN patient MAY have Hodgkin’s disease

31. BEYOND VALID INFERENCE • As a result of discovery number 2, AI researchers quickly got involved with developing better theories of commonsense reasoning and uncertain reasoning • Drawing inspiration from psychology (work on semantic networks, scripts) • Developing new inference formalisms

32. SOME RESULTS FROM COGNITIVE PSYCHOLOGY: SEMANTIC NETWORKS • Collins & Quillian, 1969: knowledge appears to be organized around objects and in a taxonomic way • A canary is yellow • A canary has feathers • A canary eats food • Haviland & Clark 1974, Sanford & Garrod 1979: ‘associated’ knowledge available when concepts are mentioned • I looked around the house. • The lounge was very spacious.

33. AN EXAMPLE OF SEMANTIC NETWORK

34. COMMONSENSE REASONING: SCRIPTS (SCHANK & ABELSON, 1977) John entered the restaurant. The menu had changed, so he ordered one of the new dishes. The bill was very steep.

35. AN EXAMPLE OF SCRIPT

36. ‘KNOWLEDGE-BASED’ SYSTEMS FOR LANGUAGE UNDERSTANDING: SHRDLU Winograd, 1974

37. A DIALOGUE WITH SHRDLU Person: PICK UP A BIG RED BLOCK.Computer: OK.Person: GRASP THE PYRAMID.Computer: I DON'T UNDERSTAND WHICH PYRAMID YOU MEAN.Person (changing their mind): FIND A BLOCK WHICH IS TALLER THAN THE ONE YOU ARE HOLDING AND PUT IT INTO THE BOX.Computer: BY "IT", I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN THE ONE I AM HOLDING.Computer: OK.Person: WHAT DOES THE BOX CONTAIN?Computer: THE BLUE PYRAMID AND THE BLUE BLOCK.Person: WHAT IS THE PYRAMID SUPPORTED BY?Computer: THE BOX.Person: HOW MANY BLOCKS ARE NOT IN THE BOX?Computer: FOUR OF THEM.Person: IS AT LEAST ONE OF THEM NARROWER THAN THE ONE WHICH I TOLD YOU TO PICK UP?Computer: YES, THE RED CUBE.

38. EXPERT SYSTEMS • Beginning with the ’70s, AI researchers developed systems that could be used to diagnose • Illnesses (MYCIN, DXPLAIN) • Mechanical problems • DELTA-CATS1, General Electric, Bonissone et al 1984 • IDEA, Centro Ricerche Fiat, ~1993 • etc

39. EXPERT SYSTEMS

40. A COMMONSENSE ENCYCLOPEDIA: CYC • A project initiated in 1984 by Doug Lenat. The goal: to encode all of commonsense knowledge • Changed the underlying formalism several times. • These days: a logic-based representation • Two versions available: • OpenCyc (http://www.opencyc.org/) • 50 000 concepts, 300 000 facts • Can be downloaded / on the Web • ResearchCyc (http://research.cyc.com/) • 300 000 concepts, 3 million facts

41. KNOWLEDGE IN CYC "Bill Clinton belongs to the class of US Presidents“(#$isa #$BillClinton #$UnitedStatesPresident) “All trees are plants” (#$genls #$Tree-ThePlant #$Plant) “Paris is the capital of France". (#$capitalCity #$France #$Paris)

42. COMMONSENSE REASONING • Modelling commonsense inference required the development of entirely new paradigms for inference beyond classical logic • Non-monotonic reasoning • Probabilistic models

43. AI RUNS INTO TROUBLE, AGAIN • The CYC project started in 1984, and by common opinion is nowhere near finished • Hand-coding of commonsense FACTS is unfeasible • (We will get back to this point later when talking about socially constructed knowledge) • Work on lower-level tasks such as speech perception revealed the impossibility of hand-coding commonsense RULES and assigning them priorities

44. SPEECH

45. KEY AI DISCOVERIES, 3 • A theory of intelligence requires a theory of how commonsense knowledge and cognitive abilities are LEARNED

46. THE MACHINE LEARNING YEARS (1985-PRESENT) • The development of methods for learning from evidence started even before Dartmouth • But machine learning has now taken center stage in AI

47. CYBERNETICS • McCulloch, Pitts (1943): first artificial neurons model (based on studies of real neurons)

48. KNOWLEDGE REPRESENTATION IN THE BRAIN

49. MODELS OF LEARNING BASED ON THE BRAIN: THE PERCEPTRON

50. LEARNING TO CLASSIFY OBJECTS