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AI - Introduction

AI - Introduction

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AI - Introduction

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  1. AI - Introduction Bertil Ekdahl

  2. What is AI? It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. John McCarthy,

  3. When did it start? • Alan Turing • 1947 lecture on intelligent machines. • 1950 article: Computing Machinery and Intelligence.

  4. The Turing Test (Borrowed from Ola Flygt.)

  5. A Turing Machine Penrose, Roger, “The Emperor’s New Mind”

  6. Two symbols where B means “blank” and 1 is the tally symbol.

  7. ENIAC (Electronic Numerical Integrator And Calculator)

  8. EDVAC (Electronic Discrete Variable Automatic Computer)

  9. Dartmouth Artificial Intelligence (AI) Conference • We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. • The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. • Dartmouth AI Project proposal: J. McCarthy et al.; Aug. 31, 1955. (

  10. Why such a conjecture? … every aspect of learning […] be so precisely described that a machine can be made to simulate it. Tabula rasa

  11. But the human intellect, which is the lowest in the order of intellects and the most removed from the perfection of the Divine intellect, is in potency with regard to things intelligible, and is at first "like a clean tablet on which nothing is written", as the Philosopher [Aristotle] says. (Thomas Aquinas, Summa Theologica 1.79.2, 1265 - 1274)

  12. Connectionism Rumelhart & McClelland: generic associationist networks, subjected to massive amounts of training, could explain all of cognition.

  13. A problem Rumelhart & McClelland was left with the answer: Why is people smarter than rats? Answer: [The] question does seem a bit puzzling. […] another aspect to the difference […] is that the human environment includes other people and the CULTURAL devices that they have developed to organize their thinking process. (1986)

  14. Why such a conjecture? …make machines use language, form abstractions and concepts … No difference between formal language (programming language) and natural language?

  15. Everything is a question of representation?

  16. Roman digits

  17. Binary digits

  18. shift out

  19. Time 2h 30min 15s + 3h 45min 37s =?

  20. Fifty-five Femtiofem Fünfundfünfzig Femoghalvtreds

  21. What is a Knowledge Representation? • A KR is a Surrogate • A KR is a Set of Ontological Commitments • A KR is a Fragmentary Theory Of Intelligent Reasoning • A KR is a Medium for Efficient Computation • A KR is a Medium of Human Expression (R. Davis, H. Shrobe, and P. Szolovits. What is a Knowledge Representation? AI Magazine, 14(1):17-33, 1993).

  22. Knowledge representation in AI • Logic • Semantic nets • Conceptual graphs • Neural nets • Object oriented methods

  23. Logic Production system: IF … THEN IF you have pain in your back AND you have eaten many Panocode THEN examine your bile.

  24. Pain in your back? No Eaten Panocode? Yes

  25. G: Men are all alike. P: IN WHAT WAY? G: They’re always bugging us about something or other. P: CAN YOU THINK OF A SPECIFIC EXAMPLE? G: Well, my boyfriend made me come here. P: YOUR BOYFRIEND MADE YOU COME HERE? G: He says I’m depressed much of the time. P: I AM SORRY TO HERE THAT YOU ARE DEPRESSED. G: It’s true. I am unhappy. P: DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY? G: I need some help, that much seems certain. P: WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP? G: Perhaps I could learn to get along with my mother. P: TELL ME MORE ABOUT YOUR FAMILY.

  26. (I remember X) ---> (Why do you remember X just now?) Person: I remember my mother. ELIZA: Why do you remember your mother just now? (My {family-member} Y me) ---> (Who else in your family Y you?) Person: My father hates me. ELIZA: Who else in your family hates you? Rules without left hand side. ELIZA: Can you elaborate on that for me?, or ELIZA: That is very interesting. Why do you say that?

  27. Logic goes beyond IF … THEN-rules The author of the Iliad wrote the Odyssey; therefore someone wrote both the Iliad and the Odyssey. No one admires anyone who admires everyone who admires someone.

  28. Semantic nets

  29. has Cat Fur Vertebra isa has has isa Animal Mammal Bear isa Whale isa lives in Fish Water lives in

  30. Pacifist isa isa Republican Quaker isa isa Nixon

  31. Conceptual Graph CG: John is going to Boston by bus.

  32. Object method /* schema facts */ man::person. woman::person. person[hasFather=>man]. person[hasMother=>woman]. /* facts */ abraham:man. sarah:woman. isaac:man[hasFather->abraham; hasMother->sarah]. /* rules consisting of a rule head and a rule body */ FORALL X,Y X[hasSon->>Y] <- Y:man[hasFather->X]. /* query */ FORALL X,Y <- X:woman[hasSon->>Y[hasHasFather->abraham]].

  33. Q. How far is AI from reaching human-level intelligence? When will it happen? A. A few people think that human-level intelligence can be achieved by writing large numbers of programs of the kind people are now writing and assembling vast knowledge bases of facts in the languages now used for expressing knowledge. However, most AI researchers believe that new fundamental ideas are required, and therefore it cannot be predicted when human level intelligence will be achieved. (John McCarthy,

  34. Why have we made limited progress in AI? Because we haven't developed sophisticated models of thinking, we need better programming languages and architectures, and we haven't focused on common sense problems that every normal child can solve. (Minsky, 2002)

  35. What’s the problem? Semantics

  36. Roman: LV Binary: 110111 Octal: 101101 Decimal: 55 Fifty-five Femtiofem Fünfundfünfzig Femoghalvtreds

  37. Susan saw the man in the park with a dog. Susan saw the man in the park with a statue. Susan saw the man in the park with a telescope

  38. Computer versus Common Sense

  39. END