Com1005: Machines and Intelligence

1. Com1005: Machines and Intelligence Amanda Sharkey

2. Last week .... • Early AI programs • The Logic Theorist • GPS General Problem Solver • Relationship to human thought? • AI hype? • AI techniques • Search • Means-Ends-Analysis • Chess • Illusion and AI • Comparison to humans • search

3. Early history of AI continued • Lighthill Report 1973 – ended most support for AI in UK • Early enthusiasm for AI • AI hype • E.g. GPS – only useful for simple problems of a particular kind. • approach depends of pre-set rankings – too complicated for complex problems. • Didn’t “scale up” • Lack of knowledge?

4. Adding knowledge? Microworlds Expert Systems CYC

5. Microworld approach Minsky: supervised students looking at microworlds Blocks world Set of solid blocks placed on tabletop. Task is to rearrange blocks using robot hand. Shrdlu: Terry Winograd (1972) at MIT Natural language understanding program Knows about environment, can reason, plan and learn.

6. Shrdlu • Terry Winograd, MIT • “Understanding natural language” (1972) • Simulated robot arm and blocks world

8. Winograd’s method: based on logic and idea that words point to things in the world. • E.g pick up the ball to the right of the small box • Known instruction – pick up • Find object that satisfies constraints – ball c and d • Ambiguous – can ask. • If answer ‘the large one’ -> ball c

9. But Shrdlu’s knowledge of the world was limited. • E.g. from Haugeland. • Build a steeple • SORRY I DON’T KNOW THE WORD ‘STEEPLE’ • A ‘steeple’ is a stack that contains two green cubes and a pyramid. • I UNDERSTAND • Trade you the steeple for three red cubes • SORRY I DON’T KNOW THE WORD ‘TRADE’ • A ‘trade’ is a free exchange of ownership • SORRY I DON’T KNOW THE WORD ‘FREE’ • Sorry, I thought you were smarter than you are • SORRY I DON’T KNOW THE WORD ‘SORRY’.

10. Shrdlu: domain-specific knowledge (as opposed to domain-general) about microworld. • But does it really understand even its microworld?

11. Expert systems • Depth of knowledge about constrained domain. • Commercially exploitable, real applications • Knowledge stored as production rules • If the problem is P then the answer is A

12. Artificial Intelligence • Understanding mind and intelligence • Creating it, or modelling it • AI and applications • Using AI techniques to do useful things • Creating the illusion of AI

13. Expert systems • Basic idea – experts have knowledge, and this knowledge can be given to computer program. • 1. Requires knowledge base – interview and observe experts and convert words and actions into knowledge base • 2. Reasoning mechanisms to apply knowledge to problems: inference engine • 3. Mechanisms for explaining their decisions

14. IF THEN rules + facts + interpreter • Forward chaining (start with facts and use rules to draw new conclusions) • Backward chaining (start with hypothesis, or goal, to prove and look for rules to prove that hypothesis).

15. Forward chaining – simple example • Rule 1: IF hot AND smoky THEN ADD fire • Rule 2: IF alarm-beeps THEN ADD smoky • Rule 3: IF fire THEN ADD switch-on sprinklers • FACT1: alarm beeps • FACT2: hot • (i) check to see rules whose conditions hold (r2). Add new fact to working memory (FACT3: smoky) • (ii) check again (r1). Add new fact (FACT4: fire) • (iii) check again (r3) Sprinklers on!

16. Expert systems usually use production rules (IF-THEN) • E.gMYCIN • knowledge based system for diagnosis and treatment of infectious diseases of the blood. • Developed at Stanford University, California in mid to late 1970s.

17. Example of MYCIN rule • If • 1. the stain of the organism is gram-positive and • 2. the morphology of the organism is coccus, and • 3. the growth conformation of the organism is clumps • Then there is suggestive evidence (0.7) that the identity of the organism is staphylococcus.

18. 1979: performance of MYCIN shown to be comparable to that of human experts. • But never used in hospitals • Knowledge base incomplete – didn’t know full spectrum of infectious diseases • Needed too much computing power • Interface not good.

19. Dendral • Expert’s assistant – could work out from data from mass spectographs which organic compound was being analysed. • Heuristic search technique constrained by knowledge of human expert.

20. Advantages of expert systems • Human experts can lose expertise • Ease of transfer of artificial expertise • No effect of emotion • Low cost alternative (once developed) • Disadvantages of expert systems • Lack of creativity, not adaptive, lacks sensory experience, narrow focus, no common sense knowledge • E.g won’t notice if medical history says patient weighs 14 pounds and is 130 years old. • More like idiot savants (retarded person who can perform well in one domain), or automated reference manuals.

21. Hubert Dreyfus criticisms • 1972 What computers can’t do • 1992 What computers still can’t do • More to expert understanding than following rules • E.g learning to drive a car. • Novice, thinking consciously • Expert, can decide what to do without thinking

22. But expert systems can still be a useful tool, especially when used together with a human expert. • As long as we don’t expect too much of them.

23. Interim Summary • Classic AI techniques • Search • Knowledge representation • Knowledge • Microworlds – Shrdlu and blocks world • Expert Systems

24. Knowledge representation • Symbolic AI • traditional AI • Good-old fashioned AI (GOFAI) • Emphasis on giving computers knowledge about the world. • Expert Systems • problems: brittle • No common sense

25. Common sense? • Making inferences – Scripts • CYC

26. Roger Schank and colleagues in 1970’s. • Top down approach to language understanding • E.g. SAM Script Applier Mechanism • Aim – to simulate human’s ability to understand stories, and answer questions

27. SAM: Script Applier Mechanism: • E.g. “a woman walked into a restaurant. A waiter showed her to the table. She ordered a rare steak and a bottle of beer. As she left, she smiled at the cashier.” • We assume she sat on a chair, the waiter brought her food, she ate it, and she paid for it. • None of these things stated in the text. • We can infer them because we have knowledge of stereotypical situations like eating in restaurants. • Roger Schank in 1970s (Scripts, Plans, Goals and Understanding)

28. John went to New York by bus. On the bus he talked to an old lady. When he left the bus, he thanked the driver. He took the subway to Leone’s. On the subway his pocket was picked. He got off the train and entered Leone’s. He had some lasagne. When the bill came he discovered he couldn’t pay. The management told him he would have to was dishes. When he left, he caught a bus to New Haven.

29. SAM • How did John get to Leone’s? • JOHN TOOK A BUS TO NEW YORK AND THEN HE TOOK THE SUBWAY TO LEONE’S. • Did anything unusual happen on the bus? • I DON’T REMEMBER ANYTHING. • Did anything unusual happen on the subway? • A THIEF PICKED JOHN’S POCKET. • What did John order? • JOHN ORDERED LASAGNE • Did anything unusual happen at the restaurant? • JOHN DISCOVERED THAT HE COULDN’T PAY THE BILL SO HE HAD TO WASH DISHES. • Why didn’t John have any money? • BECAUSE A THIEF PICKED JOHN’S POCKET • How did John get back to New Haven? • JOHN WENT FROM LEONE’S BY SUBWAY AND THEN HE WENT BACK TO NEW HAVEN BY BUS. • How was John able to take the bus to New Haven? • PROBABLY JOHN HAD A TICKET.

30. Leone story uses restaurant script, bus script and subway script. • Possible to show that people have knowledge of a number of scripts – sequences of actions that are typically performed in certain situations.

31. Computer programs given knowledge of stereotypical situations. • If certain “headers” of scripts are mentioned (e.g. he went to the RESTAURANT then stored knowledge about typical events is retrieved. • E.g. Restaurant script has several scenes • Entering, Ordering, Eating, Exiting • Eating – the waiter brings the food to the customer, the customer eats the food. • Exiting: the customer asks for the bill, the waiter brings the bill, the customer pays the waiter, the customer leaves a tip, the customer leaves the restaurant. • They could use them to infer unmentioned events.

32. Scripts – related to Minsky’s Frames • Expected structure of knowledge about a domain. • E.g. Mention “room” and we have expectations • Some always true – e.g. 4 walls • Some may be so – e.g. that there is a window • Top-down approaches

33. CYC (short for encyclopedia) • Begun in 1984 by Doug Lenat and Edward Feigenbaum • Aim to build knowledge base of common sense knowledge which could allow AI systems to perform human-like reasoning • Trying to include all that humans know but wouldn’t usually say. • Frames and slots • E.g. South Yorkshire • Largest city: Sheffield • Residents: Amanda Sharkey, Noel Sharkey • Country: UK • Supposed to reach a point where it could directly read texts, and self program.

34. CYC – belief that intelligence and understanding are rooted in the explicit language like data structures • CYC – large knowledge base • But still like an expert system • How is it connected to the real world?

35. Knowledge and knowledge representation key to: • Traditional AI • Classical AI • Symbolic AI • Different terms for same idea

36. Assessment 20% written assignment (essay) 5% group presentations 25% practical assignment (next semester) 50% exam (end of next semester)

37. Presentations 5-10 minute presentations in weeks 10 and 11 In tutorial groups Choose from following list of topics....

38. Who was Alan Turing? Computers versus Humans: the important differences Is the mind a computer? Artificial Intelligence and Games What challenges are left for Artificial Intelligence? The social effects of Artificial Intelligence: the good, the bad and the ugly Chatbots Computers and emotions AI and the media Fact or Fiction?: Artificial Intelligence in the movies

39. Newell and Simon (1981) • The physical symbol system hypothesis: • A physical symbol system has the necessary and sufficient means for general intelligent action. • A computer is a physical symbol system – • It manipulates symbols in accordance with instructions in program. • It can make logical inferences and “reason”

40. In propositional logic, procedures for manipulating symbols • E.g. Modus ponens • If p, then q, • So when proposition p occurs then q follows • Symbols can represent states of affairs in the world, but can be processed without considering what they represent. • Thought as logical manipulation of symbols

41. Physical Symbol System hypothesis • A physical symbol system has the necessary and sufficient means for general intelligent action • Strong Physical Symbol System hypothesis • Only computers are capable of thought. • Human mind is a computer • Human thinking consists of symbol manipulation

42. Symbolic model of mind • Traditional view: language of thought (Fodor, 1975) • The mind is a symbol system and cognition is symbol manipulation • Symbols refer to external phenomena • They can be stored in and retrieved from memory, and transformed according to rules.

43. Also known as Functionalism • Physical symbol system hypothesis – closely related to Functionalism or Multiple Realisability. • Thinking (symbol manipulation) can be carried out on any machine • Machine could be made of swiss cheese. • Mind is the software – can run on any hardware • Brains, or computers, or machine made of cogs, levers and springs (Babbage’s Analytical engine?).

44. Strong AI: appropriately programmed computer really is a mind, can be said to understand, and to have other cognitive states Weak AI: a computer is a valuable tool for the study of mind; can make it possible to formulate and test hypotheses rigorously. .

45. Strong AI: appropriately programmed computer really is a mind, can be said to understand, and to have other cognitive states Weak AI: a computer is a valuable tool for the study of mind; can make it possible to formulate and test hypotheses rigorously. .

46. Chinese Room Argument • John Searle: philosopher and critic of AI • “according to strong AI, the computer is not merely a tool in the study of mind; rather the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states”

47. Chinese room • Gedanken (thought) experiment • Imagine- • An operator in a room, with sets of rules about how to manipulate symbol structures • Slots in wall of room – paper can be passed in, and out. • Example of rule: if pattern is X, write 10001011010 on next empty line of exercise book named input store. • Carry out manipulations of those bits, then pair them with chinese characters and pass out of box • Symbols mean nothing to operator

48. Instruction sets, and rules, correspond to program that simulates ability of native Chinese speaker. • Symbols passed in and out correspond to sentences in meaningful dialogue. • Chinese room is able to pass the Turing test!

50. Searle: behaviour of operator is like that of computer running program. • Operator does not understand Chinese, only understands instructions for manipulating symbols. • Computer running program does not understand any more than the operator does.