1. Com1005 Machines and Intelligence Lecturer: Dr Amanda Sharkey

2.  Last week: Turing Test

3.  “rules” for the Imitation Game  Interrogator puts questions to A and B – after 5 minutes of questioning, they must say which is the machine, and which is the person.  Repeated trials, with different questioners and person.  Underspecified? Skills of questioner, and 3 rd player. Number of sessions?

4. Turing’s beliefs  “I believe that in about fifty years' time it will be possible to programme computers, with a storage capacity of about 10 9, to make them play the imitation game so well that an average interrogator will not have more than 70 percent chance of making the right identification after five minutes of questioning. The original question “can machines think?” I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted. “Turing (1950)

6. Descartes (1637)  If there were machines which bore a resemblance to our bodies and imitated our actions as closely as possible for all practical purposes, we should still have two very certain means of recognizing that they were not real men. The first is that they could never use words, or put together signs, as we do in order to declare our thoughts to others. For we can certainly conceive of a machine so constructed that it utters words, and even utters words that correspond to bodily actions causing a change in its organs. … But it is not conceivable that such a machine should produce different arrangements of words so as to give an appropriately meaningful answer to whatever is said in its presence, as the dullest of men can do. Secondly, even though some machines might do some things as well as we do them, or perhaps even better, they would inevitably fail in others, which would reveal that they are acting not from understanding, but only from the disposition of their organs. For whereas reason is a universal instrument, which can be used in all kinds of situations, these organs need some particular action; hence it is for all practical purposes impossible for a machine to have enough different organs to make it act in all the contingencies of life in the way in which our reason makes us act. (Translation by Robert Stoothoff)

7.  Two kinds of questions about Turing Test Is it likely that a computer will be able to play the imitation game so well that it can pass? (empirical question) Is it likely that a computer will be able to play the imitation game so well that it can pass? (empirical question) If a machine was to pass the Turing Test should we conclude that it is capable of thought, intelligence or mentality? (philosophical question) If a machine was to pass the Turing Test should we conclude that it is capable of thought, intelligence or mentality? (philosophical question) Even if a machine were to pass the test, some claim that it would not be capable of thought or intelligence.Even if a machine were to pass the test, some claim that it would not be capable of thought or intelligence.

9.  Hollow shell criticism Computer may pass test, but still not be able to thinkComputer may pass test, but still not be able to think Philosophers – abstract debate about whether we could ever create intelligent machines, and whether TT is an adequate testPhilosophers – abstract debate about whether we could ever create intelligent machines, and whether TT is an adequate test Alternative – just use TT as objective test, and look at ways of creating intelligenceAlternative – just use TT as objective test, and look at ways of creating intelligence

10. Class exercise  Is the Turing Test a good test for thinking/intelligence? Is it too easy or too difficult? Is it too easy or too difficult? How could it be improved? How could it be improved?

11. Possible objections considered by Turing. “I now proceed to consider opinions opposed to my own”  The theological objection *  The ‘heads in the sand’ objection*  The mathematical objection  The argument from consciousness *  Arguments from various disabilities  Lady Lovelace’s objection *  Argument from continuity in the nervous system  The argument from informality of behaviour  The argument from extra-sensory perception

12. The theological objection  People might suggest: ‘Thinking is a function of man’s immortal soul. God has given an immortal soul to every man and woman, but not to any other animal or to machines. Hence no animal or machine can think’  Turing’s dismissal: Why not believe that God could give a soul to a machine if he wished?  “I should find the argument more convincing if animals were classed with men, for there is a greater difference, to my mind, between the typical animate and the inanimate than there is between man and the other animals.”

13. The heads in the sand objection  People could say: “The consequences of machines thinking would be too dreadful. Let us hope and believe that they cannot do so."  Turing’s dismissal: argument not substantial enough to require refutation, (objection related totheological objection.)

14. Argument from consciousness  People might suggest that machines cannot feel emotions ‘Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain – that is not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, and easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants’” (Jefferson, 1949)

15. Turing’s dismissal of argument from consciousness:  How could we tell?  The only way we could be sure that a machine thinks is to be that machine and to feel oneself thinking.  Similarly, the only way to be sure that someone else thinks is to be that person.  How do we know that anyone else is conscious? Solipsism.  But we assume other people are conscious. Similarly we could assume that a machine that passes the Turing test is effectively conscious.

16. Lady Lovelace’s objection  (memoir from Lady Lovelace about Babbage’s Analytical Engine)  Babbage (1792-1871) and Analytical Engine: general purpose calculator. It was entirely mechanical, and never actually built.  “The Analytical Engine has no pretensions to originate anything. It can do what ever we know how to order it to perform” (Lady Lovelace memoir 1842)  A computer cannot be creative, it cannot originate anything

17.  Turing’s dismissal of Lady Lovelace’s objection  Computers can surprise their programmers – producing answers that were not expected. Data may have originally be given to the computer, but then it may have been able to work out its consequences and implications.

18. Further objections to Turing Test  i) Too easy?  5 minutes of questioning?  Sense organs objection  TT focuses on verbal responses, and computer can use words without really knowing their meanings.  Like driving test that only consists of answering questions  Equip it with sense organs, and you could test knowledge of the world.  But many words can be investigated without the need for sense organs.

19. Further objections to Turing Test Too difficult..... ii) Chimpanzees, dolphins, dogs and babies can all think but would not pass TT. Computer might fail because answers non-human But only means that TT is not a litmus test (red=acid, blue=non-acidic) -nothing definite follows if computer/animal/baby fails the test Only passing the test has meaning.

20.  Too difficult.... Subtle ways in which we can detect if something is human... Computer unlikely to be able to answer questions closely based on human physical and cultural experience Similar to questions designed to tell men/women apart in original parlour game.

21. Further objections to the TuringTest  iii) Simulation objection  The simulation of X is never an X  In Victorian parlour game, if man misleads the interrogator into thinking he is a woman – he doesn’t become a woman!  2 kinds of simulation Simulation1: lacks essential features e.g. simulated death, person is not dead Simulation1: lacks essential features e.g. simulated death, person is not dead Simulation2: exactly like what is being simulated but not produced in standard way Simulation2: exactly like what is being simulated but not produced in standard way

22. Further objections  iv). Black box objection A device whose inner workings are unknown. A device whose inner workings are unknown. Turing test only looks at outward behaviour Turing test only looks at outward behaviour But need to see how behaviour being produced But need to see how behaviour being produced E.g. computer could be just matching sentences to be output in response to interrogator’s questions – no thought E.g. computer could be just matching sentences to be output in response to interrogator’s questions – no thought

23. Eliza  Men are all alike  IN WHAT WAY?  They’re always bugging us about something or other  CAN YOU THINK OF A SPECIFIC EXAMPLE?  Well my boyfriend made me come here  YOUR BOYFRIEND MADE YOU COME HERE? Etc etc

24. How Eliza works  Transform input to change point of view E.g change you to I E.g change you to I You understand me > I understand you You understand me > I understand you

25.  If pattern matches transformed input choose one of responses associated with that pattern. Or choose general purpose response Pattern *mother* [tell me more about your family] Pattern *mother* [tell me more about your family] E.g. Perhaps I could learn to get along with my mother E.g. Perhaps I could learn to get along with my mother TELL ME MORE ABOUT YOUR FAMILY TELL ME MORE ABOUT YOUR FAMILY

26.  Eliza lacks Means of recognising grammatical structure of language Means of recognising grammatical structure of language Means of converting user’s query into a representation of the underlying knowledge from which inferences can be drawn. Means of converting user’s query into a representation of the underlying knowledge from which inferences can be drawn.

27. Parry  Even simpler  Looks for known patterns  When pattern detected, output first sentence from a list associated with that pattern  E.g input contains ‘you’ and expression from Abnormal List (delusional, paranoid etc)  You are delusional  I THINK I KNOW WHAT YOU DOCTORS ARE UP TO.

28. Modified Turing Test  Check outward form of Turing Test is passed  Look inside the program (black box) to see whether  i) the program works in a similar way to humans OR  ii) the program is extendible and not just designed for passing test. E.g. could motor and sensory functions be added?

29.  Or maybe idea of Turing Test should be abandoned.  Reason 1: Unitary notion of ‘intelligence’ too simplistic  Better to break down question into smaller questions  Assess computers in terms of more specific abilities – eg. Ability to navigate across a room, ability to perform logical reasoning, metaknowledge (knowledge of own limitations)

30.  Reason 2: too anthropocentric  Why should program have to work like humans?  E.g Dogs capable of cognition, but would not pass Turing Test.

31. Does it matter?  Trusting machines too much Dangers from overestimating their abilities Dangers from overestimating their abilities  Computer rights? Robot rights? Animal rights? Animal rights?

33. More recent trends  Fewer attempts to simulate general human intelligence  Instead: getting programs to do intelligent things regardless of how they do them

34.  Language: machine translation, and text processing – good results found by applying statistical methods to large lexical corpora  Also in speech recognition  Less interest in working on natural language understanding and representation

35.  Currently less interest in devising programs to pass Turing Test  Concentration instead on particular problem areas (e.g. vision, natural language, speech recognition).  Might return to Turing Test one day when individual problem areas solved?

36. Alternatives to “Traditional AI”  Alternatives: Neural Computing and Adaptive Behaviour  Minimal claim for a computer to be a ‘thinking thing’ should be adaptable  Adaptable more than learning – to adapt an organism needs to be able to learn to survive in what ever environment it finds itself.  Are chess playing programs adaptable?

37.  Rodney Brooks Traditional (symbolic) AI has looked at the easy bit – high level reasoning, divorced from the real world. Traditional (symbolic) AI has looked at the easy bit – high level reasoning, divorced from the real world. More important to work on the lower level skills (e.g. vision, movement, navigation) which have taken years to evolve. More important to work on the lower level skills (e.g. vision, movement, navigation) which have taken years to evolve.

38. Summary  Possible objections to Turing Test – raised and dismissed by Turing  Discussion of objections to Turing test  Further objections to TT Too hard to pass Too hard to pass Too easy to pass Too easy to pass  More recently: greater emphasis on adaptation and ability to survive in environment: changing emphasis away from human ‘intelligent’ behaviour, and on to low level abilities: e.g. vision, movement. changing emphasis away from human ‘intelligent’ behaviour, and on to low level abilities: e.g. vision, movement.

39. The end of lecture 2........

40. Argument from continuity with the nervous system  Nervous system is continuous: digital computer is discrete state machine.  i.e. in nervous systems, a small error in the information about the size of a nervous impulse impinging on a neuron may make a large difference to the size of the outgoing impulse.  Discrete machines – moves by sudden jumps and clicks from one state to another.  Turing’s dismissal: a discrete state machine can still give answers that are indistinguishable from a continuous machine.

41. The mathematical objection  Results of mathematical logic can be used to show that there are limitations to the powers of discrete-state machines.  E.g. halting problem: will the execution of a program P eventually halt, or will it run for ever. Turing (1936) proved that for any algorithm H that purports to solve the halting problem, there will always be a program Pi such that H will not be able to answer the halting program correctly.  i.e. certain questions cannot be answered correctly by any formal system.  Turing’s dismissal: similar limitations also apply to the human intellect.