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Computing Machinery and Intelligence By Alan M. Turing

Dec 18, 2015



  • Slide 1
  • Computing Machinery and Intelligence By Alan M. Turing
  • Slide 2
  • Computing Machinery and Intelligence Published in Mind: A Quarterly Review of Psychology and Philosophy, in 1950. I propose to consider the question, Can machines think?
  • Slide 3
  • About the paper Describes the imitation game, now called the Turing Test. Possibly one of most important and disputed topics in AI, philosophy of mind, cognitive science. The foundation of AI, and its ultimate goal? Useless and even harmful? A key paper regardless.
  • Slide 4
  • Which is machine, and which is woman??? The Imitation Game Conversation Which is man, and which is woman???
  • Slide 5
  • Lets try itcomputer of poet? At six I cannot pray: Pray for lovers, through narrow streets And pray to fly But the Virgin in their dark wintry bed
  • Slide 6
  • Lets try itcomputer of poet? What seas what shores what granite islands toward my timbers And woodthrush calling through the fog My daughter.
  • Slide 7
  • Lets try itcomputer of poet? Men with picked voices chant the names of cities in a huge gallery: promises that pull through descending stairways to a deep rumbling.
  • Slide 8
  • Lets try itcomputer of poet? Where were thou, sad Hour, selected from whose race is guiding me, Lured by the love of Autumn's being, Thou, from heaven is gone, where was lorn Urania When rocked to fly with thee in her clarion o'er the arms of death.
  • Slide 9
  • A Brief History of AI, pre- Turing Test Greek mythology: Hephaestus, idea of intelligent robots. 13 th century: talking heads, supposedly owned by Robert Bacon, Albert the Great 15 th century: da Vinci drafted robot design 16 th century: the Maharal of Pragues Golem 17 th century: Descartes animals are complex machines 19 th century: Charles Babbages Analytical Engine 1940s: Isaac Asimov Three Laws of Robotics 1943: McCulloch and Pitts, model neurons with algorithms?
  • Slide 10
  • Turings contemporaries, and subsequent related work in AI Claude Shannon, 1950: algorithm for playing Chess. Alan Newell and Herbert Simon, 1956: one of first expert systems, Logic Theorist. Noam Chomsky, 1957: analyze language mathematically, Syntactic Structures. Friedberg, 1958: genetic algorithms. Joseph Weizenbaum, 1966: writes computer program ELIZA, with some success at imitation game Computerized human psychologist Minsky and Papert, 1968: wrote book Perceptrons, showing some limitations of neural nets. Slowed research in area. Kurzweil Reading Machine, 1976: read printed text. MYCIN, 1979: expert system that diagnosed some diseases.
  • Slide 11
  • Proponents and Opponents of AI Lots of debate about potential success and limitations of AI. Herbert Simon, 1958: within ten years a digital computer will be the worlds chess champion. Hubert Dreyfus, 1972: What Computers Cant Do Human intelligence is more than manipulation of symbols. John Searle, 1980: Opposed idea of strong AI, that machines can think, with Chinese Room thought experiment.
  • Slide 12
  • The Paper Can machines think? Not a meaningful question, definitional issues Instead, suggests imitation game Description of machines, and universality of digital computers Possible objections to the question and the test, with responses: Theological, mathematical, arguments from consciousness, originality, etc. Learning machines
  • Slide 13
  • Digital Computers Are there imaginable digital computers which would do well in the imitation game? Manchester Mark 1
  • Slide 14
  • Predictions In 1950, Turing predicted that 50 years later it will be possible to program a computer with ~100 Mb memory to pass TT 70% of the time, with 5 minute conversations. It will be natural to speak of computers thinking. [The machine] may be used to help in making up its own programmes, or to predict the effect of alterations in its own structure. We may hope that machines will eventually compete with men in all purely intellectual fields.
  • Slide 15
  • Some Objections Theological objection: Thinking is part of humans souls, and so animals/machines cant think. Head-in-the-sand objection: Consequences of thinking machines are dreadful, so lets hope its not possible. Futuristic movies and books build upon this fear. Machines will never be able to do X. X = {be kind, friendly, have sense of humor, fall in love, etc.}
  • Slide 16
  • Mathematical Objection Gdels Incompleteness Theorem: in any consistent logical system that includes number theory, there are statements that cant be proved or disproved The halting problem: no machine can determine whether another machine will halt on a given input These show limitations to discrete-state machines But humans are not infallible Judge of the imitation game will not know if incorrect response is because of limitation or human error.
  • Slide 17
  • Consciousness Not until a machine can write a sonnetbecause of thoughts and emotions feltcould we agree that machine equals brain Professor Jefferson Really just attack on TT, but TT does not test whether computer thinks or feels. Solipsism: the only way to really know if a machine is thinking is to be the machine.
  • Slide 18
  • Lady Lovelaces Objection Wrote about Babbages Analytical Engine. Machine can not originate anything, and only does what it is programmed to do. But what about learning machines? Maybe machines cant surprise? But then again, humans often are surprised by machines. In addition, what is surprise? Theorems may not be surprising after they are proven, but is there no virtue in proving them?
  • Slide 19
  • Continuity of the Nervous System Nervous system is not a discrete-state machine, so cant mimic by computer. Again, interrogator cant tell difference
  • Slide 20
  • Extra-Sensory Perception Seems to acknowledge overwhelming statistical evidence for telepathy. Imitation game fails with ESP, since human can communicate with interrogator via ESP. Telepathic human is better at guessing games (i.e. which hand is coin in?) To solve this, Turing suggests putting subjects in telepathy-proof room.
  • Slide 21
  • Learning Machines Presumably the child brain is something like a notebook as one buys it from the stationers. Rather little mechanism, and lots of blank sheets. Replicate child brain, and then feed it information. Gives estimate of amount of storage in human brain: 10 9 decimal digits. Much less than currently believed. Believes that once memory is available, constructing a computer with a human-like mind is mainly one of programming. Even discusses ways of teaching computer.
  • Slide 22
  • Later debate on the TT Stuart Shiebers analogy: Deniers: intelligence is like bad cold. There is a germ, a hidden cause. Cant fake it. Approvers: intelligence is like fluency in Italian. Talk to someone in Italian for an hour. Cant say, he doesnt really know Italian, hes just faking it. Now say someone gets good grades, does well on Psychometry Can you say, Hes not really intelligent, hes just faking it to get into a good University?
  • Slide 23
  • Searles Chinese Room Thought experiment, 1980. Refined consciousness objection. There is a room, with a man who only speaks English. Man has book, with instructions: given some scribble in Chinese, output this scribble. A man fluent in Chinese sends messages (in Chinese) into room, and gets responses (also in Chinese). He cant distinguish b/w man in room and fluent Chinese speaker. But does this mean the room knows Chinese?? Conclusion: TT only tests for weak AI, not strong AI.
  • Slide 24
  • Psychologism and Behaviorism Ned Block, 1981. Intelligence cant be based only on behavior TT does not demonstrate general capacity of machine for producing reasonable responses Even a mindless machine can pass TT: Have all possible conversations of given length in memory Machine just looks up correct response Clearly not intelligent For intelligence, need capacity and compactness: No exponential blow-up in storage
  • Slide 25
  • TT Variations Harnads Total Turing Test: same as TT, but machine has to respond to all inputs, not just verbal. Needs robot with sensorimotor capabilities Wattss Inverted Turing Test: roles reversed. Computer shouldnt be able to distinguish its own outputs from those of a human. Schweizers Truly Total Turing Test: machines shouldnt just be able to converse or play chess, but develop language and invent chess. Subject Matter Expert Turing Test: test only in some field.
  • Slide 26
  • TT as Interactive Proof Shiebers argument in favor of TT, against Block: Block: Intelligence is capacity to produce sensible verbal responses to verbal stimuli without exponential storage Shieber: TT does test for this! Conventional proof: prover P sends proof of assertion to verifier V, who verifies correctness. IP adds interaction and randomness Interaction: many rounds of message-passing Randomness: V may use random bits in message Also, V approves, but possibly with some small chance of error
  • Slide 27
  • Interactive Proof of Capacity TT as proof of capacity capacity to produce sensible responses to stimuli Consider space of sequences of verbal stimuli A machine/person has capacity if it answers correctly on, say, 50% of space Now run k tests, and say subject passes 75% of t