The Turing Test! and What Computer Science Offers to Cognitive Science "

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1 The Turing Test and What Computer Science Offers to Cognitive Science " Profs. Rob Rupert and Mike Eisenberg T/R 11-12:15 Muenzinger D430

2 The Imitation Game Q: Please write me a sonnet on the subject of the Forth Bridge." A: Count me out on this one. I never could write poetry." Q: Add to " A: (Pause about 30 seconds and then give as answer) " Q: Do you play chess?" A: Yes." Q: I have K at my K1, and no other pieces. You have only K at K6 and R at R1. It is your move. What do you play?" A: (After a pause of 15 seconds) R-R8 mate."

3 Some Questions to Consider The pragmatic question (What makes passing this test so hard?) The flip side of the pragmatic question (What makes passing some versions of this test so weirdly easy?) False negatives? (Thinking beings that fail the Turing Test) False positives? (Non-thinking things that pass)

4 J. Weizenbaum s ELIZA program (1966) A typical conversation is the following: 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 He says I'm depressed much of the time. I AM SORRY TO HEAR YOU ARE DEPRESSED It's true. I am unhappy DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY I need some help, that much seems certain. WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP Perhaps I could learn to get along with my mother. TELL ME MORE ABOUT YOUR FAMILY

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6 [10:32:24] Alice:Hello, my name is Alice and I am the human. [10:34:40] Judge 3:what is your profession? [10:35:15] Alice:I am chat person. My job is chatting online. As you are sitting in front of your computer screen, you may be wondering if I am a computer or a person talking. [10:35:45] Judge 3:could we chat about windsurfing? [10:36:29] Alice:Do you mean you and me? Sure I can talk about it. windsurfing is quite an interesting topic. As you hear the sound of keys typing you may begin to feel more comfortable chatting with me. [10:36:40] Judge 3:what kind of topic is windsurfing exactly? [10:37:15] Alice:Precisely. It seemed relevant. The feel of the keys on the tips of your fingers may remind you of some questions you wanted to ask me about. [10:37:53] Judge 3:sorry but you didn't answer the question. can one eat a windsurfing? [10:38:51] Alice:There is no need to apologize. Try asking me the question again in simpler terms. Theoretically, I suppose one could. You may be seated alone in a room, or together with a group of other people using computers. You may be judging me.

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8 A Few Questions (Besides Turing s) The computational metaphor, and the embodied mind objection The necessity of evolution objection

9 Searle s Chinese Room

10 Replies to Searle s Thought Experiment The Systems Reply The Robot Reply The Brain-Simulator Reply The Combination Reply The Other-Minds Reply The Many-Mansions Reply

11 Some Additional Things to Consider about the Chinese Room Degrees of consciousness (animals? That thermostat again?) The danger of common sense argument

12 An alternative way of thinking about the computational metaphor : direct physical combinations of computational and biological materials

13 If Machines Can t Think, What Good Is the Computational Metaphor?

14 What Does It Mean for a Problem to Be Hard? Some problems may not have a solution, or may simply be ill-defined. Examples: "Write a computer program that will pass the Turing test. (Turing) "Define (and/or teach) virtue. (Plato) Some problems may be simply impossible given the resources. (Unlike the first class of problems, we at least know that these are both well-defined and unsolvable.) Examples: "Using a straightedge and compass, and given an angle theta, "construct an angle of magnitude theta/3. "Write a program which, given any computer program P and "number N as input, determines whether P will ever halt when "run on input N. (Turing)

15 Some problems may be impossible to solve with complete accuracy, but they can be approached by using approximations, guesswork, or heuristics. What this means is that perhaps all the "solutions" will be wrong (and we try to make most of the solutions as "right" as possible); or it might mean that some solutions, but not all, will be right. Examples: "Given a two-dimensional scene projected on a retina or "camera plane, deduce the three-dimensional scene (set of "objects) that produced this two-dimensional projection. "Given a finite set of sentences, determine the formal "structure of a context-free grammar that generated those "sentences. "

16 Some problems may be defined in such a way that they can only (or best) be approached by techniques that incorporate some notion of uncertainty, probability, or vagueness: Examples: "Was there life on Mars at some past time? "If I have to place a bet on a future event (e.g., whether the " "Rockies will win the pennant in 2013), how should I bet? "Is this object (person, animal) a threat? "Is this shape: 0 an ellipse? Is it "close" to an ellipse? Some problems may be completely solvable in principle; we could even write an algorithm to solve them. But this algorithm would take so long to run (or equivalently would require so much space) in most "standard" cases that we are forced to use more approximate (and hence unsure) means to approach the problem. "Given a configuration of a chess board, find the best move "for the player whose turn it is. "Given a map of the U.S., and 100 cities (including Boulder), find "the shortest "complete tour" of the cities, beginning and ending "in Boulder, and visiting each of the other cities exactly once. "

17 Some problems fortunately are "easy" in the sense that we can write a program to solve them, and the program will typically run in a reasonable time. "Given a positive number, find its square root. "Given a context-free grammar G, produce a sentence " "using that grammar. "Given 100 linear equations in 100 variables, determine " "whether those equations have a solution, and if so, " "it is. "

18 Computational Notions of Hardness of Problems Order-of-growth notation: how does a problem s difficulty scale up to the large cases? Polynomial vs. exponential-time problems NP-Complete problems Parallelism as a general strategy

19 Problem-Solving as a Subject Matter for Cognitive Science Tendency toward general-purpose strategies (as opposed to domain-specific or modular mechanisms) Evolutionarily recent Close in spirit to Gardner s mainstream cognitive science of the mid-1980 s, in that problems of this type have relatively little in the way of an affective component

20 Problem Solving versus... Judgment and decision-making Acquisition of skills/expertise Other areas of cognition (e.g., vision, language acquisition...)

21 Problem Spaces (or, the Puzzle Approach) The idea of a problem space " Some tractable examples of problem spaces" Search strategies: how to explore a problem space" What makes for a good search strategy?" Going beyond puzzles"

22 A Problem Space consists of: A set of states (reflecting the state of the problem, or more powerfully, what your knowledge of the problem is)" A set of operators: actions (computational or physical) that change one state to another, or move from one state to another." A specific instance of a problem will also include:" "a start state" "one or more goal states" "additional information about the structure of the problem such as constraints on operators; reversibility or cost of moves; expectations about the ease or difficulty of finding a solution; ways of evaluating how close we are to a solution; and so on. " "

23 Often a problem space is represented as a graph in which the nodes are knowledge states, and operators are directed edges that link one state to another. The problem space graph may also be represented in tree form, in which the root of the tree is the initial state, and the successive layers of the tree represent all those states that we can get to within one (two, three ) moves. "

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