The Turing Test

1. PDF slide on Turing Test

2. For an overview, see my "Freshman-level lecture notes on AI"

3. For information on the philosophy of AI more generally, see my "Philosophy of AI" webpage.

4. For information on the Chinese Room Argument, see my "Chinese-Room Argument" webpage .

5. Turing, Alan M. (1950), "Computing Machinery and Intelligence", Mind 59: 433-460.
   • HTML version
   • another HTML version
   • Reprinted in:
     • Anderson, Alan Ross (ed.) (1964), Minds and Machines (Englewood Cliffs, NJ: Prentice-Hall): 4-30.
     • Collins, Allan M. & Smith, Edward E. (eds.) (1988), Readings in Cognitive Science (San Mateo, CA: Morgan Kaufmann): 6-19.
     • Haugeland, John (ed.) (1997), Mind Design II: Philosophy, Psychology, Artificial Intelligence; revised and enlarged edition (Cambridge, MA: MIT Press): 29-56.
     • Cummins, Robert, & Cummins, Denise Dellarosa (eds.) (2000), Minds, Brains, and Computers: The Foundations of Cognitive Science (Malden, MA: Blackwell): 153-167.

6. Wilkes, Maurice V. (1953), "Can Machines Think?", Discovery (England) 14 (May): 151ff.
   Reprinted in:
   • Proceedings of the Institute of Radio Engineers 41(10) (1953): 1230-1234.

7. Special Issue on Alan Turing and Artificial Intelligence, Journal of Logic, Language and Information 9(4) (2000).
   Includes:
   • Rapaport, William J. (2000), "How to Pass a Turing Test: Syntactic Semantics, Natural-Language Understanding, and First-Person Cognition", pp.467-490.
     • Reprinted in Moor 2003, below.

     • Abstract: A theory of "syntactic semantics" is advocated as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, as the study of relations between symbols and meanings, can be turned into syntax—a study of relations among symbols (including meanings)—and hence syntax can suffice for the semantical enterprise. (2) Semantics, as the process of understanding one domain modeled in terms of another, can be viewed recursively: The base case of semantic understanding—understanding a domain in terms of itself—is syntactic understanding. (3) An internal (or "narrow"), first-person point of view makes an external (or "wide"), third-person point of view otiose for purposes of understanding cognition.

8. Special Issue on the Turing Test 50 Years Later, Minds and Machines 10(4) (2000).

9. Second Special Issue on the Turing Test 50 Years Later, Minds and Machines 11(1) (2001).

10. Moor, James H. (ed.) (2003), The Turing Test: The Elusive Standard of Artificial Intelligence (Dordrecht: Kluwer).

11. Von Ahn, Luis; Blum, Manuel; & Langford, John (2004), "Telling Humans and Computers Apart Automatically: How Lazy Cryptographers Do AI", Communications of the ACM 47(2) (February): 57-60.

12. Shieber, Stuart M. (2004), The Turing Test: Verbal Behavior as the Hallmark of Intelligence (Cambridge, MA: Harvard University Press).
    1. Rapaport, William J. (2005), Review of Shieber 2004, Computational Linguistics 31(3): 407-412.

    2. Shieber, Stuart M. (2006), "Does the Turing Test Demonstrate Intelligence or Not?", Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-06, Boston). [PDF]

    3. Shieber, Stuart M. (2007), "The Turing Test as Interactive Proof", Noûs 41(4) (December): 686-713.

13. Rapaport, William J. (2006), "The Turing Test", in Keith Brown (ed.), Encyclopedia of Language and Linguistics, 2nd Edition, Vol. 13, pp. 151-159. (Oxford: Elsevier).
    Abstract: This article describes the Turing Test for determining whether a computer can think. It begins with a description of an "imitation game" for discriminating between a man and a woman, discusses variations of the Test, standards for passing the Test, and experiments with real Turing-like tests (including Eliza and the Loebner competition). It then considers what a computer must be able to do in order to pass a Turing Test, including whether written linguistic behavior is a reasonable replacement for "cognition", what counts as understanding natural language, the role of world knowledge in understanding natural language, and the philosophical implications of passing a Turing Test, including whether passing is a sufficient demonstration of cognition, briefly discussing two counterexamples: a table-lookup program and the Chinese Room Argument.