Abstract
This paper revisits the often debated question Can machines think? It is argued that the usual identification of machines with the notion of algorithm has been both counter-intuitive and counter-productive. This is based on the fact that the notion of algorithm just requires an algorithm to contain a finite but arbitrary number of rules. It is argued that intuitively people tend to think of an algorithm to have a rather limited number of rules. The paper will further propose a modification of the above mentioned explication of the notion of machines by quantifying the length of an algorithm. Based on that it appears possible to reconcile the opposing views on the topic, which people have been arguing about for more than half a century.
- Abramson, D. (2008). Turing's responses to two objections. Minds and Machines, 18, 147-167. Google ScholarDigital Library
- Adleman, L. (1979). Time, space and randomness. Technical report, Massachusetts Institute of Technology. MIT/LCS/79/TM-131.Google Scholar
- Church, A. (1936). An unsolvable problem of elementary number theory. American Journal of Mathematics, 58.Google Scholar
- Copeland, B. J. (2002). Hypercomputation. Minds and Machines, 12, 461-502. Google ScholarDigital Library
- Floridi, L., Taddeo, M., & Turilli, M. (2009). Turing's imitation game: Still an impossible challenge for all machines and for some judges--an evaluation of the 2008 Loebner contest. Minds and Machines, 19, 145-150. Google ScholarDigital Library
- Hutter, M. (2005). Universal artificial intelligence: Sequential decisions based on algorithmic probability. Berlin: Springer. Google Scholar
- Legg, S., & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391-444. Google ScholarCross Ref
- Levin, L. A. (1984). Randomness conservation inequalities: information and independence in mathematical theories. Information and Control, 61. Google ScholarDigital Library
- Li, M., Vitányi, P. (2008). An introduction to Kolmogorov complexity and its applications. Text and Monographs in Computer Science, 3rd edn. Berlin: Springer. Google Scholar
- Lucas, J. R. (1961). Minds, machines, and Gödel. Philosophy, 36, 112-117. (rpt. in: Minds and Machines, ed. Alan R. Anderson, Englewood Cliffs, NJ, Prentice-Hall, 1964). Google ScholarCross Ref
- Minsky, M. (1962). Size and structure of universal Turing machines using tag systems. In: Recursive function theory, proceedings, symposium on pure mathematics, Vol. 5, pp. 229-238. American Mathematical Society.Google Scholar
- Penrose, R. (1989). The Emperor's new Mind. Oxford: Oxford University Press.Google Scholar
- Penrose, R. (1994). Shadows of the Mind. Oxford: Oxford University Press.Google Scholar
- Penrose, R. (1996). Beyond the doubting of a shadow. Psyche, 2, 89-129.Google Scholar
- Piccinini, G. (2003). Alan Turing and the mathematical objection. Minds and Machines, 13, 23-48. Google ScholarCross Ref
- Piccinini, G. (2007). Computationalism, the Church-Turing thesis, and the Church-Turing fallacy. Synthese, 154, 97-120.Google ScholarCross Ref
- Post E. L. (1943). Formal reduction of the general combinatorial decision problem. American Journal of Mathematics, 65.Google Scholar
- Sekanina, L. (2007). Evolved computing devices and the implementation problem. Minds and Machines, 17, 311-329. Google ScholarDigital Library
- Shagrir, O. (1997). Two dogmas of computationalism. Minds and Machines, 7, 321-344. Google ScholarCross Ref
- Shagrir, O. (2002). Effective computation by humans and machines. Minds and Machines, 12, 221-240. Google ScholarCross Ref
- Solomonoff, R. J. (1964). Complexity-based induction systems: Comparisons and convergence theorems. Information and Control, 7, 1-22 and 224-254.Google ScholarCross Ref
- Turing, A. M. (1937). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 2(42), 230-265 and (43) 544-546.Google ScholarCross Ref
- Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.Google ScholarCross Ref
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