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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Computing Machinery and Intelligence | 5/5 | https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligence | reference | science, encyclopedia | 2026-05-05T16:31:27.598653+00:00 | kb-cron |
Nature of inherent complexity: The child machine could either be one that is as simple as possible, merely maintaining consistency with general principles, or the machine could be one with a complete system of logical inference programmed into it. This more complex system is explained by Turing as "..would be such that the machines store would be largely occupied with definitions and propositions. The propositions would have various kinds of status, e.g., well-established facts, conjectures, mathematically proved theorems, statements given by an authority, expressions having the logical form of proposition but not belief-value. Certain propositions may be described as "imperatives." The machine should be so constructed that as soon as an imperative is classed as "well established" the appropriate action automatically takes place". Despite this built-in logic system the logical inference programmed in would not be one that is formal, rather it would be one that is more pragmatic. In addition the machine would build on its built-in logic system by a method of "scientific induction". Ignorance of the experimenter: An important feature of a learning machine that Turing points out is the ignorance of the teacher of the machines' internal state during the learning process. This is in contrast to a conventional discrete state machine where the objective is to have a clear understanding of the internal state of the machine at every moment during the computation. The machine will be seen to be doing things that we often cannot make sense of or something that we consider to be completely random. Turing mentions that this specific character bestows upon a machine a certain degree of what we consider to be intelligence, in that intelligent behaviour consists of a deviation from the complete determinism of conventional computation but only so long as the deviation does not give rise to pointless loops or random behaviour. The importance of random behaviour: Though Turing cautions us of random behaviour he mentions that inculcating an element of randomness in a learning machine would be of value in a system. He mentions that this could be of value where there might be multiple correct answers or ones where it might be such that a systematic approach would investigate several unsatisfactory solutions to a problem before finding the optimal solution which would entail the systematic process inefficient. Turing also mentions that the process of evolution takes the path of random mutations in order to find solutions that benefit an organism but he also admits that in the case of evolution the systematic method of finding a solution would not be possible. Turing concludes by speculating about a time when machines will compete with humans on numerous intellectual tasks and suggests tasks that could be used to make that start. Turing then suggests that abstract tasks such as playing chess could be a good place to start another method which he puts as "..it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English.". An examination of the development in artificial intelligence that has followed reveals that the learning machine did take the abstract path suggested by Turing as in the case of Deep Blue, a chess playing computer developed by IBM and one which defeated the world champion Garry Kasparov (though, this too is controversial) and the numerous computer chess games which can outplay most amateurs. As for the second suggestion Turing makes, it has been likened by some authors as a call to finding a simulacrum of human cognitive development. Such attempts at finding the underlying algorithms by which children learn the features of the world around them are only beginning to be made.
== See also == History of artificial intelligence
== Notes ==
== References == Brooks, Rodney (1990), "Elephants Don't Play Chess" (PDF), Robotics and Autonomous Systems, 6 (1–2): 3–15, CiteSeerX 10.1.1.588.7539, doi:10.1016/S0921-8890(05)80025-9, retrieved 30 August 2007 Crevier, Daniel (1993). AI: The Tumultuous Search for Artificial Intelligence. New York, NY: BasicBooks. ISBN 0-465-02997-3. Dreyfus, Hubert (1972), What Computers Can't Do, New York: MIT Press, ISBN 978-0-06-011082-6 Dreyfus, Hubert; Dreyfus, Stuart (1986), Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer, Oxford, UK: Blackwell Dreyfus, Hubert (1979), What Computers Still Can't Do, New York: MIT Press. Harnad, Stevan; Scherzer, Peter (2008), "First, Scale Up to the Robotic Turing Test, Then Worry About Feeling", Artificial Intelligence in Medicine, 44 (2): 83–9, CiteSeerX 10.1.1.115.4269, doi:10.1016/j.artmed.2008.08.008, PMID 18930641, archived from the original on 8 February 2012, retrieved 29 August 2010. Haugeland, John (1985), Artificial Intelligence: The Very Idea, Cambridge, Mass.: MIT Press. Moravec, Hans (1976), The Role of Raw Power in Intelligence, archived from the original on 3 March 2016, retrieved 7 November 2007 Hofstadter, Douglas (1979), Gödel, Escher, Bach: an Eternal Golden Braid. Lucas, John (1961), "Minds, Machines and Gödel", in Anderson, A.R. (ed.), Minds and Machines, archived from the original on 19 August 2007, retrieved 2 December 2022 Moravec, Hans (1988), Mind Children, Harvard University Press Penrose, Roger (1989), The Emperor's New Mind: Concerning Computers, Minds, and The Laws of Physics, Oxford University Press, ISBN 978-0-14-014534-2 Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2 Searle, John (1980), "Minds, Brains and Programs" (PDF), Behavioral and Brain Sciences, 3 (3): 417–457, doi:10.1017/S0140525X00005756, S2CID 55303721 Turing, Alan (October 1950), "Computing Machinery and Intelligence" (PDF), Mind, LIX (236): 433–460, doi:10.1093/mind/LIX.236.433 Saygin, A. P. (2000). "Turing Test: 50 years later". Minds and Machines. 10 (4): 463–518. doi:10.1023/A:1011288000451. hdl:11693/24987. S2CID 990084. Noah Wardrip-Fruin and Nick Montfort, eds. (2003). The New Media Reader. Cambridge: MIT Press. ISBN 0-262-23227-8. "Lucasfilm's Habitat" pp. 663–677.
== External links == PDF with the full text of the paper Saygin, Ayse Pinar; Cicekli, Ilyas; Akman, Varol (1999). "An analysis and review of the next 50 years". Minds and Machines: 2000. CiteSeerX 10.1.1.157.1592.