Self-improving AI: an Analysis [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Minds and Machines 17 (3):249-259 (2007)
Self-improvement was one of the aspects of AI proposed for study in the 1956 Dartmouth conference. Turing proposed a “child machine” which could be taught in the human manner to attain adult human-level intelligence. In latter days, the contention that an AI system could be built to learn and improve itself indefinitely has acquired the label of the bootstrap fallacy. Attempts in AI to implement such a system have met with consistent failure for half a century. Technological optimists, however, have maintained that a such system is possible, producing, if implemented, a feedback loop that would lead to a rapid exponential increase in intelligence. We examine the arguments for both positions and draw some conclusions.
|Keywords||Artificial intelligence Autogeny Bootstrap fallacy Complexity barrier Learning Self-improving|
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Pierre De Loor, Kristen Manac’H. & Jacques Tisseau (2009). Enaction-Based Artificial Intelligence: Toward Co-Evolution with Humans in the Loop. [REVIEW] Minds and Machines 19 (3):319-343.
Roman Yampolskiy & Joshua Fox (2013). Safety Engineering for Artificial General Intelligence. Topoi 32 (2):217-226.
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