David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Minds and Machines 17 (4):391-444 (2007)
A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.
|Keywords||AIXI complexity theory intelligence theoretical foundations Turing test intelligence tests/measures/definitions|
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Citations of this work BETA
Kaj Sotala (2012). Advantages of Artificial Intelligences, Uploads, and Digital Minds. International Journal of Machine Consciousness 4 (01):275-291.
Roman Yampolskiy & Joshua Fox (2013). Safety Engineering for Artificial General Intelligence. Topoi 32 (2):217-226.
Erica L. Neely (2014). Machines and the Moral Community. Philosophy and Technology 27 (1):97-111.
Achim Hoffmann (2010). Can Machines Think? An Old Question Reformulated. Minds and Machines 20 (2):203-212.
José Hernández-Orallo & David L. Dowe (2013). On Potential Cognitive Abilities in the Machine Kingdom. Minds and Machines 23 (2):179-210.
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