Minds and Machines 17 (4):391-444 (2007)

Authors
Marcus Hutter
Australian National University
Abstract
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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DOI 10.1007/s11023-007-9079-x
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References found in this work BETA

Minds, Brains, and Programs.John R. Searle - 1980 - Behavioral and Brain Sciences 3 (3):417-57.
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Citations of this work BETA

Artificial Intelligence, Values, and Alignment.Iason Gabriel - 2020 - Minds and Machines 30 (3):411-437.
Machines and the Moral Community.Erica L. Neely - 2013 - Philosophy and Technology 27 (1):97-111.
Advantages of Artificial Intelligences, Uploads, and Digital Minds.Kaj Sotala - 2012 - International Journal of Machine Consciousness 4 (01):275-291.

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