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.
Computing Machinery and Intelligence.Alan M. Turing - 1950 - Mind 59 (October):433-60.
The Mismeasure of Man.Stephen Jay Gould - 1981 - W.W. Norton and Company.
The Society Of Mind.Marvin L. Minsky - 1986 - Simon & Schuster.

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Citations of this work BETA

Artificial Intelligence, Values, and Alignment.Iason Gabriel - 2020 - Minds and Machines 30 (3):411-437.
Reward Is Enough.David Silver, Satinder Singh, Doina Precup & Richard S. Sutton - forthcoming - Artificial Intelligence:103535.
Machines and the Moral Community.Erica L. Neely - 2013 - Philosophy and Technology 27 (1):97-111.

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