Universal Intelligence: A Definition of Machine Intelligence
Shane Legg,Marcus Hutter +1 more
TLDR
A number of well known informal definitions of human intelligence are taken, and mathematically formalised to produce a general measure of intelligence for arbitrary machines that formally captures the concept of machine intelligence in the broadest reasonable sense.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.read more
Citations
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Human-level control through deep reinforcement learning
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Journal ArticleDOI
Unmasking Clever Hans predictors and assessing what machines really learn.
Sebastian Lapuschkin,Stephan Wäldchen,Alexander Binder,Grégoire Montavon,Wojciech Samek,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +7 more
TL;DR: In this article, the authors apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games, and propose a semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines.
References
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