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In my 1993 book "The Structure of Intelligence" (SOI), I presented a formal definition of intelligence as "the ability to achieve complex goals in complex environments." I then argued (among other things) that pattern recognition is the key to achieving intelligence, due to the algorithm The subtle question in this kind of definition is: How do you average over the space of goals and environments? If you average over all possible goals and environments, weighting each one by their complexity perhaps (so that success with simple goals/environments is rated higher), then you have a definition of "how generally intelligent a system is," where general intelligence is defined in an extremely mathematically inclusive way. The line of thinking I undertook in SOI was basically a reformulation in terms of "pattern theory" of ideas regarding algorithmic information and intelligence that originated with Ray Solmonoff; and Solomonoff's ideas have more recently been developed by Shane Legg and Marcus Hutter into a highly rigorous mathematical definition of intelligence. I find this kind of theory fascinating, and I'm pleased that Legg and Hutter have done a more thorough job than I did of making a fully formalized theory of this nature.
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The IEET is a 501(c)3 non-profit, tax-exempt organization registered in the State of Connecticut in the United States.
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