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	<title>Ray Solomonoff - Revision history</title>
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	<updated>2026-09-15T06:37:03Z</updated>
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		<title>imported&gt;ALT-TEXT: Import: 52 additional AI people glossary entries</title>
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		<updated>2026-09-07T20:10:00Z</updated>

		<summary type="html">&lt;p&gt;Import: 52 additional AI people glossary entries&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;== Ray Solomonoff ==&lt;br /&gt;
An American mathematician who attended the 1956 Dartmouth workshop and went on to invent algorithmic probability, assigning to any observed sequence a prior probability based on the length of the shortest program that could produce it. Combined with Bayes&amp;#039; rule, this yields a formal theory of universal inductive inference: the best prediction is the simplest explanation consistent with the data. Developed alongside Kolmogorov and Chaitin&amp;#039;s work on algorithmic information theory, it is the sharpest mathematical statement of Occam&amp;#039;s razor and an idealised limit for what any learning system could achieve. (See also: [[Thomas Bayes]], [[Machine learning]], [[Overfitting]])&lt;br /&gt;
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