21 Facts About Ray Solomonoff

Ray Solomonoff was the inventor of algorithmic probability, his General Theory of Inductive Inference, and was a founder of algorithmic information theory.

Ray Solomonoff was an originator of the branch of artificial intelligence based on machine learning, prediction and probability.

Ray Solomonoff circulated the first report on non-semantic machine learning in 1956.

Ray Solomonoff founded the theory of universal inductive inference, which is based on solid philosophical foundations and has its root in Kolmogorov complexity and algorithmic information theory.

Ray Solomonoff was born on July 25,1926, in Cleveland, Ohio, son of Jewish Russian immigrants Phillip Julius and Sarah Mashman Solomonoff.

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In 1956 Minsky and McCarthy and others organized the Dartmouth Summer Research Conference on Artificial Intelligence, where Ray Solomonoff was one of the original 10 invitees—he, McCarthy, and Minsky were the only ones to stay all summer.

Ray Solomonoff wanted to pursue a bigger question, how to make machines more generally intelligent, and how computers could use probability for this purpose.

Ray Solomonoff was one of the 10 attendees at the 1956 Dartmouth Summer Research Project on Artificial Intelligence.

Ray Solomonoff wrote and circulated a report among the attendees: "An Inductive Inference Machine".

Ray Solomonoff called this new form of probability "Algorithmic Probability" and showed how to use it for prediction in his theory of inductive inference.

Ray Solomonoff showed and in 1964 proved that the choice of machine, while it could add a constant factor would not change the probability ratios very much.

Algorithmic Probability and Universal Induction became associated with Ray Solomonoff, who was focused on prediction — the extrapolation of a sequence.

Later in the same 1960 publication Ray Solomonoff describes his extension of the single-shortest-code theory.

Ray Solomonoff enlarged his theory, publishing a number of reports leading up to the publications in 1964.

The 1964 papers give a more detailed description of Algorithmic Probability, and Ray Solomonoff Induction, presenting five different models, including the model popularly called the Universal Distribution.

Ray Solomonoff wanted to understand the deeper implications of this probability system.

Ray Solomonoff called this the "Conceptual Jump Size" of the problem.

Levin's search technique approximates this order, and so Ray Solomonoff, who had studied Levin's work, called this search technique Lsearch.

Ray Solomonoff was most recently a visiting professor at the CLRC.

Ray Solomonoff followed this with a short series of lectures, and began research on new applications of Algorithmic Probability.

Algorithmic Probability and Ray Solomonoff Induction have many advantages for Artificial Intelligence.