Editing Ray Solomonoff (section)

== Work history — the later years ==
In other papers he explored how to limit the time needed to search for solutions, writing on resource bounded search.  The search space is limited by available time or computation cost rather than by cutting out search space as is done in some other prediction methods, such as Minimum Description Length.

Throughout his career Solomonoff was concerned with the potential benefits and dangers of A.I., discussing it in many of his published reports.  In 1985 he analyzed a likely evolution of A.I., giving a formula predicting when it would reach the "Infinity Point".<ref>"The Time Scale of Artificial Intelligence: Reflections on Social Effects," Human Systems Management, Vol 5, pp. 149–153, 1985 [http://world.std.com/~rjs/timesc.pdf (pdf version)]</ref>  This work is part of the history of thought about a possible [[Technological Singularity|technological singularity]].

Originally algorithmic induction methods extrapolated ordered sequences of strings.  Methods were needed for dealing with other kinds of data.

A 1999 report,<ref>"Two Kinds of Probabilistic Induction," The Computer Journal, Vol 42, No. 4, 1999. [http://world.std.com/~rjs/compj99.pdf (pdf version)]</ref> generalizes the Universal Distribution and associated convergence theorems to unordered sets of strings and a 2008 report,<ref>"Three Kinds of Probabilistic Induction, Universal Distributions and Convergence Theorems" 2008. [http://world.std.com/~rjs/chris1.pdf (pdf version)]</ref> to unordered pairs of strings.

In 1997,<ref>"The Discovery of Algorithmi Probability," Journal of Computer and System Sciences, Vol 55, No. 1, pp. 73–88 [http://world.std.com/~rjs/barc97.pdf (pdf version)]</ref> 2003 and 2006 he showed that incomputability and subjectivity are both necessary and desirable characteristics of any high performance induction system.

In 1970 he formed his own one man company, Oxbridge Research, and continued his research there except for periods at other
institutions such as MIT, University of Saarland in Germany and the Dalle Molle Institute for Artificial Intelligence in Lugano, Switzerland.  In 2003 he was the first recipient of the Kolmogorov Award by The Computer Learning Research Center at the Royal Holloway, University of London, where he  gave the inaugural Kolmogorov Lecture. Solomonoff was most recently a visiting professor at the CLRC.

In 2006 he spoke at [[AI@50]], "Dartmouth Artificial Intelligence Conference: the Next Fifty Years"  commemorating the fiftieth anniversary
of the original Dartmouth summer study group.  Solomonoff was one of five original participants to attend.

In Feb. 2008, he gave the keynote address at the Conference "Current Trends in the Theory and Application of Computer Science" (CTTACS), held at [[Notre Dame University–Louaize|Notre Dame University]] in Lebanon.  He followed this with a short series of lectures, and began research on new applications of Algorithmic Probability.

Algorithmic Probability and Solomonoff Induction have many advantages for Artificial Intelligence.  Algorithmic Probability gives extremely accurate probability estimates.  These estimates can be revised by a reliable method so that they continue to be acceptable.  It utilizes search time in a very efficient way.  In addition to probability estimates, Algorithmic Probability "has for AI another important value:  its multiplicity of models gives us many different ways to understand our data;

A description of Solomonoff's life and work prior to 1997 is in "The Discovery of Algorithmic Probability", Journal of Computer and System Sciences, Vol 55, No. 1, pp 73–88, August 1997. The paper, as well as most of the others mentioned here, are available on his website at the [http://world.std.com/~rjs/pubs.html publications page].

In an article published the year of his death, a journal article said of Solomonoff: "A very conventional scientist understands his science using a single 'current paradigm'—the way of understanding that is most in vogue at the present time.  A more creative scientist understands his science in very many ways, and can more easily create new theories, new ways of understanding, when the 'current paradigm' no longer fits the current data".<ref>"Algorithmic Probability, Theory and Applications," In Information Theory and Statistical Learning, Eds Frank Emmert-Streib and Matthias Dehmer, Springer Science and Business Media, 2009, p. 11</ref>