- 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 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" .
- A description of algorithmic probability and how it was discovered is Solomonoff's "The Discovery of Algorithmic Probability", Journal of Computer and System Sciences, Vol 55, No. 1, pp 73–88, August 1997. The paper, and the others mentioned here, are available on his website at the publications page.
I'm cutting the above three paragraphs, which were recently added at the same time. The first uses undefined terms and offers no support for its assertions. The second reads like iconoclasm for its own sake. I'll save the reference to the paper, and move it to the earlier paragraph about history. —Tamfang (talk) 03:23, 16 May 2011 (UTC)
- "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