Algorithmic Probability — Its Discovery — Its Properties and Application to Strong AI

We will first describe the discovery of Algorithmic Probability — its motivation, just how it was discovered, and some of its properties. Section two discusses its Completeness — its consummate ability to discover regularities in data and why its Incomputability does not hinder to its use for practical prediction. Sections three and four are on its Subjectivity and Diversity — how these features play a critical role in training a system for strong AI. Sections five and six are on the practical aspects of constructing and training such a system. The final Section, seven, discusses present progress and open problems.