Progress In Incremental Machine Learning

We will describe recent developments in a system for machine learning that we’ve been working on for some time (Sol 86, Sol 89). It is meant to be a “Scientist’s Assistant” of great power and versatility in many areas of science and mathematics. It differs from other ambitious work in this area in that we are not so much interested in knowledge itself, as we are in how it is acquired how machines may learn. To start off, the system will learn to solve two very general kinds of problems. Most, but perhaps not all problems in science and engineering are of these two kinds. The first kind is Function Inversion. These are the P and NP problems of computational complexity theory. They include theorem proving, solution of equations, symbolic integration, etc. The second kind of problem is Time Limited Optimization. Inductive inference of all kinds, surface reconstruction, and image restoration are a few examples of this kind of problem. Designing an automobile in 6 months satisfying certain specifications and having minimal cost, is