**Algorithmic Information Theory**

"Randomness and Proof" is a nice, introductory-level article on algorithmic information theory. It's by G. J. Chaitin, who has personally done a lot of the foundational work in this area. A random, interesting snippet:

Solomonoff represented a scientist's observations as a series of binary digits. The scientist seeks to explain these observations through a theory, which can be regarded as an algorithm capable of generating the series and extending it, that is, predicting future observations. For any given series of observations there are always several competing theories, and the scientist must choose among them. The model demands that the smallest algorithm, the one consisting of the fewest bits, be selected. Stated another way, this rule is the familiar formulation of Occam's razor: Given differing theories of apparently equal merit, the simplest is to be preferred.Thus in the Solomonoff model a theory that enables one to understand a series of observations is seen as a small computer program that reproduces the observations and makes predictions about possible future observations. The smaller the program, the more comprehensive the theory and the greater the degree of understanding. Observations that are random cannot be reproduced by a small program and therefore cannot be explained by a theory. In addition the future behavior of a random system cannot be predicted. For random data the most compact way for the scientist to communicate his observations is for him to publish them in their entirety.

I'm writing a paper on Kolmogorov complexity for a class in complexity theory I'm taking this semester -- if I get a chance, I'll try to blog about some of the fascinating ideas in this field.