Compress Wikipedia and Win AI Prize

Baldrson writes "If you think you can compress a 100M sample of Wikipedia better than paq8f, then you might want to try winning win some of a (at present) 50,000 Euro purse. Marcus Hutter has announced the Hutter Prize for Lossless Compression of Human Knowledge the intent of which is to incentivize the advancement of AI through the exploitation of Hutter's theory of optimal universal artificial intelligence. The basic theory, for which Hutter provides a proof, is that after any set of observations the optimal move by an AI is find the smallest program that predicts those observations and then assume its environment is controlled by that program. Think of it as Ockham's Razor on steroids. Matt Mahoney provides a writeup of the rationale for the prize including a description of the equivalence of compression and general intelligence."

Comments

Re:It's a big world out there

[DrJimbo]

Harmonious Botch said:

This - in humans, at least - can lead to the cyclic reinforcement of one's belief system. The belief system that explains observations initially is used to filter observations later.

I encourage you to read E. T. Jaynes' book: Probability Theory: The Logic of Science. It used to be available on the Web in pdf form before a published version became available.

In it, Jaynes shows that an optimal decision maker shares this same tendency of reinforcing exiting belief systems. He even gives examples where new information reinforces the beliefs of optimal observers who have reached opposite conclusions (due to differing initial sets of data). Each observer believes the new data further supports their own view.

Since even an optimal decision maker has this undesirable trait, I don't think the existence of this trait is a good criteria for rejecting decision making models.