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."
"The basic theory...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." In a finite discrete environment ( like Shurdlu: put the red cylinder on top of the blue box ) that may be possible. But in the real world the problem is knowing that one's observations are all - or even a significant percentage - of the possible observations.
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.
TFA is a neat idea theoreretically, but it's progeny will never be able to leave the lab.
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