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Information and Intelligence

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If we were to try to construct an intelligent machine, what, in very broad terms, would we be aiming to do? It can be argued that the answer is (roughly speaking) to compress data as efficiently as possible. This leads to the idea of “Kolmogorov Complexity” (and related measures), which also arises as the universal prior for probabilistic inference.

For fun, consider the following question: Suppose someone said that in one hour’s time you could have 1 second of computing time on a computer which executed 10 instructions per second, had a similarly large amount of memory, and started with a copy of the internet and all the libraries of the world (and possibly some other real world data) in its memory. At the end of the 1 second you get to keep a reasonable amount of output (say 1012 bytes). What program would you write? What would your answer be if the computer started off with a blank memory?

Some background reading for those interested:

http://www.idsia.ch/juergen/loconet/nngen.html http://www.geocities.com/jim_bowery/cprize.html http://people.cs.uchicago.edu/fortnow/papers/soph.pdf http://citeseer.ist.psu.edu/gacs93lecture.html http://homepages.cwi.nl/~paulv/papers/algorithmicstatistics.pdf

This talk is part of the Inference Group series.

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