Information and Intelligence
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If you have a question about this talk, please contact Phil Cowans.
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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