The real problem here is more serious- even if one grants such a superintelligence, hypothesis space is extremely large. And it isn't clear why a superintelligence would immediately want to look for hypotheses that involved increased locality. Moreover, unless one has a lot more data (like say planetary orbits) one can't even get easy evidence for the idea of an inverse square law for gravitational strength, and that requires very careful observations (to a close approximation all the orbits of major planets are circles. It is only when one has a lot of good data over time that one sees that they are ellipses.) The paragraph and much of the rest of the essay is a combination of failure to appreciate how much information is necessary and a failure to appreciate the incredible size of hypothesis space in a way that seems similar to hindsight bias/ illusion of transparency.
It is only when one has a lot of good data over time that one sees that they are ellipses.
The case of Gauss computing the orbit of Ceres (which I am now surprised to find was not just a case of plug in the data and run least squares over a class of simple orbital models) suggests that intelligence coupled with the determination/capability to work through long chains of computation can substantially reduce the amount of data required for inference.
Today's post, Changing the Definition of Science was originally published on 18 May 2008. A summary (taken from the LW wiki):
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This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was No Safe Defense, Not Even Science, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
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