(3) I think it is possible to do better in the real world. In the extreme case, a Bayesian superintelligence could use enormously less sensory information than a human scientist to come to correct conclusions. First time you ever see an apple fall down, you observe the position goes as the square of time, invent calculus, generalize Newton's Laws... and see that Newton's Laws involve action at a distance, look for alternative explanations with increased locality, invent relativistic covariance around a hypothetical speed limit, and consider that General Relativity might be worth testing.
Hmm, "real world" and "superintelligence" in the same breath...
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 go...
Today's post, Changing the Definition of Science was originally published on 18 May 2008. A summary (taken from the LW wiki):
Discuss the post here (rather than in the comments to the original post).
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.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.