VLADIMIR NESOV IN HIS 11/07/07 10:54 PM POST SAID VLADIMIR>>>> Hutter shows that prior can be selected rather arbitrarily without giving up too much
ED>>>> Yes. I was wondering why the Solomonoff Induction paper made such a big stink about picking the prior (and then came up which a choice that struck me as being quite sub-optimal in most of the types of situations humans deal with). After you have a lot of data, you can derive the equivalent of the prior from frequency data. As the Solmononoff Induction paper showed, using Bayesian formulas the effect of the prior fades off fairly fast as data comes in. (However, I have read that for complex probability distributions the choice of the class of mathematical model you use to model the distribution is part of the prior choosing issue, and can be important but that did not seem to be addressed in the Solomonoff Induction paper. For example in some speech recognition each of the each speech frame model has a pre-selected number of dimensions, such as FFT bins (or related signal processing derivatives), and each dimension is not represented by a Gausian but rather by a basis function comprised of a set of a selected number of Gausians.) It seems to me that when you dont have much frequency data, we humans normally make a guess based on the probability of similar things, as suggested in the Kemp paper I cited. It seems to me that is by far the most commonsensical approach. In fact, due to the virtual omnipreseance of non-literal similarity in everything we see and hear, (e.g., the same face virtually never hits V1 exactly the same) most of our probabilistic thinking is dominated by similarity derived probabilities. BEN GOERTZEL WROTE IN HIS Thu 11/8/2007 6:32 AM POST BEN>>>> [referring the Vlads statement that about AIXIs uncomputability]Now now, it doesn't require infinite resources -- the AIXItl variant of AIXI only requires an insanely massive amount of resources, more than would be feasible in the physical universe, but not an infinite amount ;-) ED>>>> So, from a practical standpoint, which is all I really care about, is it a dead end? Also, do you, or anybody know, if Solmononoff (the only way I can remember the name is Soul man on off like Otis Redding with a microphone problem) Induction have the ability of deal with deep forms of non-literal similarity matching in is complexity calculations. And is so how? And if not, isnt it brain dead? And if it is a brain dead why is such a bright guy as Shane Legg spending his time on it. YAN KINK YIN IN HIS 11/8/2007 9:16 AM POST SAID YAN>>>> Is there any research that can tell us what kind of structures are better for machine learning? Or perhaps w.r.t a certain type of data? Are there learning structures that will somehow "learn things faster"? ED>>>> Yes, brain science. It may not point out the best possible architecture, but it points out one that works. Evolution is not theoretical, and not totally optimal, but it is practical. Systems like Novamente which is loosely based on many key ideas from brain science probably have a much more likely chance of getting useful stuff up and running soon that any more theortical approaches, because the search space has already been narrowed by many trillions of trials and errors over hundreds of millions of years. Ed Porter ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=62880190-75103d
