Lukasz Stafiniak wrote in part on Thu 11/8/2007 11:54 AM
LUKASZ ######>> I think the main point is: Bayesian reasoning is about conditional distributions, and Solomonoff / Hutter's work is about conditional complexities. (Although directly taking conditional Kolmogorov complexity didn't work, there is a paragraph about this in Hutter's book.) ED ######>> what is the value or advantage of conditional complexities relative to conditional probabilities? When you build a posterior over TMs from all that vision data using the universal prior, you are looking for the simplest cause, you get "the probability of similar things", because similar things can be simply transformed into the thing under question, moreover you get it summed with the probability of things that are similar in the induced model space. ED ######>> Whats a TM? Also are you saying that the system would develop programs for matching patterns, and then patterns for modifying those patterns, etc, So that similar patterns would be matched by programs that called a routine for a common pattern, but then other patterns to modify them to fit different perceptions? So are the programs just used for computing Kolmogorov complexity or are they also used for generating and matching patterns. Does it require that the programs exactly match a current pattern being received, or does it know when a match is good enough that it can be relied upon as having some significance? Can the programs learn that similar but different patterns are different views of the same thing? Can they learn a generalizational and compositional hierarchy of patterns? Can they run on massively parallel processing. The Hutters expectimax tree appears to alternate levels of selection and evaluation. Can the Expectimax tree run in reverse and in parallel, with information coming up from low sensory levels, and then being selected based on their relative probability, and then having the selected lower level patterns being fed as inputs into higher level patterns and then repeating that process. That would be a hierarchy that alternates matching and then selecting the best scoring match at alternate levels of the hierarchy as is shown in the Serre article I have cited so many times before on this list. LUKASZ ######>> You scared me... Check again, it's like in Solomon the king. ED######>> Thanks for the correction. After I sent the email I realized the mistake, but I was too stupid to parse it as Solomon-(the King)-off. I was stuck in thinking of Sol-om-on-on-off, which is hard to remember and that is why I confused it. Solomon-(the King)-off is much easier to remember. I have always been really bad at names, foreign languages, and particularly spelling. LUKASZ ######>> Yes, it is all about non-literal similarity matching, like you said in later post, finding a library that makes for very short codes for a class of similar things. ED######>> are these short codes sort of like Wolfram little codelettes, that can hopefully represent complex patterns out of very little code, or do they pretty much represent subsets of visual patterns as small bit maps. Ed Porter -----Original Message----- From: Lukasz Stafiniak [mailto:[EMAIL PROTECTED] Sent: Thursday, November 08, 2007 11:54 AM To: [email protected] Subject: Re: [agi] How valuable is Solmononoff Induction for real world AGI? On 11/8/07, Edward W. Porter <[EMAIL PROTECTED]> wrote: > > > > VLADIMIR NESOV IN HIS 11/07/07 10:54 PM POST SAID > > VLADIMIR>>>> "Hutter shows that prior can be selected rather > VLADIMIR>>>> arbitrarily > without giving up too much" BTW: There is a point in Hutter's book that I don't fully understand: the belief contamination theorem. Is the contamination reintroduced at each cycle in this theorem? (The only way it makes sense.) > > (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.) Yes. The choice of Solomonoff and Hutter is to take a distribution over all computable things. > > It seems to me that when you don't 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. > I think the main point is: Bayesian reasoning is about conditional distributions, and Solomonoff / Hutter's work is about conditional complexities. (Although directly taking conditional Kolmogorov complexity didn't work, there is a paragraph about this in Hutter's book.) When you build a posterior over TMs from all that vision data using the universal prior, you are looking for the simplest cause, you get "the probability of similar things", because similar things can be simply transformed into the thing under question, moreover you get it summed with the probability of things that are similar in the induced model space. > > ED>>>> So, from a practical standpoint, which is all I really care > ED>>>> 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) You scared me... Check again, it's like in Solomon the king. > 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, isn't it brain dead? And if it is a brain dead why is > such a bright guy as Shane Legg spending his time on it. > Yes, it is all about non-literal similarity matching, like you said in later post, finding a library that makes for very short codes for a class of similar things. OK I must post now or I'll get lost in other posts ;-) ----- 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/?&; ----- 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=63189360-e35c5f
