--- On Sat, 9/20/08, Mike Tintner <[EMAIL PROTECTED]> wrote: > Matt: A more appropriate metaphor is that text compression > is the altimeter > by which we measure progress. (1) > > Matt, > > Now that sentence is a good example of general intelligence > - forming a new > connection between domains - altimeters and progress. > > Can you explain how you could have arrived at it by > > A)logic ( incl. Nars or PLN or any other kind) > B)mathematics > > or how you would *understand* it by any means of > > C) text compression, > D) predictive analysis of sentences/texts in Google. > > Can you explain how any of the rational systems, currently > being discussed > here, can be applied to any problem of general intelligence > whatsoever?
Certainly. A metaphor is a type of analogy: "intelligence is to compression as flight is to ___?" The general form is "A is to B as C is to X", and solve for X. Roughly, the solution is X = B + C - A where A, B, C, and X are vectors in semantic space, i.e. rows of a matrix M such that M[i,j] is the probability of words i and j appearing near each other (e.g. in the same paragraph or document) in a large text corpus. Variations of this technique very nearly equal human performance on the analogy section of the college SAT exam. http://aclweb.org/aclwiki/index.php?title=SAT_Analogy_Questions The leading contender is latent relational analysis (LRA), which means applying the above equation to a matrix M that has been compressed using singular value decomposition (SVD). SVD consists of factoring the matrix M = USV, where U and V are orthonormal and S is diagonal (the eigenvalues), then tossing out all but the largest elements of S. This allows U and V to be reduced from, say, 20000 x 20000 to 20000 x 200. SVD in effect applies the transitive property of semantic relatedness, the notion that if A is near B and B is near C, then A is near C. Gorrell gives an efficient algorithm for computing the SVD using a neural network. In this example, the network would be 20000 x 200 x 20000 where U and V are the weight matrices and the retained elements of S are the hidden units. Hidden units are added as training proceeds, such that the size of the weight matrices is approximately the size of the text corpus read so far. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.7961 To answer your question, this is most like (D), predictive analysis of text, and would be a useful technique for text compression. -- Matt Mahoney, [EMAIL PROTECTED] ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=114414975-3c8e69 Powered by Listbox: http://www.listbox.com
