--- On Thu, 9/4/08, Pei Wang <[EMAIL PROTECTED]> wrote: > I guess you still see NARS as using model-theoretic > semantics, so you > call it "symbolic" and contrast it with system > with sensors. This is > not correct --- see > http://nars.wang.googlepages.com/wang.semantics.pdf and > http://nars.wang.googlepages.com/wang.AI_Misconceptions.pdf
I mean NARS is symbolic in the sense that you write statements in Narsese like "raven -> bird <0.97, 0.92>" (probability=0.97, confidence=0.92). I realize that the meanings of "raven" and "bird" are determined by their relations to other symbols in the knowledge base and that the probability and confidence change with experience. But in practice you are still going to write statements like this because it is the easiest way to build the knowledge base. You aren't going to specify the brightness of millions of pixels in a vision system in Narsese, and there is no mechanism I am aware of to collect this knowledge from a natural language text corpus. There is no mechanism to add new symbols to the knowledge base through experience. You have to explicitly add them. > You have made this point on "CPU power" several > times, and I'm still > not convinced that the bottleneck of AI is hardware > capacity. Also, > there is no reason to believe an AGI must be designed in a > "biologically plausible" way. Natural language has evolved to be learnable on a massively parallel network of slow computing elements. This should be apparent when we compare successful language models with unsuccessful ones. Artificial language models usually consist of tokenization, parsing, and semantic analysis phases. This does not work on natural language because artificial languages have precise specifications and natural languages do not. No two humans use exactly the same language, nor does the same human at two points in time. Rather, language is learnable by example, so that each message causes the language of the receiver to be a little more like that of the sender. Children learn semantics before syntax, which is the opposite order from which you would write an artificial language interpreter. An example of a successful language model is a search engine. We know that most of the meaning of a text document depends only on the words it contains, ignoring word order. A search engine matches the semantics of the query with the semantics of a document mostly by matching words, but also by matching semantically related words like "water" to "wet". Here is an example of a computationally intensive but biologically plausible language model. A semantic model is a word-word matrix A such that A_ij is the degree to which words i and j are related, which you can think of as the probability of finding i and j together in a sliding window over a huge text corpus. However, semantic relatedness is a fuzzy identity relation, meaning it is reflexive, commutative, and transitive. If i is related to j and j to k, then i is related to k. Deriving transitive relations in A, also known as latent semantic analysis, is performed by singular value decomposition, factoring A = USV where S is diagonal, then discarding the small terms of S, which has the effect of lossy compression. Typically, A has about 10^6 elements and we keep only a few hundred elements of S. Fortunately there is a parallel algorithm that incrementally updates the matrices as the system learns: a 3 layer neural network where S is the hidden layer (which can grow) and U and V are weight matrices. [1]. Traditional language processing has failed because the task of converting natural language statements like "ravens are birds" to formal language is itself an AI problem. It requires humans who have already learned what ravens are and how to form and recognize grammatically correct sentences so they understand all of the hundreds of ways to express the same statement. You have to have human level understand of the logic to realize that "ravens are coming" doesn't mean "ravens -> coming". If you solve the translation problem, then you must have already solved the natural language problem. You can't take a shortcut directly to the knowledge base, tempting as it might be. You have to learn the language first, going through all the childhood stages. I would have hoped we have learned a lesson from Cyc. 1. Gorrell, Genevieve (2006), "Generalized Hebbian Algorithm for Incremental Singular Value Decomposition in Natural Language Processing", Proceedings of EACL 2006, Trento, Italy. http://www.aclweb.org/anthology-new/E/E06/E06-1013.pdf -- 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=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
