In Section 2.2.1 of http://www.springerlink.com/content/978-1-4020-5045-9 (also briefly in http://nars.wang.googlepages.com/wang.AGI-CNN.pdf ) I compared the three major traditions of formalization used in AI:
*. dynamical system. In this framework, the states of the system are described as points in a multidimensional space, and state changes are described as trajectories in the space. It mainly comes from the tradition of physics. *. inferential system. In this framework, the states of the system are described as sets of beliefs the system has, and state changes are described as belief derivations and revisions according to inference rules. It mainly comes from the tradition of logic. *. computational system. In this framework, the states of the system are described as data stored in the internal data structures of the system, and state changes are described as data processing following algorithms. It mainly comes from the tradition of computer science. My conclusion is: "In principle, these three frameworks are equivalent in their expressive and processing power, in the sense that a virtual machine defined in one framework can be implemented by another virtual machine defined in another framework. Even so, for a given problem, it may be easier to find solutions in one framework than in the other frameworks. Therefore, the frameworks are not always equivalent in practical applications." Therefore, the problem of using an n-space representation for AGI is not its theoretical possibility (it is possible), but its practical feasibility. I have no doubt that for many limited application, n-space representation is the most natural and efficient choice. However, for a general purpose system, the situation is very different. I'm afraid for AGI we may have to need millions (if not more) dimensions, and it won't be easy to decide in advance what dimensions are necessary. Corpus-based Learning can use this representation because there the dimensions are automatically generated from a corpus, which is available at the beginning. An AGI system cannot assume that, because it has to accept new knowledge (including novel concepts and words) at run time. Can we allow new dimensions be introduced, and old ones deleted, when the system is running? Pei On 11/26/06, J. Storrs Hall, PhD. <[EMAIL PROTECTED]> wrote:
On Saturday 25 November 2006 13:52, Ben Goertzel wrote: > About Teddy Meese: a well-designed Teddy Moose is almost surely going > to have the big antlers characterizing a male moose, rather than the > head-profile of a female moose; and it would be disappointing if a > Teddy Moose had the head and upper body of a bear and the udders and > hooves of a moose; etc. So obviously a simple blend like this is not > just **any** interpolation, it's an interpolation where the most > salient features of each item being blended are favored, wherever this > is possible without conflict. But I agree that this should be doable > within an n-vector framework without requiring any breakthroughs... A little more about this: The salient features of a bear or moose are those that would go into a caricature. (There is also a significant anthropomorphization, a blending in of human characteristics.) It's long been shown that *with the proper mapping*, caricatures can be generated by n-space geometry. You find a point that represents an average of individuals in the class you're interested in, take the individual you're trying to caricature and project further along the line of difference. A classic example is Susan Brennan's caricature generator: Brennan, S. "Caricature Generation: The Dynamic Exaggeration of Faces by Computer." Leonardo 18, No. 3 (1985), 170-178. (an example is shown in http://cogprints.org/172/00/faces1.ps) Another more recent result using an n-space representation (they call it a Vector Space Model) is Turney, Peter D. and Littman, Michael L. (2005) Corpus-based Learning of Analogies and Semantic Relations. Machine Learning 60(1-3):pp. 251-278. (http://cogprints.org/4518/01/NRC-48273.pdf) A follow-on paper (http://arxiv.org/pdf/cs.CL/0412024) is the work that recently got in the news by equalling the performance of college-bound students on verbal-analogy SAT test questions. You can get some help finding the "average animal" and seeing how much human character is mixed in by backtracking from teddy bears. Another approach, just as congenial to my tentative architecture, is to use a memory of a caricature moose, e.g. Bullwinkle. --Josh ----- 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/?list_id=303
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