In response to Pei Wangs post of 10/4/2007 3:13 PM Thanks for giving us a pointer so such inside info.
Googling for the article you listed I found 1. The Logic of Categorization, by PeiWang at http://nars.wang.googlepages.com/wang.categorization.pdf FOR FREE; and 2. A logic of categorization Authors: Wang, Pei; Hofstadter, Douglas; Source: Journal of Experimental & Theoretical Artificial Intelligence <http://www.ingentaconnect.com/content/tandf/teta> , Volume 18, Number 2, June 2006 , pp. 193-213(21) FOR $46.92 Is the free one roughly as good as the $46.92 one, and, if not, are you allowed to send me a copy of the better one for free? Edward W. Porter Porter & Associates 24 String Bridge S12 Exeter, NH 03833 (617) 494-1722 Fax (617) 494-1822 [EMAIL PROTECTED] -----Original Message----- From: Pei Wang [mailto:[EMAIL PROTECTED] Sent: Thursday, October 04, 2007 3:13 PM To: agi@v2.listbox.com Subject: Re: [agi] breaking the small hardware mindset On 10/4/07, Edward W. Porter <[EMAIL PROTECTED]> wrote: > > > > Josh, > > (Talking of "breaking the small hardware mindset," thank god for the > company with the largest hardware mindset -- or at least the largest > physical embodiment of one-- Google. Without them I wouldn't have > known what "FARG" meant, and would have had to either (1) read your > valuable response with less than the understanding it deserves or (2) > embarrassed myself by admitting ignorance and asking for a > clarification.) > > With regard to your answer, copied below, I thought the answer would > be something like that. > > So which of the below types of "representational problems" are the > reasons why their basic approach is not automatically extendable? > > > 1. They have no general purpose representation that can represent > almost anything in a sufficiently uniform representational scheme to > let their analogy net matching algorithm be universally applied > without requiring custom patches for each new type of thing to be > represented. > > 2. They have no general purpose mechanism for determining what are > relevant similarities and generalities across which to allow slippage > for purposes of analogy. > > 3. They have no general purpose mechanism for automatically finding > which compositional patterns map to which lower level representations, > and which of those compositional patterns are similar to each other in > a way appropriate for slippages. > > 4. They have no general purpose mechanism for automatically > determining what would be appropriately coordinated slippages in > semantic hyperspace. > > 5. Some reason not listed above. > > I don't know the answer. There is no reason why you should. But if > you -- or any other interested reader do, or if you have any good > thoughts on the subject, please tell me. I guess I do know more on this topic, but it is a long story for which I don't have the time to tell. Hopefully the following paper can answer some of the questions: A logic of categorization Pei Wang and Douglas Hofstadter Journal of Experimental & Theoretical Artificial Intelligence, Vol.18, No.2, Pages 193-213, 2006 Pei > I may be naïve. I may be overly big-hardware optimistic. But based > on the architecture I have in mind, I think a Novamente-type system, > if it is not already architected to do so, could be modified to handle > all of these problems (except perhaps 5, if there is a 5) and, thus, > provide powerful analogy drawing across virtually all domains. > > Edward W. Porter > Porter & Associates > 24 String Bridge S12 > Exeter, NH 03833 > (617) 494-1722 > Fax (617) 494-1822 > [EMAIL PROTECTED] > > > > -----Original Message----- > From: J Storrs Hall, PhD [mailto:[EMAIL PROTECTED] > Sent: Thursday, October 04, 2007 1:44 PM > To: agi@v2.listbox.com > Subject: Re: [agi] breaking the small hardware mindset > > > > On Thursday 04 October 2007 10:56:59 am, Edward W. Porter wrote: > > You appear to know more on the subject of current analogy drawing > > research than me. So could you please explain to me what are the > > major current problems people are having in trying figure out how to > > draw analogies using a structure mapping approach that has a > > mechanism for coordinating similarity slippage, an approach somewhat > > similar to Hofstadter approach in Copycat? > > > Lets say we want a system that could draw analogies in real time > > when generating natural language output at the level people can, > > assuming there is some roughly semantic-net like representation of > > world knowledge, and lets say we have roughly brain level hardware, > > what ever that is. What are the current major problems? > > The big problem is that structure mapping is brittlely dependent on > representation, as Hofstadter complains; but that the FARG school > hasn't really come up with a generative theory (every Copycat-like > analogizer requires a pile of human-written Codelets which increases > linearly with the knowledge base -- and thus there is a real problem > building a Copycat that can learn its concepts). > > In my humble opinion, of course. > > 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/?& > > ________________________________ > 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/?& ----- 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=50088530-12f519