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: [email protected]
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: [email protected]
> 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
>
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