Jim,

 

In the Riesenhuber and Poggio paper the binding that were handled implicitly
involved spatial relationships, such as an observed roughly horizontal line
substantially touching an observed roughly vertical line at their respective
ends, even though their might be other horizontal and vertical lines not
having this relationship in the input pixel space.  It achieves such
implicit bindings by having enough separate models to be able to detect, by
direct mapping, such a touching relationship between a horizontal and
vertical lines at each of many different locations in the visual input
space.

 

But the Poggio paper deals with a relatively small number of relationships
in a relatively small (160x160) low dimensional (2d) space using 23 million
models.  You imply you have been able to accomplish a somewhat similar
implicit representation of bindings in a much higher dimensional and
presumably large semantic space.  Unfortunately I was unable to understand
from your description how you claimed to have accomplished this.

 

Could you please clarify you description with regard to this point.

 

Ed Porter 

 

-----Original Message-----
From: Jim Bromer [mailto:[EMAIL PROTECTED] 
Sent: Monday, July 14, 2008 1:38 PM
To: agi@v2.listbox.com
Subject: Re: FW: [agi] WHAT PORTION OF CORTICAL PROCESSES ARE BOUND BY "THE
BINDING PROBLEM"?

 

I started reading a Riesenhuber and Poggio paper and there are some
similarities to ideas that I have considered although my ideas were
explicitly developed about computer programs that would use symbolic
information and are not neural theories.  It is interesting that Risesnhuber
and Poggio argued that "the binding problem seems to be a problem for only
some models of object recognition."  In other words, it seems that they are
claiming that the problem disappears with their model of neural cognition! 

The study of feature detectors in cats eyes is old news and I did
incorporate that information into the development of my own theories.

I have often talked about the use of multi-level complex methods and I see
some similarity to the ideas that they discussed to my ideas.  In my model
an input would be scanned for different features using different kinds of
analysis on the input.  So then a configuration of simple features would be
derived from the scan and these could be associated with a number of complex
groups of objects that have been previously associated with the features.
Because the complex groups of objects are complexes (in the general sense),
and would be learned by previous experience, they are not insipidly modeled
on one standard model. These complex objects are complex in that they are
not all cut from one standard.  The older implementations that used
operations that were taken from set theory on groups were set on object
models that were very old-world and were not derived from learning.  For
example they were non-experiential. (I cannot remember the term that I am
looking for but experiential is the anthropomorphic term).  All of the
groupings in old models that looked for intersections were of a few
predefined kinds, and most significantly they did not recognize that
ideologically incommensurable references could affect meaning (or effect)
even if the references were strongly associated and functionally related.
The complex groupings of objects that I have in mind would have been derived
using different methods of analysis and combination and when a group of them
is called from an input analysis their use should tend to narrow the objects
that might be expected given the detection by the feature detectors.
Although I haven't expressed myself very clearly, this is very similar to
what Riesenhuber and Poggio were suggesting that their methods would be
capable of. So, yes,I think some similar methods can be used in NLP.

However, my model also includes the recognition that comparing apples and
oranges is not always straight forward.  This gives you an idea of what I
mean by ideologically incommensurable associations. If I were to give some
examples, a reasonable person might simply assume that the problems
illustrated by the examples could easily be resolved with more information,
and that is true.  But the point that I am making is that this view of
ideologically incommensurable references can be helpful in the analysis of
the kinds of problems that can be expected from more ambitious AI models.

Jim Bromer

 

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