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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agi
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