Connectionist symbol processing: any progress?

[Michael Cohen]

Eytan Ruppin wrote:

>   There is really no ``contradiction in terms'' here. Indeed, associative
> memory networks (or attractor neural networks) can activate only one
> stored concept at a time. However, such networks should not be viewed as
> representing the whole brain but should be (and indeed
> are) viewed as representing modular cortical structures such as columns.
> Given this interpretation, the problem is resolved; If these networks are
> sufficiently loosely coupled then many patterns can be
> activated together, resulting in complex and rich dynamics.
>
>  We should be careful before discarding our models on false grounds. We have
> too few viable models that can serve as paradigms of information processing.
>
>   Best wishes,
>
>   Eytan Ruppin.

I think the real question is what substantial validated progress has been made
over anabove Formal Language // Transformational Grammar // Standard Artificial
Intelligence on
aspects of human language processing or parsing via connectionists methods.
Have we better understood technological projects such as machine translation or
semantic processing or have hard experimental evidence that these techniques are
valuable in understanding what humans do.  If not other ideas had best be
persued, if so then we should keep on plugging away at the problem using these
techniques.

Its not whether in principle something can be stated using a network automaton.
Its whether the language is good for the problem at hand.  Surely Thermodynamics
is Turing Computable, however Turing Machines alas are good in and of themselves
for almost nothing save producing Models of Computability in which to guage
complexity of Algorithms.
                --mike
--
Michael Cohen mike at cns.bu.edu
Associate Professor, Center for Adaptive Systems
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