> Actually, in attractor neural nets it's well-known that using random
> asynchronous updating instead of deterministic synchronous updating does
NOT
> change the dynamics of a neural network significantly.  The attractors are
> the same and the path of approach to an attractor is about the same.  The
> order of updating turns out not to be a big deal in ANN's.  It may be a
> bigger deal in backprop neural nets and the like, but those sorts of
"neural
> nets" are a lot further from anything I'm interested in...

I'd rather get ride of the notion of "attractor" altogether. Though it may
be useful for perception, in high-level cognition I don't see anything like
it. Of course, some beliefs are more stable than others, but are they states
to which all processes converge?

> Hmmm....  Pei, I don't see how to get NARS' truth value functions out of
an
> underlying neural network model.  I'd love to see the details....  If
truth
> value is not related to frequency nor to synaptic conductance, then how is
> it reflected in the NN?

What I mean is not that NARS, as a reasoning system, can be (partially or
completely) implemented by a network, but that NARS can be seen as a
network --- though different from conventional NN.

I think NN is much better than traditional AI in its philosophy --- I like
parallel processing, distributed representation (to a certain extent),
incremental learning, competing results, and so on. However, ironically, the
techniques of NN is less flexible than symbolic AI. I don't like NN when it
uses fixed network topology, has no semantics (and even claims it to be an
advantage), takes the goal of learning as converging to a function (mapping
input to output), does global updating, uses "activation" for both logical
and control purposes, and so on.

My way to combine the two paradigms is not to build a hybrid system that is
part symbolic and part connectionist, but to build a unified system which is
similar to symbolic AI in certain aspects, and similar to NN in some other
aspects.

Pei


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