On 12/18/05, Ben Goertzel <[EMAIL PROTECTED]> wrote:
>
> The way I think about it, a neural net is a dynamical system composed
> of connected components that roughly model neurons.  The system's
> dynamics have got to take place via equations that update the
> quantitative parameters of the simulated neurons and synapses, and the
> system's interface with the outside world has got to take place via
> input and output neurons receiving "raw perceptual stimuli" and
> outputting "raw actuator controls."

This is not that different from what I called a "NN model" in the memo.

> Of course, there are some NN architectures that don't fulfill this
> definition, but IMO they are cheating and are just "NN-ish"...
>
> If an NN architecture like this creates an internal "semantic network"
> via self-organization, that's fine.
>
> Also, if you understood the dynamics of the NN well enough to
> explicitly encode knowledge in the NN in a way that worked effectively
> within its neuronlike dynamics, that would still be within the NN
> paradigm.
>
> Typical semantic network architectures are not based on closely
> neuronlike components, so they don't qualify as NN's in my view.

Agree.

> I believe that probabilistic logic operations can be boiled down to NN
> operations in some way, and I've posted some partial ideas in this
> regard before (Google "Hebbian Logic"), but this line of reasoning has
> not been effectively completed by me or anyone else yet, and IMO until
> one has either
>
> a) theoretically understood how uncertain logic is grounded in NN's, or
>
> b) figured out how the brain represents and manipulates complex knowledge
>
> then as I said I think NN-based AGI is a non-starter.

As I said before, the induction and abduction rule of NARS is not far
from Hebbian learning. What I don't like is just the stuff related to
vector/matrix calculations, conversion to attractors, and so on.

Pei

> -- Ben
>
> -- Ben
>
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