🚀 go-pugleaf

Erik Vick wrote:
> 
> Louis Savain wrote:
> >
> > In article <343AAFF1.6D35@dial.pipex.com>, Howard Snart
> > <aac88@dial.pipex.com> wrote:
> >
> > >[...]
> > >This approach stems from several misgivings I have over the existing
> > >methods of teaching neural nets... most significantly
> > >backtracking.(which I see as un-scalable, and fundementally wrong... you
> > >force the outputs to conform, whereas the problem may lie far from the
> > >outputs)
> >
> >   I agree.  Traditional neural nets are not biologically plausible,
> > nor do they do the kind of learning that is needed to gain common
> > sense.
> >

	I disagree. I feel that the formation of a topographical map
of the environment is obvious starting in Striata cortex area 17 (field
of vision division, 
edge detection), 18 and 19 (starting three dimensional mappings with
stereoscopic 
details. Where the corpus colosum begins to wire both hemisphere
together).

	All of this structure and organization within the brain can be
explained
 with mechanism which the neuron does posses. The mechanism seem to
contain some 
very simple functionality which parallels the learning process that
back-prop
engages. This is only one very insignificant aspect of computing ability 
which neurons do posses. In fact Back-Prop can be broken down 
into a very basic model that neuron could employ quite easily.

We are not ignoring this are we?

	The drift of gaze through the center mass of a pattern,
this with the method area 17 and 18 divide the visual pattern can
minimize the non linearity of the sensed 2-D space information, by the
way.
Which can also introduce size invariance and the sense of rotational
invariance of patterns to the degree which the brain does hold. Just
modeling
concepts. Many of these concepts which arise in the brain can be
attribute to
these simple computing ideas as the numerous neural networking papers
have 
suggested.

	Actually the Back Prop model is a very simplified model of a neuron.
Yet it can mirror a portion of the computing ability the neuron
contains. One 
that does not require the back prop algorithm to exist as a subroutine.
It has the possibility to be mirrored because of the nature of patterns
themselves.
 
	  When adding up all the other variables which the various real neuron 
structures present throughout the brain they can really add up. I am
actually 
very surprised how obvious the computational mechanism of the brain are.
The
problem I see is there vast variations. The many different structures
are
enough to take quite a while to familiarize ones self with.

Opinion, "obviously common sense lies in
the brains ability to model and communicate internally in
some modeling mode.

Failure in most systems to communicate with us as we do with each other
seem to lie in their inability to track time, and model space. 

EXAMPLE, Go ask Alice here. See if it can yet understand
any sense of time or space or both together."

http://alice.eecs.lehigh.edu:1991/

A simple ability to extend Alice's ideas of
time and space with some fuzzy measure (or Statistical) internal model.
I believe would give grand changes in the intelligibility
of its conversation.

Probably mine too. 
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