From: Jim choate <ravage@bga.com>
To: m5@vail.tivoli.com (Mike McNally)
Message Hash: 875288d3e0df02ad86d968bf30689048546a508d0a7dc26fd69ee2cbd0e01695
Message ID: <199403302035.AA07693@zoom.bga.com>
Reply To: <9403301931.AA19705@vail.tivoli.com>
UTC Datetime: 1994-03-30 20:36:20 UTC
Raw Date: Wed, 30 Mar 94 12:36:20 PST
From: Jim choate <ravage@bga.com>
Date: Wed, 30 Mar 94 12:36:20 PST
To: m5@vail.tivoli.com (Mike McNally)
Subject: Re: Crypto and new computing strategies
In-Reply-To: <9403301931.AA19705@vail.tivoli.com>
Message-ID: <199403302035.AA07693@zoom.bga.com>
MIME-Version: 1.0
Content-Type: text
>
>
> Jim choate writes:
> > Also there is the potential to use neural networks at these levels
> > (which are not necessarily reducable to Turing models, the premise
> > has never been proven)
>
> Uhh, gee; given that I've seen neural networks implemented on
> conventional computer systems, and as far as I know those were
> perfectly functional (if slow) neural networks, I think that pretty
> much proves it (as if it needed to be).
>
> I'd say that the burden of proof is to demonstrate that there are
> algorithms implementable on a neural network which are unimplementable
> on a Turing machine. That'd be a pretty significant breakthrough.
>
> > The bottom line is that this whole area is a unknown and if we persist in
> > carrying unproven assumptions from the macro-world over into the QM
> > model we WILL be in for a nasty surprise.
>
> Complexity theory doesn't have anything to do with any world, macro-
> or micro- or mega- or whatever. It's mathematics.
>
> --
> | GOOD TIME FOR MOVIE - GOING ||| Mike McNally <m5@tivoli.com> |
> | TAKE TWA TO CAIRO. ||| Tivoli Systems, Austin, TX: |
> | (actual fortune cookie) ||| "Like A Little Bit of Semi-Heaven" |
>
I use both digital and analog circuits in some of my designs and they are not
necessarily reducable. Just because you can use a neural network to solve a
problem using conventional architecture machines does not a priori prove
anything about the reducability of the technology.
I would have to say that 'spin glass' model neural networks might be such a
model. However, either way you approach it (yours o r mine) it has not been
done and assuming it is the same will lead to some problems.
Complexity theory is mathematics so I would have to say your last assertion
is total drivel.
r
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