Crypto and new computing strategies

[Jim choate]

> 
> 
> 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 at 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