On 30/10/2007, Pei Wang <[EMAIL PROTECTED]> wrote:
> Thanks for the link. I agree that this work is moving in an
> interesting direction, though I'm afraid that for AGI (and adaptive
> systems in general), TM may be too low as a level of description ---
> the conclusions obtained in this kind of work may be correct, but not
> constructive enough.

I'm interested in this sort of work, not to tell me exactly how to
build a system but to give me some way of cutting down the number of
possibile systems. Ideally it would be adopted by the general
community, and AI work might progress more quickly. Hopefully we would
be able to make statements like, "System X only uses a FSM  as the
function F that maps the Input i and work tape memory W on to W and
the output tape O, whereas experiments have shown that some
collections of neural cells can be equivalent to a memory bounded UTM
in expressiveness for F." These kind of statements would allow systems
to be evaluated theoretically without building robots or other
empirical testing methods.

Then the expensive business of narrowing down exactly which system
(and how it should be initially programmed/what knowledge it needs),
could be more tightly focused. Rather than the effort being spread out
all over the place, as it is at the moment.

> Even so, I'll be interested in how far they can
> go.
>
> You may be interested in the works of Peter Kugel

>My own comment on TM is at
http://nars.wang.googlepages.com/wang.computation.pdf


Thanks, I had skimmed your paper before, though they are not quite
where I am looking to go, they are useful different views. I would
also go with an expansion of what computation is, rather than saying
AI is non-computational due to it learning and changing from
experience. For example, as it stands a program that downloads and
replaces a part of itself (fairly standard nowadays), would be said to
be non-computational. Which seems fairly weird.

 Will Pearson

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