James Ratcliff wrote:
>However, part of the key to intelligence is **self-tuning**.
>I believe that if an AGI system is built the right way, it can effectively
>tune its own parameters, hence adaptively managing its own complexity.
I agree with Ben here, isnt one of the core concepts of AGI the ability
to modify its behavior and to learn?
That might sound like a good way to proceed, but now consider this.
Suppose that the AGI is designed with a "symbol system" in which the
symbols are very much mainstream-style symbols, and one aspect of them
is that there are "truth-values" associated with the statements that use
those symbols (as in "I like cats", t=0.9).
Now suppose that the very fact that truth values were being *explicitly*
represented and manipulated by the system was causing it to run smack
bang into the Complex Systems Problem.
In other words, suppose that you cannot get that kind of design to work
because when it scales up the whole truth-value maintenance mechanism
just comes apart.
Suppose, further, that the only AGI systems that really do work are ones
in which the symbols never use "truth values" but use other stuff (for
which there is no interpretation) and that the thing we call a "truth
value" is actually the result of an operator that can be applied to a
bunch of connected symbols. This [truth-value = external operator] idea
is fundamentally different from [truth-value = internal parameter] idea,
obviously.
Now here is my problem: how would "parameter-tuning" ever cause that
first AGI design to realise that it had to abandon one bit of its
architecture and redesign itself?
Surely this is more than parameter tuning? There is no way it could
simply stop working and completely redesign all of its internal
architecture to not use the t-values, and make the operators etc etc.!
So here is the rub: if the CSP does cause this kind of issue (and that
is why I invented the CSP idea in the first place, because it was
precisely those kinds of architectural issues that seemed wrong), then
parameter tuning will never be good enough, it will have to be a huge
and very serious new approach to making our AGI designs flexible at the
design level.
Does that make sense?
Richard Loosemore
This will have to be done with a large amount of self-tuning, as we will
not be changing parameters for every action, that wouldnt be efficient.
(this part does not require actual self-code writing just yet)
Its more a matter of finding out a way to guide the AGI in changing the
parameters, checking the changes and reflecting back over the changes to
see if they are effective for future events.
What is needed at some point is being able to converse at a high level
with the AGI, and correcting their behaviour, such as "Dont touch that,
cause it will have a bad effect" and having the AGI do all of the
parameter changing and link building and strengthening/weakening
necessary in its memory. It may do this in a very complex way and may
effect many parts of its systems, but by multiple reinforcement we
should be able to guide the overall behaviour if not all of the
parameters directly.
James Ratcliff
*/Benjamin Goertzel <[EMAIL PROTECTED]>/* wrote:
> Conclusion: there is a danger that the complexity that even Ben
agrees
> must be present in AGI systems will have a significant impact on our
> efforts to build them. But the only response to this danger at the
> moment is the bare statement made by people like Ben that "I do not
> think that the danger is significant". No reason given, no explicit
> attack on any component of the argument I have given, only a
statement
> of intuition, even though I have argued that intuition cannot in
> principle be a trustworthy guide here.
But Richard, your argument ALSO depends on intuitions ...
I'll try, though, to more concisely frame the reason I think your
argument
is wrong.
I agree that AGI systems contain a lot of complexity in the dynamical-
systems-theory sense.
And I agree that tuning all the parameters of an AGI system externally
is likely to be intractable, due to this complexity.
However, part of the key to intelligence is **self-tuning**.
I believe that if an AGI system is built the right way, it can
effectively
tune its own parameters, hence adaptively managing its own complexity.
Now you may say there's a problem here: If AGI component A2 is to
tune the parameters of AGI component A1, and A1 is complex, then
A2 has got to also be complex ... and who's gonna tune its parameters?
So the answer has got to be that: To effectively tune the parameters
of an AGI component of complexity X, requires an AGI component of
complexity a bit less than X. Then one can build a self-tuning AGI
system,
if one does the job right.
Now, I'm not saying that Novamente (for instance) is explicitly built
according to this architecture: it doesn't have N components wherein
component A_N tunes the parameters of component A_(N+1).
But in many ways, throughout the architecture, it relies on this sort of
fundamental logic.
Obviously it is not the case that every system of complexity X can
be parameter-tuned by a system of complexity less than X. The question
however is whether an AGI system can be built of such components.
I suggest the answer is yes -- and furthermore suggest that this is
pretty much the ONLY way to do it...
Your intuition is that this is not possible, but you don't have a proof
of this...
And yes, I realize the above argument of mine is conceptual only --
I haven't
given a formal definition of complexity. There are many, but that would
lead into a mess of math that I don't have time to deal with right now,
in the context of answering an email...
-- Ben G
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