From: James Rogers (email@example.com)
Date: Wed May 15 2002 - 12:50:00 MDT
On Mon, 2002-05-06 at 07:27, Ben Goertzel wrote:
> I think that an efficient way of doing Solomonoff induction for modest
> program sizes, can definitely form the core of an AGI.
> We do something similar in Novamente, though with a different algorithm (our
> design involves the use a hybridization of probabilistic inference and
> evolutionary programming for program induction)
There is another way to look at this that is correct, though I
personally don't really think about it in these terms. Every "model" is
essentially a universal Turing machine (with everything that implies,
except infinite memory) with a very unusual computing architecture
revolving around a structure that is an algorithmic superset (as it
were) of Solomonoff induction.
One of the generally interesting and useful things I've noticed
relatively recently is that it appears that the Halting Problem can
essentially be ignored. Any problem that exceeds a certain complexity
will stop at a best approximation when it hits the complexity limit of
the model, and infinite loop type situations that fit within the
complexity limit of the model are immediately detected simply as a
consequence of the organization. Everything appears to halt eventually,
though it may be an exception that causes it. I only mention this
because I've had to write code for regular software systems in the past
that could resolve these kinds of things, which was an awkward and ugly
thing at best and not totally reliable either. In this case, I seem to
have gotten those features for free, though it took me a long time to
even notice. Note that I haven't rigorously evaluated this, so I am not
100% certain, but what I described above appears to be the case; it
would certainly be pretty cool if this turned out to be correct in the
> However, I also suspect there may be a long path from getting the individual
> "model" (in your language) to work well, and figuring out how to piece
> together a bunch of models into an overall functional mind....
Actually, this is one of the easier parts. I'm still fiddling with
this, but it is really at the point of just picking something I like and
going with it as there are a couple clean and reasonably simple
approaches that are working. As I stated, I can hard merge and turn two
models into one larger model that still has the general capabilities of
the individual models, but there is a loss of efficiency when you do
this with two models that have been built with two very different types
of data input. Clustering unrelated models rather than merging them
really does seem like the optimum pathway. Having short-term/working
memory model goes a long way toward making this work pretty seamlessly.
> Pei and I long ago made the observation that most of the algorithms
> important for mind have exponential time and space complexity, but with a
> manageably small exponent for realistic problem sizes. It's interesting
> that you've come up with the same conclusion in your work.
This became apparent to me quite a while back. The reason that one of
my long-time favorite rants is about the hardware limitation being
memory rather than CPU is that inadequate CPU will just make things run
slower, but inadequate memory pretty much leaves you hosed. Inadequate
CPU may make things slow but at least it will get the right answer
eventually; inadequate memory is a "brick wall" limiter on effective
intelligence at any speed, IMO.
I'm really short on time these days, so I apologize if my responses are
a bit belated.
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