RE: Is generalisation a limit to intelligence?

From: James Rogers (
Date: Sun Dec 03 2000 - 00:59:50 MST

On Sat, 02 Dec 2000, Ben Goertzel wrote:
> However, we lack a quantitative science that can tell us exactly how quickly
> the error rate approaches zero as the memory (&, in a real-time
> situation, processing power able to exploit this memory) approaches
> infinity. Eliezer and I differ in that I believe such a science will
> someday exist ; We also differ in that he intuits this error rate
> approaches zero faster than I intuit it does.

I recently joined this list and have been watching this
thread with some interest. Some of the discussion seems odd, if only
because I am approaching this issue from a completely different angle.

A lot of this quantitative science (mathematics really) has already been
done. Or at least, a lot more has been done than is apparently assumed by
some of what I have seen written on this list.

Information theoretic approaches have already demonstrated much of what is
being questioned, or at least insofar as finite-state machines are
concerned. Generally speaking, given a finite amount of memory and an
arbitrarily long sequence of data (generated by any finite state machine
no matter how complex), it is possible to attain the minimum possible
predictive error rate using universal prediction schemes. An optimal
prediction scheme can be algorithmically generated and the error rate
figured for any data generated by finite-state machinery. <much lengthy
theory omitted> In short, it has been demonstrated that for any finite
state machine, it is possible to ascertain the minimum possible predictive
error rate for any data sequence given any finite amount of memory. An
optimal prediction scheme will typically approach the theoretical error
limit quite fast. However, sub-optimal prediction schemes, nonparametric
or unknown models, and similar types of situations may approach their
theoretical error rates quite slowly. It would be trivial for a
computer today to calculate error rates for any optimal universal
predictive scheme. These would seem to answer the above question and
quite a few others I've seen on this thread. The only glaring exception
to the above is if AIs don't run on finite state machinery.

Among the interesting things that have been shown with respect to this is
that humans are quite apparently finite state-machines. The first example
of this was Hagelbarger at Bell Labs (and later Claude Shannon), who first
demonstrated that humans are apparently unable to generate truly random
sequences of any kind; computers using information theoretic prediction
algorithms were able to successfully predict the behavior of humans
intentionally attempting to generate random data, with an error rate many,
many orders of magnitude below what would be expected if the human
participants were actually generating random data.

I've actually been using information theoretic approaches in my engines
for several years now, and with generally superb results across many
fields. It has been widely rumored that Claude Shannon made his fortune by
"working" the stock market (as an aside, a couple years ago I calculated
that running an optimal predictive engine against the entire NASDAQ in
realtime, based on the best engine I had produced to date, would require a
machine capable of 10^11 Flops sustained. The amount of memory was
reasonably attainable though.) I've found it odd that information theory
is routinely overlooked in AI research since it provides such a solid
foundation for the mathematical basis of the topic.

I am currently working on putting together a website with a lot of
the theory and actual application of my work, quite a few parts of which
have been applied in the commercial sector. Splitting my time between
this, audio signal analysis/processing research, and something resembling a
9-5 has me strapped for time, but hopefully I will get some descriptive and
more in-depth articles published on my website relatively soon. I've been
working on adaptive and self-learning systems for many years (though I
only really started working on AI when it was clear that a lot of the
research and development I was doing was very applicable to that
particular domain -- my original interests were some neglected areas of
database theory).


-James Rogers

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