Re: AI - WHY?

Sergio Navega (snavega@ibm.net)
Wed, 11 Nov 1998 04:32:10 +0100 (MET)

Martin Sewell wrote in message <71qak3$vfc@news3.force9.net>...
>Hello (or perhaps: "Help!")
>
>I am currently doing a PhD in Intelligent Trading Systems (as in stock
>markets, etc.), and as the title suggests fully intended to utilise various
>aspects of Artificial Intelligence. As you'd imagine, the major goal is
>trying to predict an almost-but-not-quite-random time series.
>
>Neural networks, genetic algorithms, fuzzy logic - they all seemed so
>interesting (yes, even fun!), my only concern was which technique(s) were
>best applied to this sort of application.
>
>I now seem to be gradually eliminating all of the AI techniques which I set
>out to use.
>
>Goodbye fuzzy logic - why not do it properly using statistical inference
and
>probability?
>
>GAs? Just another search algorithm - adaptive simulated annealing (ASA)
may
>be better. (Okay, ASA could be considered AI too?)
>
>Neural networks? Classical statistical pattern recognition may be better
in
>many cases.
>
>I'm not aware of much work on the theoretical study of intelligent space.
>Maybe this should be tackled first?
>
>Any comments or suggestions would be appreciated. Either to rekindle my
>enthusiasm for AI, or to confirm my suspicions.
>
>Regards
>
>Martin Sewell
>martin@msewell.force9.co.uk
>

Dear Martin,

You asked for any comments, so here it goes...

The problem of stock market prediction appears to be similar to the
problem our brain must handle in trying to understand this world of
ours. Lots of signals that appear to make some sense but with lots
of noise too.

I guess that's why AI seems intuitively appropriate to handle this
sort of task. You have pretty much summarized the attempts to
tackle this problem (I'm not sure if you should've mentioned
some statistical methods as Independent Component Analysis and
others).

My vision of the problem would try to go to a completely
different direction. Before going on, let me refresh my
memory of the few I know of the financial market. Stock
trade analysts, up to the point I know, use one of two
techniques: fundamentalists and graphists. Fundamentalists
try to predict on the basis of what is known by the
more theoretical influences on economy (supply/demand, etc).
Graphists try to predict on the basis of identifying
graphic behavior similar to past ones (graphic inflection,
rupture of point of support, etc).

I guess that AI could be useful if it worked using a mix
of these two techniques. The fundamentalists try to
develop "causal models" that are able to explain what's
going on with the market. The graphists try to "recognize
patterns" of previous graphical behaviors and see if they're
similar to the current situation. I'm working on a general
model of intelligence which uses (among other components)
these two mentioned: recognition of patterns and
discovery of causal models. This is, in my opinion,
fundamental parts of any intelligent entity and I
guess it could be helpful to a practical application
as yours.

Well, this is not much, but is better than nothing.

Regards,
Sergio Navega.

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