[Paleopsych] genetics as an intelligent system

[Werbos, Dr. Paul J.]

Having spent all of about 5 minutes of real thinking about the
questions Greg raises... enough thoughts pop into the mind that
I doubt I have time to type them all.

First -- one of the reasons why the establishment may find it difficult to 
fully
address the questions is that they are very limited in this case in the degree
of mathematical abstraction they use. It's a kind of qualitative limitation
in how mathematical thinking is used...

The neuroscience establishment (which I know much better) has been struggling
with similar limitations... maybe a bit harder and a bit more successfully 
so far...

------

It is interesting to ask: now that we have learned a lot about intelligent 
systems in GENERAL..
and now that some of us have a reasonable first-order idea of how this maps 
into the brain..
what about the genetic system?

Forgive me for using a new term which sounds a bit pretentious -- 
"metagenetics."
The prefix "meta" has been badly misused lately, but in this case -- what 
else would
be a good single word to refer to the idea of a genetic system which
"learns to learn"?

Part of Greg's message is that we need to understand metagenetics in order 
to make
any sense at all of 97 percent of the human genome. That's a big step, a 
good one,
and an important one. That idea has existed in some form for a long time, 
but to
give it a snazzy new one-word version and focus more attention on it is 
still a good step.

But is there more going on here?

A natural way to interpret "metagenetics"... is to think of ... a kind of 
second-order system which is
still designed to perform the same basic functions people think about in 
genetic algorithms
or evolutionary computing: maximizing some kind of fitness function U(w) as 
a function of a set
of weights or parameters w. (Parameters could be anything from body 
characteristics
to behavioral response characteristics .. to anything...) A sophisticated 
way to explore the space
of possible .. genotypes. Back in 1999
(at a plenary talk at CEC99, the IEEE Conference on Evolutionary 
Computing), I challenged
people to send me proposals to address a more interesting computational task:
to design systems which LEARN to do stochastic search to maximize U(w,X), 
where w is as before,
and X is a set of observed variables available to enhance performance. I 
have reiterated this in many
talks and tutorials... I call this task "Brain-Like Stochastic search." 
It's very important in
engineering, for example; if we use evolutionary search to find the best 
possible chip design
for some task.... it would be good to represent DIFFERENT chip design tasks 
by a vector X,
and then use a system which learns to do better on chip design task in general.
For now, it's enough of a challenge to treat X as "exogenous," but someday 
one could advance to
dynamic X...

Now: one COULD follow up on Greg's questions by asking whether we can model 
the genetic system
as one which implements "Brain-Like Stochastic Search" with dynamic X. We 
may ask: to what
extent does this richer functional interpretation become essential to 
understanding the basics
of what we really see with the genome?

Now -- a certain degree of "stockpiling" can be important even in that 
limited context.

But another question occurs to me today: would it make any sense to go even 
further,
and evaluate the possibility of a still higher level of intelligence in the 
genetic system?
I wonder.

In brains, evolutionary computing is certainly far from enough, in any form.
(And I suppose I know a few key things about Edelman's work that Edelman 
doesn't....)
In a word -- TIME. Optimizing results INTO THE FUTURE, with anticipation or 
foresight
(both explicit and implicit), is absolutely central to how brains work.

Could there be anything like THAT in the genetic system? I wonder...

Various types of memory are essential in brains. There are many levels of 
stockpiling in brains.
Could any of THAT be transferrable to the genetic case?

I wonder.

This morning I was thinking more about Einstein than about Greg... but I 
suppose such thoughts would be
off-topic on this list.  Oh, well.

Best of luck,

    Paul