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From: markh@csd4.csd.uwm.edu (Mark William Hopkins)
Newsgroups: comp.ai.neural-nets
Subject: Re: the baby bootstrap (determining input...)
Message-ID: <2939@uwm.edu>
Date: 18 Mar 90 02:26:33 GMT
References: <720@berlioz.nsc.com> <6603@hydra.gatech.EDU> <5061@ccncsu.ColoState.EDU>
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Reply-To: markh@csd4.csd.uwm.edu (Mark William Hopkins)
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In article <5061@ccncsu.ColoState.EDU> ld231782@longs.LANCE.ColoState.Edu (Lawrence Detweiler) writes:
>>Note that babies bootstrap, so that "knowing what to look for" becomes
>>increasingly sophisticated.
>
>Sure, an artificial NN can "learn" to speak by an EXTERNAL CONTROL that
>modifies its weights.  But the human can do it with an INTERNAL one. 
>Can somebody invent a black box that can learn to speak (and make
>sense!) solely from interaction with the outside environment?

A child who gets a spanking for swearing is not undergoing internal control :)

The point is, you just have to look in the right places to find the human
analog to a neural net's explicit training.  The instinct to survive surely
figures in here too in terms of providing "external control".

You hit upon the key idea: there is an INTERACTION with an external
environment.  So there's going to be a teaching agent somewhere.  If it's not
a parent or authority, it'll be the environment itself or something in it.
After all, teachers don't have to be animate.

>A specious argument is that babies DO have "models", namely their
>parents.  But in the beginning it is all just so much input.  Babies
>must "learn to learn."

The ability to "learn to learn" must be present at least in part from birth.
Plus, there must be certain invariant fundamental ways of domain independent
learning that we have all our lives from birth that cannot be changed, but
that can be used as the basis for further bootstrapping: ways that are
inherent in the very neuro-chemistry of our brain.

The best way to resolve these issues is to start thinking of a human's mind
as an integral part of a combined mind-body system.  The human is an
intelligent control system first, logic machine second if at all.  All of
our learning is based on this ancient attribute of our species and other
species.  We develop concepts and abstractions out of our actual concrete
experiences of mobility, eating, sleeping, etc.  If you think of a control
function such as walking as being C function, the symbol for walking that
we gradually build up would be a pointer to that C function, which itself
can then be used in other functions as data.  So we abstract routines into
symbols and build up a towering hierarchy so much so that the concrete
basis of the tower very nearly falls out of sight.

That ability to create abstractions is built-in, and is probably uniquely
human in terms of our ability to abstract on anything (and by exclusion,
it must be deduced to take place in the neocortex).

The famous broom-balancing neural net controller ideally would not have
needed anything more than the learning algorithm (representing the
built-in genetic endowment) a representation of the goal (which to measure
errors against), and a physical device to control (a broom carrying cart).
Without any external intervention it would have "learned" to do the balancing
trick.  The environment representing implicitly the laws of physics would
have been the teaching agent, so its training would really be external.
I'm not aware of the details of that experiment, but I don't think it
actually had any external agent training it (except maybe to rebalance the
broom).