Re: How much intelligence?

"chadmaester" <chad.d.johnson@xxxxxxxxx> wrote:
What other approaches are there besides the behaviorist approach to
language?

The Behaviorist approach believes that language is nothing more than a
learned behavior which is learned the same way we learn all behavior - the
same way a dog for example can learn to respond to the sit command. This
implies we have one type of behavior learning hardware that is used for
language as well as everything else we learn (like walking).

The anti-Behaviorist view grows from the idea that humans are special
because of their language powers (which everyone agrees) and since lower
animals don't have the type of language powers humans have, then there must
be something very different about humans which give us this power. The
assumption is that if we learn the same way dogs learn, then dogs should
have the same language skills we have. But they don't. So the conclusion
is that we must have special language hardware in our brains to explain our
advanced language skills (which is obvious), which must use a different
system than reinforcement for learning (which is not obvious). From there,
the theories about how the language hardware works, are infinite,

Skinner and Chomsky are see as the fathers of these alternate views.
There's been a divide in the field ever since. Skinner was a Behaviorist,
and Chomsky is a Linguist.

http://en.wikipedia.org/wiki/Noam_Chomsky

http://en.wikipedia.org/wiki/B._F._Skinner

The truth, like always, is probably somewhere in the middle.

For AI, it doesn't hurt to study both views because in the end, we do have
to build a language machine that has it's behavior shaped by reinforcement.

My preference for a reductionist view makes me lean heavily towards the
Skinner side because I don't believe that evolution gave us one type of
learning hardware to learn to walk and a different type of hardware to
learn to talk. I believe we have stronger language powers than the lower
animals because evolution gave us more of the same learning hardware
connected in a way to allowed it to be used for language. So I think the
solution to creating human behavior in a machine will be a single type of
learning technology that has the power to learn everything from walking, to
talking. And I believe it will get it's direction from pain and pleasure
inputs (aka a reinforcement learning machine). But how do you build, and
configure such a machine, to allow it to learn such a large and complex set
of behaviors? That's the question that needs to be answered.

In general, I think that a majority of people today feel that Skinner's
view of behavior was oversimplified and that human behavior in general is
just too complex and too advanced to be explained by reinforcement learning
alone. Maybe pigeons work that way but not humans is a common mantra you
will hear. Most people I think would tend to say that Behaviorism failed.
The work done in AI with reinforcement learning machines over the years
generally has led to the conclusion that trial and error learning (another
way to look at reinforcement learning) is just too simple and not a strong
enough learning theory. This has caused the field of AI to branch off in
all sorts of different directions trying to understand the nature of the
beast we are dealing with (studying the very nature of language and
knowledge for two example).

I however am part of a minority that thinks Skinner had it right and that
it's just societies overinflated ego that keeps it from believing that man
is nothing more than a reinforcement learning machine.

--
Curt Welch http://CurtWelch.Com/
curt@xxxxxxxx http://NewsReader.Com/
.

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