Re: How much intelligence?

Curt Welch said:
"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.



If you want to characterize natural language, and all the other behaviors we
exhibit that most other animals don't, as being the result of simple expansion
of the same capabilities as they have, then you have to show how such abilities
rely on some certain critical level of such capabilities or structures in order
to function, without requiring any addtional structures. Up until our rational
level level of thinking, associative learning dominates, as demonstrated by
Pavlov. But, associative learning is emotional, where an input causes a
positive or negative feeling by virtue of its direct or indirect association
with pleasure or pain, and that feeling causes us to approach or flee the cause
of the input. It's all about what is good or bad, as directly experienced, and
what is associated with those good or bad things, as directly experienced. We
still operate this way, but not exclusively.

When we speak of natural language, there is no emotion necessarily involved. We
can say, "There are seven red bananas on the counter," and not care, while
noting the fact. Language is not emotional persay, and not concrete. It's an
abstraction. Associations are relations between things, but are not true
abstractions of concrete realities into symbolic format. In order to learn
language, the first thing that is necessary is the ability to maintain an
abstraction, a variable that refers to something, rather than simply
associating feelings with its presence or absence.

Then, as Lester points out, there are considerations of syntax and grammar
which govern the structure of the language itself, regardless of meaning. There
are universal components to natural language which the comparison of various
languages shows can be handled in a variety of ways, but which have the same
underlying roles. We have objects of various levels of concreteness, for which
we need nouns. We have changes to those objects, things that happen to them,
for which we need words called verbs. We have attributes of both nouns and
verbs, which we call adjectives and adverbs. Then we have qualifiers for nouns
and verbs, such as articles and prepositions in English, which are handled
differently in different languages. It's all about describing objects and what
happens to them in space and time, or in whatever dimensions non-physical
objects operate in, and it depends on there being abstract representations of
such objects and events, and their atttributes and qualifiers, which we can
manipulate using the syntactical rules of the language.

So, Curt, I disagree. To latch on to associative learning as the single core
mechanic of mind is a mistake in my opinion. What separates us from other
animals is not just brute neuronal count. It's a different configuration that
allows us to formulate representations of realities which can then be
manipulated according to logical symbolic rules, which are represented by the
same mechanism. Each such representation acts as a symbol. Language is strings,
not feelings.

Granted, sometimes logic can be simulated by associative learning, like the
horse that stamps out the answers to simple addition problems, which it has
heard before and learned the answer to, but that's fake, and not an explanation
of what logic really is, or how it manifests itelf in all the ways that
separate us from other animals. Sorry for the length. Enditem.

:)
--
Smiles,

Tony
.

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