Re: How much intelligence?

Tony Orlow <aeo6@xxxxxxxxxxx> wrote:
Curt Welch said:

Curt, this isn't a matter of elevating us above animal status. I am
perfectly happy to admit that I am nothing but a smart ape. I agree that
associative leads to rational thinking, but as far as I can tell, the
mechanism is somewhat different. Rather than varying degrees of positive
or negative associations between sets of stimuli built up through
experience, which lead to emotional responses,

I don't think of walking to the kitchen to answer the phone as an emotional
response, yet we have no trouble explaining that behavior in terms of
reinforcement.

natural language and other
apparently distinctly human abilities stem from a different type of
processing.

Yeah, many people like to believe that. I just don't happen to be one of
them. There's far too much in common with all behaviors to waste much
serious time trying to define it as different. It's mostly the people that
specialize in the study of language that seem to want to see their field of
study as being unique and special. Is that because they know more about
language than anyone (which we would expect to be true), or is it because
their focus has simply blinded them to what exists outside of their field?

The people who focus on all behavior can't find a reason to justify
language behavior as somehow uniquely different from all the other
behaviors. If anyone should be an expert on the differences between
language behavior and other behaviors, shouldn't it be the Behaviorists and
not the Linguists?

When I propose a statement, it needs to be parsed for meaning
and correlated with concepts, but it doesn't need to be emotionally
evaluated.

Language has always struck me as one of the most emotional behaviors we
have. You see no emotion in a poem? In the words "Four score and seven
years ago .."? In the words, "I have a dream....", in the words "Get the
*** out of here you ass hole!". Language is not free from emotion by any
means. But how much emotion is there in bending down to pick up a pen when
you drop it? Some behaviors strike us as being emotional and some don't,
but the boundary doesn't seem to have anything to do with whether the
behavior is language behavior or some other type of behavior.

Of course, the interplay between the emotional levels of mind
and the rational levels of mind are complex, and often confounding in the
search for a precise description of the relationship, but it's really not
that hard to characterize.

It's not really hard at all. It's all easily explained in terms of
reinforcement of behaviors which are contingent on the current
environmental context (where the internal state of the brain is part of the
environment).

The only answer to the type of argument stated above by Tony is to
actually build a reinforcement leaning machine that can learn to use
language the way humans use language. Nobody has done that.

Has anyone tried in the last 50 years?

You bet. I've spent over 30 years trying (off and on).

If it is possible simply by brute
force of associative learning to develop natural language, and the answer
is so Pavlovianly simple, why have efforts not yet succeeded along these
lines?

Understanding what a machine is doing, can be far easier than understanding
how it is doing it. We understand the what. We don't yet fully understand
the how.

Have you used a compression program to reduce the size of a computer data
file? Seems almost like magic that it can make it smaller, and then later
return it to its full size. Yet, with a simple example, we can understand
how it can do this. We could see how it could change a string of zeros
into a special marker, followed by a single zero, and then a count of the
number of times to repeat that zero. If the file had enough long strings
of repeating characters, this simple technique would allow us to translate
the file to this encoded version which was smaller.

But have you ever tried to write a compression program? If you have, you
would understand how hard it is to write a good compression program that
could take most files, and make them smaller. Understanding what it is
doing, is easy. Understanding how it can do it because we have an example
of one algorithm it could use, is easy. Understand how to make it work as
well as the ones we have on the shelf is very very hard. Every few years,
someone comes up with an improved algorithm for compression programs which
no one had thought of for the past 50 years. These things are really
interesting because once the algorithm is discovered, it's trivial to
understand it, and understand why it works so well - but yet it's something
a large collection of smart people never thought about, until that one guy
figured it out.

There are many algorithms like this which are easy to understand in theory,
but very hard to find a good implementation for. Sorting is another
classic example. It's a trivial problem, yet new sorting algorithms are
stilling be discovered:

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

"library sort was first published in 2004"

For example, image recognition software that works as well as the brain has
never been duplicated. It's trivial to understand what the task is. And
it's trivial to write programs to demonstrate that it can be done. But
doing it as well as the brain, has not yet been duplicated. No one has
build software that can look a pictures, and tell you what's in the
picture. Is it a cat? Or a dog? Or a car? Or a house on a lake?
Nothing we have produced comes close to what humans can do but yet there's
no real magic about what the task is. It's just the "how" that continues
to elude us. But slowly but surely, the algorithms and the understanding
of how it's done, is improving.

Reinforcement learning is the same thing. The principle about how it works
is easy to demonstrate, and easy to implement for simple cases, but it's
been a long slow road to find strong implementations that work well for the
hard problems. Image recognition is tied up in the same problem, because
if you can't do a good job of classifying the sensory data (be it vision,
or sound, or touch or whatever), then you can't do a good job of
associating behaviors (actions) with sensory events.

AI is an easy problem to understand, but a hard problem to implement. The
Behaviorists told us what human behavior is 50 years ago, but far too few
people believed what they were telling us. Surely man, the greatest thing
on the face of the planet, was more than a trial and error learning
machine? Nope.

Lack of computing power or memory? Nope, that's not it.

Lack of the correct algorithm. If we had the right algorithms, we could
build human level AI today. It might cost a small fortune and take up a
warehouse, but it would probably be in our power to build it if we had the
right algorithms. I suspect however, that it wouldn't even be that
expensive - that a rack or two of special processors could do it. We can
already build very small, very complex, and very fast hardware into a very
small space these days.

Everyone understands that brains are learning machines of some type but
nobody knows with what type of learning machine we are. Is it lots of
different types of technology's welded together, is it one general
technology, or something between the two? This is what the field of AI
has been trying to get a handle on for 50 years and though the field
has produced a large amount of knowledge and new technologies, we
really don't know anything more today than we did 50 years ago about
what is the right technology because nothing yet produced has managed
to act like a human. There have been lots of promising results, but no
answers.

And what aspects of language have such attempts failed to produce, if not
all?

The correct question is what behavior has such attempts failed to produce.
And the answer is that a ton of behaviors far simpler than natural
language, have still failed to be reproduced. We haven't yet seen a
reinforcement learning machine learn to walk on two legs for example (as
far as I know).

All language is just behavior, and all behavior is language. We don't call
it body language for the fun of it. Seeing somebody smile at us has
meaning just like when someone speaks to us. It's not a different type of
meaning, it's the same type of meaning. We understand all sensory data
using the same systems for extracting "meaning" out of the sensory data.
We know the meaning of the phone ringing as much as we know the meaning of
thunder as much as we know the meaning of a water bottle. All these things
allow us to predict things about the future, and allows us to select
behaviors to deal with what we expect the future to hold. We see a water
bottle and we know that we can fix our thirst by using the correct arm
motions to drink the water hidden in the bottle even though we can't see
the water.

We see a message written on the note pad that says "I left the water bottle
in in the fridge" and we extract the same meaning from the vision of that
note that we extracted from the vision of the glass of water. It regulates
the behaviors we will select in the future to respond to thirst.

The entire world is constantly speaking to us. The fact that some of the
things we "hear" in the environment we choose to call "English" doesn't
mean that that sensory data was processed with a different type of hardware
than what was used to process what the water bottle "said" to us.

The brain is a language machine. It receives a constant flow of language
from the environment in the form of sensory data, and in response, it sends
out a constant flow of language to our muscles to make them move. The
syntax, grammar, and semantics of the raw sensory data that the brain deals
with is far more complex than the fairly trivial syntax and semantics of
English. Natural language like English is just a trivial small subset of
all the language the brain has to understand, and produce just to stay
alive. Natural language is just the top of the iceberg in terms of the
language that the brain has to deal with. The reason we have had so much
trouble building specialized language machines (chat bots for example) is
because that without the foundation of language processing that allows us
to understand what the raw sensory data that represents a bottle of water
means, we have no hope of understanding what a few written words mean.

Doesn't grammatical structure seem like an inherent ingredient in
the recipe for language?

Of course it is. But the brain is dealing with that same problem in a far
more complex form as it learns to do even the most basic things in life,
like recognizing a water bottle and not mistaking a picture of a water
bottle for a real water bottle because one can be used to solve a thirst
problem and the other can't.

Do you know that when a child is raised in
isolation, like a closet or attic, if they don't learn natural language
by a certain age they never can?

Are you aware that if a baby is born with a cataract in their eye, it must
be fixed very quickly, because if you wait too long, the brain will never
learn to use the bad eye even though the eye is fixed and working
perfectly? And when they do fix these problems in babies, they must tape
the good eye closed in order to allow the bad eye time to "catch up" with
the good eye.

This happens because the brain is very plastic, and the neurons that would
normally have been used for the bad eye get co-opted for use processing
data from the good eye.

The cortex is very plastic, and if you don't use it for one type of data,
it will be consumed and allocated to processes other data. That's why if
the language center isn't allowed to develop for language use, it will be
taken over for some other purpose, and once allocated, it never goes back.

Do you know that there are cases where
more than one child has been hidden away like this and that, given the
opportunity, they will develop their own language spontaneously? If you
want to talk about Skinner's Box, you might want to keep these other
boxes in mind. They clearly indicate that there are innate mechanisms in
the human mind meant to handle abstraction and language

Actually, your example strongly suggests it's not natural language
hardware. If it were natural language hardware, it wouldn't be taken over
for another purpose, such as vision or other aspects of sound besides
language. Your example suggests that the hardware we use for language can
easily be used for other functions and isn't specialized only for the use
of language.

But, as I've suggested, a think a better way to look at the brain is as a
language machine. Everything it does is language processing, not just the
subset set of behaviors we call English.

which are greatly
reduced in those other animals which have them, and distinct in nature
from emotional processing.

All the people that carefully study behavior, find no justification for
using the term "emotional processing".

I am not saying other animals don't have
logic. That border collie in Germany and that Afrrican Grey parrot both
exhibit clear simple logic. That's what really separates us from other
animals, the degree of logic, not of emotional processing.

Of course, I am always happy to hear specific counterarguments. :)

The low level hardware that allows us to all of this, seems to be very
generic, and very plastic. It reacts to whatever data is feed to it. It's
all generic signal (aka language) processing hardware.

Our ability to abstract is just as important when chasing a rabbit for food
as it is when we use language. We need to take the raw sensory data of
this furry grey object and abstract out the concept that it's food that we
can eat and not a rock. When we chase a rabbit, and we sense a vertical
brown band in our vision, we need to correct abstract out the fact that
it's a tree that will stop us from getting the food if we don't correctly
act to move around it before we run into it.

When the furry grey rabbit vanishes from sight behind the tree, we need to
correct abstract the idea that the rabbit is not gone, but simply hiding
behind the tree so that we can run around the tree to find it.

All behavior is nothing more than a problem of selecting the correct
behavior to produce in response to the current environment we find
ourselves in. And that environment might at times include the memory of a
rabbit we just saw 5 seconds ago before it ran behind the tree, or might
include a sequence of sounds we call words. In all cases, we are able to
select, and produce, a very wide range of different behaviors in response
to a very complex and changing multidimensional environment. And it's all
done in parallel so that one behavior might be selected for the right leg
and another might be selected for the left arm that has nothing to do with
the elements of the environment triggered the right leg behavior.

The neocortex is playing a key role in processing the raw sensory data and
abstracting a valid description of the environment by the patterns of
neural activity it creates. We know the fuzzy grey thing zipping across
our field of vision is the same type of thing we just ate last week because
the neocortex has created the same patterns of activity for this grey patch
of light as it did for the one last week which we ate and really liked the
taste of even though that thing was very different in many ways. It had to
correctly define the concept of "good tasting rabbit" and correctly
associate that grey streak of light with that concept. It plays the same
role when it correctly manages to classify strings of sounds as a given
word and strings of words to have a collection of abstract meanings. We
learn how to respond to all these different complex combinations of
environmental conditions by a long slow processes of reinforcement
learning. That training tells the brain which conditions to ignore, and
which to use as triggers of different behaviors.

When it's all done, we do end up with natural language hardware. But that
language hardware was shaped out of the same context sensitive behavior
generating hardware we use to create all our behaviors.

We know that our finger motions are under the control of reinforcement
learning. If the fingers are also used to type language, how can language
not be under the control of the same reinforcement learning system? The
brain only gave us one system for learning to move our fingers and that one
system is used for all finger motions we learn to produce. It makes no
sense to assume evolution did it any other way.

The reason we have strong language skills when other animals don't is just
because we have a more complex motion controller system that has enough of
the right hardware in the right configuration to produce all the behaviors
needed for language. Learning to communicate in English is like learning
100,000 tricks. If your behavior learning hardware can only learn 1,000
tricks, then there's no way you can use it to learn language. Or if the
behavior learning hardware only produces a context which goes back 2
seconds, you can't learn to parse and correctly respond to a 10 second
string of sounds.

So I agree that we have hardware which is configured in just the right way
that it allows us to learn all the tricks we call natural language skills,
but I don't agree that it's special hardware that works differently than
the hardware which allows us to learn all our other behavior tricks. It's
all one big reinforcement learning system built to allow us to adapt all
our behavior to the needs of our environment during our life time.

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

Loading