Why Computer is Dumb

Most people think computers will never be able to think.   That is, really
think. Not now or ever.   To be sure, most people also agree that computers
can do many things that a person would have to be thinking to do.   Then
how could a machine seem to think but not actually think?  Well, setting
aside the question of what thinking actually is, I think that most of us
would answer that by saying that in these cases, what the computer is
doing is merely a superficial imitation of human intelligence. It has been
designed to obey certain simple commands, and then it has been provided
with programs composed of those commands.  Because of this, the
computer has to obey those commands, but without any idea of what's
happening. 

Indeed, when computers first appeared, most of their designers intended
them for nothing only to do huge, mindless computations.  That's why the
things were called "computers".   Yet even then, a few pioneers --
especially Alan Turing -- envisioned what's now called "Artificial
Intelligence" - or "AI".  They saw that computers might possibly go
beyond arithmetic, and maybe imitate the processes that go on inside
human brains.  

Today, with robots everywhere in industry and movie films, most people
think Al has gone much further than it has. Yet still, "computer experts"
say machines will never really think. If so, how could they be so smart,
and yet so dumb?

================== CAN MACHINES BE CREATIVE? ==================

We naturally admire our Einsteins and Beethovens, and wonder if
computers ever could create such wondrous theories or symphonies.  Most
people think that creativity requires some special, magical  "gift" that
simply cannot be explained. If so, then no computer could create - since 
anything machines can do (most people think can be explained.

To see what's wrong with that, we must avoid one naive trap. We mustn't
only look at works our culture views as very great, until we first get good
ideas about how ordinary people do ordinary things. We can't expect to
guess, right off, how great composers write great symphonies. I don't
believe that there's much difference between ordinary thought and
highly creative thought. I don't blame anyone for not being able to do
everything the most creative people do. I don't blame them for not being
able to explain it, either. I do object to the idea that, just because we can't
explain it now, then no one ever could imagine how creativity works.

We shouldn't intimidate ourselves by our admiration of our Beethovens
and Einsteins. Instead, we ought to be annoyed by our ignorance of how
we get ideas - and not just our "creative" ones. Were so accustomed to the
marvels of the unusual that we forget how little we know about the
marvels of ordinary thinking. Perhaps our superstitions about creativity
serve some other needs, such as supplying us with heroes with such
special qualities that, somehow, our deficiencies seem more excusable.

Do outstanding minds differ from ordinary minds in any special way? I
don't believe that there is anything basically different in a genius, except
for having an unusual combination of abilities, none very special by
itself. There must be some intense concern with some subject, but that's
common enough. There also must be great proficiency in that subject;
this, too, is not so rare; we call it craftsmanship. There has to be enough
self-confidence to stand against the scorn of peers; alone, we call that
stubbornness. And certainly, there must be common sense. As I see it, any
ordinary person who can understand an ordinary conversation has
already in his head most of what our heroes have. So, why can't
"ordinary, common sense" - when better balanced and more fiercely
motivated - make anyone a genius,

So still we have to ask, why doesn't everyone acquire such a combination?
First, of course, it sometimes just the accident of finding a novel way to
look at things. But, then, there may be certain kinds of difference-in-
degree. One is in how such people learn to manage what they learn:
beneath the surface of their mastery, creative people must have
unconscious administrative skills that knit the many things they know
together. The other difference is in why some people learn so many more
and better skills. A good composer masters many skills of phrase and
theme - but so does anyone who talks coherently.

Why do some people learn so much so well? The simplest hypothesis is
that they've come across some better ways to learn! Perhaps such "gifts"
are little more than tricks of "higher-order" expertise. Just as one child
learns to re-arrange its building-blocks in clever ways, another child
might learn to play, inside its head, at Fe-arranging how it learns!

Our cultures don't encourage us to think much about learning. Instead
we regard it as something that just happens to us. But learning must itself
consist of sets of skills we grow ourselves; we start with only some of them
and and slowly grow the rest. Why don't more people keep on learning
more and better learning skills? Because it's not rewarded right away, its
payoff has a long delay. When children play with pails and sand, they're
usually concerned with goals like filling pails with sand. But once a child
concerns itself instead with how to better learn, then that might lead to
exponential learning growth! Each better way to learn to learn would lead
to better ways to learn - and this could magnify itself into an awesome,
qualitative change. Thus, first-rank "creativity" could be just the
consequence of little childhood accidents.

So why is genius so rare, if each has almost all it takes? Perhaps because
our evolution works with mindless disrespect for individuals. I'm sure no
culture could survive, where everyone finds different ways to think. If
so, how sad, for that means genes for genius would need, instead of
nurturing, a frequent weeding out.

================== PROBLEM SOLVING. ==================

We can hardly expect to be able to make machines do wonders before we
find how to make them do ordinary, sensible things. The earliest
computer programs were little more than simple lists and loops of
commands like "Do this. Do that. Do this and that and this again until that
happens". Most people still write programs in such languages (like BASIC
or FORTRAN) which force you to imagine everything your program will
do from one moment to the next. Let's call this "do now" programming.

Before long, Al researchers found new ways to make programs. In their
"General Problem Solver" system, built in the late 1950's- Allen Newell,
J.C.Shaw and Herbert A.Simon showed ways to describe processes in terms
of statements like "If the difference between what you have and what you
want is of kind D, then try to change that difference by using method M."
This and other ideas led to what we call "means-ends" and "do if needed"
programming methods. Such programs automatically apply rules
whenever they're needed, so the programmers don't have to anticipate
when that will happen. This started an era of programs that could solve
problems in ways their programmers could not anticipate, because the
programs could be told what sorts of things to try, without knowing in
advance which would work. Everyone knows that if you try enough
different things at random, eventually you can do anything. But when
that takes a million billion trillion years, like those monkeys hitting
random typewriter keys, it's not intelligence -- just Evolution. The new
systems didn't do things randomly, but used "advice" about what was
likely to work on each kind of problem. So, instead of wandering around
at random, such programs could sort of feel around, the way you'd climb a
hill in the dark by always moving up the slope. The only trouble was a
tendency to get stuck on smaller peaks, and never find the real mountain
tops.

Since then, much Al research has been aimed at finding more "global"
methods, to get past different ways of getting stuck, by making programs
take larger views and plan ahead. Still, no one has discovered a
"completely general" way to always find the best method -- and no one
expects to.

Instead, today, many Al researchers aim toward programs that will match
patterns in memory to decide what to do next. I like to think of this as "do
something sensible" programming. A few researchers -- too few, I think
-- experiment with programs that can learn and reason by analogy. These
programs will someday recognize which old experiences in memory are
most analogous to new situations, so that they can "remember" which
methods worked best on similar problems in the past.

================== CAN COMPUTERS UNDERSTAND? ==================

Can we make computers understand what we tell them?   In 1965, Daniel
Bobrow wrote one of the first Rule-Based Expert Systems.   It was called 
"STUDENT" and it was able to solve a  variety of high-school algebra "word
problems"., like these:

The distance from New York to Los Angeles is 3000 miles. If the
average speed of a jet plane is 600 miles per hour, find the time it
takes to travel from New York to Los Angeles by jet.

Bill's father's uncle is twice as old as Bill's father. Two years from
now I Bill's father will be three times as old as Bill. The sum of their
ages is 92.
Find Bill's age.

Most students find these problems much harder than just solving the
formal equations of high school algebra. That's just cook-book stuff -- but
to solve the informal word problems, you have to figure out what
equations to solve and, to do that, you must understand what the words and
sentences mean. Did STUDENT understand? It used a lot of tricks. It was
programmed to guess that "is" usually means "equals". It didn't even try to
figure out what "Bill's fathers' uncle" means -- it only noticed that this
phrase resembles "Bill's father". It didn't know that "age" and "old" refer
to time, but it took them to represent numbers to be put in equations. With
a couple of hundred such word-trick-facts, STUDENT sometimes managed
to get the right answers.

Then dare we say that STUDENT "understands" those words? Why bother.
Why fall into the trap of feeling that we must define old words like
"mean" and "understand"? It's great when words help us get good ideas,
but not when they confuse us. The question should be: does STUDENT avoid
the "real meanings" by using tricks?

Or is it that what we call meanings really are just clever bags of tricks.
Let's take a classic thought-example, such as what a number means.
STUDENT obviously knows some arithmetic, in the sense that it can find
such sums as "5 plus 7 is 12". But does it understand numbers in any other
sense - say, what 5 "is" - or, for that matter, what are "plus" or "is"? What
would �say if I asked you, "What is Five"? Early in this century, the
philosophers Bertrand Russell and Alfred North Whitehead proposed a
new way to define numbers. "Five", they said, is "the set of all possible sets
with five members". This set includes each set of five ball-point pens, and
every litter of five kittens. Unhappily, it also includes such sets as "the
Five things you'd least expect" and "the five smallest numbers not
included in this set" -- and these lead to bizarre inconsistencies and
paradoxes. The basic goal was to find perfect definitions for ordinary
words and ideas. But even to make the idea work for Mathematics, getting
around these inconsistencies made the Russell-Whitehead theory too
complicated for practical, common sense, use. Educators once actually
tried to make children use this theory of sets, in the "New Mathematics"
movement of the 1960's; it only further set apart those who liked
mathematics from those who dreaded it. I think the trouble was, it tried to
get around a basic fact of mind: what something means to me depends to
some extent on many other things I know.

What if we built machines that weren't based on rigid definitions? Wont
they just drown in paradox, equivocation, inconsistency? Relax! Most of
what we people "know" already overflows with contradictions; still we
survive. The best we can do is be reasonably careful; let's just make our
machines that careful, too. If there remain some chances of mistake, well,
that's just life.

================== WEBS OF MEANING. ==================

If every meaning in a mind depends on other meanings in that mind,
does that make things too ill-defined to make a scientific project work?
No, even when thing go in circles, there still are scientific things to do!
Just make new kinds of theories - about those circles themselves! The
older theories only tried to hide the circularities. But that lost all the
richness of our wondrous human meaning-webs; the networks in our
human minds are probably more complex than any other structure
Science ever contemplated in the past. Accordingly, the detailed theories
of Artificial Intelligence will probably need, eventually, some very
complicated theories. But that's life, too.

Let's go back to what numbers mean. This time, to make things easier,
well think about Three. I'm arguing that Three, for us, has no one single,
basic definition, but is a web of different processes that each get meaning
from the others. Consider all the roles "Three" plays. One way we tell a
Three is to recite "One, Two, Three", while pointing to the different
things. To do it right, of course, you have to (i) touch each thing once and
(ii) not touch any twice. One way to count out loud while you pick up each
object and remove it. Children learn to do such things in their heads or,
when that's too hard, to use tricks like finger-pointing. Another way to
tell a Three is to use some Standard Set of Three things. Then bring �set of
things to the other set, and match them I one-to-one: if all are matched
and none are left, then there were Three. That "standard I Three" need
not be things, for words like "one, two, three" work just as well. For Five
we have a wider choice. One can think of it as groups of Two and Three, or
One and Four. Or, one can think of some familiar shapes -. a pentagon, an
X,  a Vee,  a cross, an aeroplane; they all make Fives.

   o o    o o   o   o     o        o
  o   o    o     o o    o o o   o o o
    o     o o     o       o        o

Because each trick works in different situations, our power stems from
being able to shift from one trick to another. To ask which meaning is
correct - to count, or match, or group - is foolishness. Each has its uses
and its ways to support the others. None has much power by itself, but
together they make a versatile skill-system. Instead of flimsy links in
chain of definitions in the mind, each word we use can activate big webs
of different ways to deal of things, to use them, to remember them, to
compare them, and so forth. With multiply-connected knowledge-nets,
you can't get stuck. When any sense of meaning fails, you can switch to
another. The mathematician's way, once you get into the slightest trouble,
you're stuck for good!

Why, then, do mathematicians stick to slender chains, each thing
depending as few things as is possible? The answer is ironic:
mathematicians want to get stuck! When anything goes wrong, they want
to be the first to notice it. The best way to be sure of that is having
everything collapse at once! To them, fragility is not bad, because it helps
them find the perfect proof, lest any single thing they think be
inconsistent with any other one. That's fine for Mathematics; in fact,
that's what much of mathematics is. It's just not good Psychology. Let's
face it, our minds will always hold some beliefs that turn out wrong.

I think it's bad psychology, when teachers shape our children's
mathematics into long, thin, fragile, definition tower-chains, instead of
robust cross-connected webs. Those chains break at their weakest links,
those towers topple at the slightest shove. And that's what happens to a
child's mind in mathematics class, who only takes a moment just to watch
a pretty cloud go by. The purposes of ordinary people are not the same as
those of mathematicians and philosophers, who want to simplify by
having just as few connections as can be. In real life, the best ideas are
cross-connected as can be. Perhaps that's why our culture makes most
children so afraid of mathematics. We think we help them get things
right, by making things go wrong most times! Perhaps, instead, we ought
to help them build more robust networks in their heads.

================== CASTLES IN THE AIR. ==================

The secret of what something means lies in the ways that it connects to all
the other things we know. The more such links, the more a thing will
mean to us. The joke comes when someone looks for the "real" meaning of
anything. For, if something had just one meaning, that is, if it were only
connected to just one other thing, then it wold scarcely "mean" at all!

That's why I think we shouldn't program our machines that way, with
clear and simple logic definitions. A machine programmed that way
might never "really" understand anything -- any more than a person
would. Rich, multiply-connected networks provide enough different ways
to use knowledge that when one way doesn't work, you can try to figure
out why. When there are many meanings in a network, you can turn
things around in your mind and look at them from different perspectives;
when you get stuck, you can try another view. That's what we mean by
thinking!

That's why I dislike logic, and prefer to work with webs of circular
definitions. Each gives meaning to the rest. There's nothing wrong with
liking several different tunes, each one the more because it contrasts
with the others. There's nothing wrong with ropes - or knots, or woven
cloth - in which each strand helps hold the other strands together - or
apart! There's nothing very wrong, in this strange sense, with having all
one's mind a castle in the air!

To summarize: of course no computer could understand anything real --
or even what a number is - if forced to single ways to deal with them. But
neither could a child or philosopher. So such concerns are not about
computers at all, but about our foolish quest for meanings that stand by
themselves, outside any context. Our questions about thinking machines
should really be questions about our own minds.

================== ARE HUMANS SELF-AWARE? ==================

Most people assume that computers can't be conscious, or self-aware; at
best they can only simulate the appearance of this. Of course, this
assumes that we, as humans, are self-aware. But are we? I think not. I
know that sounds ridiculous, so let me explain.

If by awareness we mean knowing what is in our minds, then, as every
clinical psychologist knows, people are only very slightly self-aware, and
most of what they think about themselves is guess-work. We seem to build
up networks of theories about what is in our minds, and we mistake these
apparent visions for what's really going on. To put it bluntly, most of
what our "consciousness" reveals to us is just "made up". Now, I don't
mean that we're not aware of sounds and sights, or even of some parts of
thoughts. I'm only saying that we're not aware of much of what goes on
inside our minds.

When people talk, the physics is quite clear: our voices shake the air; this
makes your ear-drums move -- and then computers in your head convert
those waves into constituents of words. These somehow then turn into
strings of symbols representing words, so now there's somewhere in your
head that "represents" a sentence. What happens next?

When light excites your retinas, this causes events in your brain that
correspond to texture, edges, color patches, and the like. Then these, in
turn, are somehow fused to "represent" a shape or outline of a thing.
What happens then?

We all comprehend these simple ideas. But there remains a hard problem,
still. What entity or mechanism carries on from there? We're used to
saying simply, that's the "self". What's wrong with that idea? Our standard
concept of the self is that deep inside each mind resides a special, central
"self" that does the real mental work for us, a little person deep down
there to hear and see and understand what's going on. Call this the
"Single Agent" theory. It isn't hard to see why every culture gets attached
to this idea. No matter how ridiculous it may seem, scientifically, it
underlies all principles of law, work, and morality. Without it, all our
canons of responsibility would fall, of blame or virtue, right or wrong.
What use would solving problems be, without that myth; how could we
have societies at all?

The trouble is, we cannot build good theories of the mind that way. In
every field, as Scientists we're always forced to recognize that what we
see as single things - like rocks or clouds, or even minds - must sometimes
be described as made of other kinds of things. We'll have to understand
that Self, itself, is not a single thing.

============ NEW THEORIES ABOUT MINDS AND MACHINES. ============

It is too easy to say things like, "Computer can't do (xxx), because they
have no feelings, or thoughts". But here's a way to turn such sayings into
foolishness. Change them to read like this. "Computer can't do (xxx),
because all they can do is execute incredibly intricate processes, perhaps
millions at a time". Now, such objections seem less convincing -- yet all
we did was face one simple, complicated fact: we really don't yet know
what the limits of computers are. Now let's face the other simple fact: our
notions of the human mind are just as primitive.

Why are we so reluctant to admit how little is known about how the mind
works? It must come partly from our normal tendency to repress
problems that seem discouraging. But there are deeper reasons, too, for
wanting to believe in the uniqueness and inexplicability of Self. Perhaps
we fear that too much questioning might tear the veils that clothe our
mental lives.

To me there is a special irony when people say machines cannot have
minds, because I feel we're only now beginning to see how minds
possibly could work -- using insights that came directly from attempts to
see what complicated machines can do. Of course we're nowhere near a
clear and complete theory - yet. But in retrospect, it now seems strange
that anyone could ever hope to understand such things before they knew
much more about machines. Except, of course, if they believed that minds
are not complex at all.

Now, you might ask, if the ordinary concept of Self is so wrong, what
would I recommend in its place? To begin with, for social purposes, I don't
recommend changing anything - it's too risky. But for the technical
enterprise of making intelligent machines, we need better theories of
how to "represent", inside computers, the kinds of webs of knowledge and
knowhow that figure in everyone's common-sense knowledge systems.
We must develop programs that know, say, what numbers mean, instead of
just being able to add and subtract them. We must experiment with all
sorts of common sense knowledge, and knowledge about that as well.

Such is the focus of some present-day Al research. True, most of the world
of "Computer Science" is involved with building large, useful, but shallow
practical systems, a few courageous students are trying to make
computers use other kinds of thinking, representing different kinds of
knowledge, sometimes, in several different ways, so that their programs
won't get stuck at fixed ideas. Most important of all, perhaps, is making
such machines learn from their own experience. Once we know more
about such things, we can start to study ways to weave these different
schemes together. Finally, we'll get machines that think about themselves
and make up theories, good or bad, of how they, themselves might work.
Perhaps, when our machines get to that stage, we'll find it very easy to
tell it has happened. For, at that point, they'll probably object to being
called machines. To accept that will be will be difficult, but only by this
sacrifice will machines free us from our false mottos.

================== KNOWLEDGE AND COMMON SENSE ==================

We've all enjoyed those jokes about the stupid and literal behavior of
computers. They send us silly checks and bills for $0.00. They can't tell
when we mean "hyphen" from when we mean minus They don't mind
being caught in endless loops, doing the same thing over again a billion
times. This total lack of common sense is one more reason people think
that no machine could have a mind. It's not just that they do only what
they're told, it's also that they're so dumb it's almost impossible to tell
them how to do things right.

Isn't it odd, when you think about it, how even the earliest Al programs
excelled at "advanced" subjects, yet had no common sense? A 1961
program written by James Slagle could solve calculus problems at the
level of college students; it even got an A on an MIT exam. But it wasn't till
around 1970 that we managed to construct a robot programs that could see
and move well enough to handle ordinary things like children's building
blocks and do things like stack them up, take them down, rearrange them,
and put them in boxes.

Why could we make programs do those grown-up things before we could
make them do those childish things? The answer is a somewhat
unexpected paradox: much "expert" adult thinking is basically much
simpler than what happens in a child's ordinary play! It can be harder to
be a novice than to be an expert! This is because, sometimes, what an
expert needs to know and do can be quite simple -- only, it may be very
hard to discover, or learn, in the first place. Thus, Galileo had to be smart
indeed, to see the need for calculus. He didn't manage to invent it. Yet any
good student can learn it today.

The surprising thing, thus, was that when it was finished, Slagle's
program needed only about a hundred "facts" to solve its college-level
calculus problems. Most of them were simple rules about algebra. But
others were about how to guess which of two problems is likely to be
easier; that that kind of knowledge is especially important, because it
helps the program make good judgments about what to do next. Without
this such programs only thrash about; with it they seem much more
purposeful. Why do human students take so long to learn such rules? We
do not know.

Today we know much more about making such "expert" programs -- but
we still don't know much more about making programs with more
"common sense". Consider all the different things that children do, when
they play with their blocks. To build a little house one has to mix and
match many different kinds of knowledge: about shapes and colors, space
and time, support and balance, stress and strain, speed, cost, and keeping
track. An expert sometimes can get by with deep but narrow bodies of
knowledge - but common sense is, technically, a lot more complicated.

Most ordinary computer programs do just the things they're programmed
for. Some Al programs are more flexible; when anything goes wrong,
they can back up to some previous decision and try something else. But
even that is much too crude a base for much intelligence. To make them
really smart, we'll have to make them more reflective. A person tries,
when things go wrong, to understand what's going wrong, instead of just
attempting something else. We look for causal explanations, or excuses,
and, when we find them, add them to our networks of belief and
understanding. We do intelligent learning. Some day programs, too, could
do such things -- but first we'd need a lot more research to find out how.

================== UNCONSCIOUS FEARS AND PHOBIAS. ==================

I'll bet that when we try to make machines more sensible, we'll find that
learning what is wrong turns out to be as important as learning what's
correct. In order to succeed, it helps to know the likely ways to fail. Freud
talked about censors in our minds, that keep us from forbidden acts or
thoughts. And, though those censors were proposed to regulate our social
activity, I think we use such censors, too, for ordinary problem solving --
to know what not to do. Perhaps we learn a new one each time anything
goes wrong, by constructing a process to recognize similar
circumstances, in some "subconscious memory".

This idea is not popular in contemporary psychology, perhaps because
censors only suppress behavior, so their activity is invisible on the
surface. When a person makes a good decision, we tend to ask what "line
of thought" lies behind it. But we don't so often ask what thousand
prohibitions might have warded off a thousand bad alternatives. If
censors work inside our minds, to keep us from mistakes and absurdities,
why can't we feel that happening? Because, I suppose, so many thousands
of them work at once that, if you had to think about them, you'd never get
much done. They have to ward off bad ideas before you "get" those bad
ideas.

Perhaps this is one reason why so much of human thought is
"unconscious". Each idea that we have time to contemplate must be a
product of many events that happen deeper and earlier in the mind. Each
conscious thought must be the end of processes in which it must compete
with other proto-thoughts, perhaps by pleading little briefs in little
courts. But all that we do sense of that are just the final sentences.

And how, indeed, could it be otherwise? There's no way any part of the
mind could know everything that happens in the rest. Our conscious
minds must be like high executives, who can't be burdened with the small
details. There's only time for summaries from other, smaller parts of
mind, that know much more about much less; the ones that do the real
work.


================== SELF-CONSCIOUS COMPUTERS. ==================

Then, is it possible to program a computer to be self-conscious? People
usually expect the answer to be "no". What if we answered that machines
are capable, in principle, of even more and better consciousness than
people have?

I think this could be done by providing machines with ways to examine
their own mechanisms while they are working. In principle, at least, this
seem possible; we already have some simple Al programs that can
understand a little about how some simpler programs work. (There is a
technical problem about the program being fast enough, to keep up with
itself, but that can be solved by keeping records.) The trouble is, we still
know far too little, yet, to make programs with enough common sense to
understand even how today's simple Al problem-solving programs work.
But once we learn to make machines that are smart enough to understand
such things, I see no special problem in giving them the "self-insight"
they would need to understand, change, and improve themselves.

This might not be so wise to do. But what if it turns out that the only way
to make computers much smarter is to make them more self-conscious?
For example, it might turn out to be too risky to assign a robot to
undertake some important, long-range task, without some "insight" about
it's own abilities. If we don't want it to start projects it can't finish, we'd
better have it know what it can do. If we want it versatile enough to solve
new kinds of problems, it may need to be able to understand how it
already solves easier problems. In other words, it may turn out that any
really robust problem solver will to understand itself enough to change
itself. Then, if that goes on long enough, why can't those artificial
creatures reach for richer mental lives than people have. Our own
evolution must have constrained the wiring of our brains in many ways.
But here we have more options now, since we can wire machines in any
way we wish.

It will be a long time before we learn enough about common sense
reasoning to make machines as smart as people are. Today, we already
know quite a lot about making useful, specialized, "expert" systems. We
still don't know how to make them able to improve themselves in
interesting ways. But when we answer such questions, then we'll have to
face one, even stranger, one. When we learn how, then should we build
machines that might be somehow "better" than ourselves? We're lucky
that we have to leave that choice to future generations. I'm sure they
won't want to build the things that well unless they find good reasons to.

Just as Evolution changed man's view of Life, Al will change mind's view
of Mind. As we find more ways to make machines behave more sensibly,
we'll also learn more about our mental processes. In its course, we will
find new ways to think about "thinking" and about "feeling". Our view of
them will change from opaque mysteries to complex yet still
comprehensible webs of ways to represent and use ideas. Then those
ideas, in turn, will lead to new machines, and those, in turn, will give us
new ideas. No one can tell where that will lead and only one thing's sure
right now: there's something wrong with any claim to know, today, of
any basic differences between the minds of men and those of possible
machines.