Today, we wonder how humans and computers think.
The University of Houston's College of Engineering
presents this series about the machines that make
our civilization run, and the people whose
ingenuity created them.
As the blast-front of the
computer revolution sweeps outward, a question nags
us. "How will the fully-evolved computer differ
from our own brain -- what'll be left for us?" We
haven't found the answer, but Ann Finkbeiner gives
a progress report. She tells us about two ways
computer engineers try to copy human thinking.
The first way is to use what's called artificial
intelligence. That means trying to convert human
thought into rules. Suppose we want a computer to
play chess. We teach it the rules for moving
chessmen and for anticipating the consequences of
moves. We tell it not to put its knight there,
because the queen'll take it. Next we go to
chess-masters and ask them to tell us how they make
decisions. We reduce their answers to rules -- as
best we can -- and feed them into the computer,
too. When we're through, we have a program that can
play pretty well against the best chess-players.
That works because chess is fairly logical. And the
computer is way ahead of us in manipulating logic.
On the other hand, we have an edge over computer
chess programs because we ultimately use something
beyond logic. We can generalize and associate
ideas; and that's quite beyond an
artificial-intelligence machine. In simple terms,
the machine and the human act out the balance
between Captain Kirk and Mr. Spock.
Now people are looking at a second method for
replicating human thought. It's called the neural
network. The idea is to copy the actual machinery
of the human brain -- not just to try to write
rules for thinking. The brain is made up of
building blocks called neurons. They're fairly
simple, but they have an important feature. Their
logical action is adaptive. Neurons can learn.
Experiments with machines using neural elements are
beginning to give some startling results. What one
can do is find an answer that's good enough very
quickly. Asked to multiply 7 by 6, one might tell
you the answer is about 40. That's not impressive
at face value, but it's what we do when -- say --
we recognize a face. It'd take enormous
digital-computer capacity to do that precisely. But
we don't do it precisely. We don't compare every
feature with the ones stored in our memory. We
recognize friends instantly -- but approximately --
by comparing a few key features. A digital computer
can't think that way, but a neural network machine
The jury is still out on these new machines. They
do hold a lot of promise in tasks like reading
fingerprints and translating languages. More than
that, they help us understand human thinking. Most
fascinating: they tell us that our ability to
tolerate imperfection is what gives us an edge over
the digital computer.
I'm John Lienhard, at the University of Houston,
where we're interested in the way inventive minds