operating, machines. As pointed out earlier, the brain operates in
parallel and makes up for its slower operating individual parts in this
way. Designers are now working on parallel operation for computers, an
improvement that may be even more important than random-access memory.
_Bionics_
It is obvious that while there are many differences in the brain and the
computer there are also many striking similarities. These similarities
have given rise to the computer-age science of “bionics.” A coinage of
Major J. E. Steele of the Air Force’s Wright Air Development Center,
_bionics_ means applying knowledge of biology and biological techniques
to the design of electronic devices and systems. The Air Force and other
groups are conducting broad research programs in this field.
As an indication of the scope of bionics, Dr. Steele himself is a flight
surgeon, primarily trained as neurologist and psychiatrist, with
graduate work in electronics and mathematics. Those engaged in bionics
research include mathematicians, physical scientists, embryologists,
philosophers, neurophysiologists, psychologists, plus scientists and
engineers in the more generally thought of computer fields of
electronics and other engineering disciplines.
A recent report from M.I.T. is indicative of the type of work being
done: “What the Frog’s Eyes Tell the Frog.” A more ambitious project is
one called simply “Hand,” which is just that. Developed by Dr. Heinrich
Ernst, “Hand” is a computer-controlled mechanical hand that is described
as the first artificial device to possess a limited understanding of the
outside world. Although it will undoubtedly have industrial and other
applications, “Hand” was developed primarily as a study of the cognitive
processes of man and animals.
Besides the Air Force’s formal bionics program, there are other research
projects of somewhat similar nature. At Harvard, psychologists Bruner
and Miller direct a Center for Cognitive Studies, and among the
scientists who will contribute are computer experts. Oddly, man knows
little of his own cognitive or learning process despite the centuries of
study of the human mind. It has been said that we know more about
Pavlov’s dog and Skinner’s pigeons than we do about ourselves, but now
we are trying to find out. Some find it logical that man study the
animals or computer rather than his own mind, incidentally, since they
doubt that an intelligence can understand itself anyway.
As an example of the importance placed on this new discipline, the
University of California at Los Angeles recently originated a course in
its medical school entitled “Introduction to the Function and Structure
of the Nervous System,” designed to help bridge the gap between
engineering and biology. In Russia, M. Livanov of the Soviet Academy
Research Institute of Physiology in Higher Nervous Activity has used a
computer coupled with an electric encephaloscope in an effort to
establish the pattern of cortical connections in the brain.
While many experts argue that we should not necessarily copy the brain
in designing computers, since the brain is admittedly a survival device
and somewhat inflexible as a result of its conditioning, it looks
already as if much benefit has come from the bionics approach.
The circuitry of early computers comprised what is called “soldered”
learning. This means that the connections from certain components hook
up to certain other components, so that when switches operated in a
given order, built-in results followed. One early teaching device,
called the Electric Questionnaire, illustrates this built-in knowledge.
A card of questions and answers is slipped over pegs that are actually
terminals of interconnected wires. Probes hooked to a battery are
touched to a question and the supposed correct answer. If the circuit is
completed, a light glows; otherwise the learner tries other answers
until successful.
More sophisticated systems are those of “forced” learning and free
association. Pioneer attempts at teaching a computer to “perceive” were
conducted at Cornell University under contract with the Air Force to
investigate a random-network theory of learning formulated by Dr. Frank
Rosenblatt. Specifically, the Perceptron learns to recognize letters
placed in front of its “eyes,” an array of 400 photocells. The human
brain accomplishes perception in several steps, though at a high enough
rate of operation to be thought of as a continuous, almost
instantaneous, act. Stimuli are received by sense organs; impulses
travel to neurons and form interconnections resulting in judgment,
action if necessary, and memory. The Perceptron machine functions in
much the same manner.
[Illustration:
_Electronics_
Simplified version of a mammalian visual system (A) and Perceptron
simulating the biological network (B).
]
The forced learning technique, in which Perceptron was told when it
correctly identified a letter, and when it missed, was used first. Later
it was found that “corrective” or reinforced teaching, which notes only
errors, was more effective. After Perceptron had seen each letter
fifteen times and received proper correction, it could subsequently
identify all the letters correctly.
Announcement of Perceptron triggered many wild headlines and a general
misconception in the public mind. Dr. Rosenblatt and other developers
wisely refuse to comment on the potential of his machine, but the number
of experiments being conducted indicates wide scientific interest, and
_perceptron_ has attained the prestige of an uncapitalized generic term.
However, the theory of its random process has been questioned by
scientists including Theodore Kalin, one of the builders of an early
electrical logic machine. Kalin feels that intelligence presupposes a
certain minimum of _a priori_ knowledge: the wired-in learning of the
computer or the instincts or inherited qualities of animals. This of
course echoes the thoughts of Kant who deplored the notion as similar to
all the books and papers in a library somehow arranging themselves
properly on the shelves and in filing cabinets.
Indeed, the whole idea of finding human intelligence mirrored in the
electronic innards of the computer has been flatly denounced at some
scientific symposiums. Computers given an intelligence test at the
University of Michigan “flunked,” according to researchers. Another
charge is that the reaction of the brain’s neuron depends on its history
and thus cannot be compared with the computer. However, other
researchers seem to have anticipated this weakness and are working on
electronic or electrochemical neurons that also are conditioned by their
input. Despite criticism, the bionics work proceeds on a broad front.
More recently a machine called Cybertron has been developed by the
Raytheon Company. This more sophisticated machine is being trained to
recognize sonar sounds, using the corrective technique. If Cybertron
errs, the teacher pushes a “goof” button. When the machine is fully
developed, Raytheon feels it will be able to recognize all typical
American word sounds, using its 192 learning elements, and to type them
out.
Computers generally do “logical” operations. Many human problems do not
seem to be logical, and can be solved only by experience, as the
mathematician Gödel demonstrated some years ago. Since Cybertron solves
such “alogical” problems, its builders prefer not to call it a computer,
but rather a self-organizing data-processor that adapts to its
environment. Among the variety of tasks that Cybertron could perform are
the grading of produce and the recognition of radar signals. Raytheon
foresees wide application for Cybertron as a master learner with
apprentice machines incapable of learning but able to “pick the brains”
of Cybertron and thus do similar tasks.
[Illustration:
_Cornell Aeronautical Laboratory_
With the letter C in its field of view, Perceptron’s photocells at top
center are activated. Simultaneously, response units in panel at
right identify the letter correctly.
]
The assembly of machines like Perceptron and Cybertron requires elements
that simulate the brain’s neuron. One such component which has evolved
from bionics research is the Artron, or artificial neuron. Inside the
Artron are logic gates and inhibit gates. By means of reward or
punishment, the Artron learns to operate a “statistical switch” and send
impulses to other Artrons or to a readout. There are two interesting
parallels here besides the operation of a simulated neural net. One is
the statistical approach to decisions and learning. The late John von
Neumann theorized that the brain’s actions might be statistical, or
based on probability. Second, the designers of Artron see a similarity
in its operation and Darwin’s theory of natural selection.
Another new component in the bionics approach is the “neuristor.” This
semiconductor diode simulates the axon, the nerve fiber that connects
with the neuron. Another device is the “memistor,” unique in that it
uses electrochemical phenomena to function as a memory unit. A different
kind of artificial neuron called MIND is made up of magnetic cores.
There is another plus factor in this duplication of what we think is the
system used by the brain. While one neuron may not be as reliable as a
vacuum tube or transistor, the complete brain is millions of times more
dependable than any of its single parts. This happy end result is just
the reverse of what man has come up with in his complex computer
systems. For instance, individual parts in the Minuteman missile must
have a reliability factor of 99.9993% so that the system will have a
fair chance of working properly. Duplication of the brain’s network may
well lead to electronic systems that are many times more reliable than
any of their individual parts.
Bionics is apparently a fruitful approach, both for benefiting computer
technology and for learning more about the human brain. As an example,
consider the fact that work with the Perceptron indicated that
punishment was more effective in the learning process than punishment
and reward together. This of course does not say that such a method
would work best with a human subject, but if separate tests with human
beings proved a similar result, it might then be safe to infer some
similarity between the human and computer brain.
One of the biggest roadblocks to implementation of a humanlike neural
net is economic. Since there are some 10 billion neurons in the brain,
and early electronic neurons consisted of several components including
transistors which are a bargain at $2 each, building such a computer
might double our national debt. Bionics workers have been thinking
dreamily in terms of something like one cent per artificial neuron. This
is a ridiculously low figure, but even at that a one-tenth brainpower
computer with only a billion penny neurons would cost $10 million for
those components alone!
[Illustration:
_Cornell Aeronautical Laboratory_
Random wiring network between the Mark I Perceptron’s 400 photocell
sensors and the machine’s association units.... The Mark I has ten
sensory output connections to each of its 512 association units.
]
Not yet whipped, researchers are now thinking in terms of mass-producing
lattices of thin metal, in effect many thousands of elements in a
microscopic space, and propagating electrochemical waves rather than an
electrical current through them.
[Illustration:
_Raytheon Co._
When Cybertron doesn’t catch on to a new lesson, engineers push the
goof button to punish the machine. When it learns correctly it is
allowed to continue its studies with no interruption, thus it
constantly improves its skill.
]
Other ideas include getting down to the molecular level for components.
If this is achieved it will be a downhill pull, for even the human
neuron consists of many molecules. Farfetched as these ideas seem,
packaging densities of 100 billion per cubic foot are being talked of as
foreseeable in less than ten years. This is only about ten times as
bulky as the goal, the human brain, and when it is achieved the computer
will be entitled to a big head.
_The Computer as a Thinker_
About the time Johnny was having all his trouble reading, a computer
named JOHNNIAC was given the basic theorems needed, and then asked to
prove the propositional calculus in the _Principia Mathematica_, a task
certainly over the heads of most of us. The computer waded through the
job with no particular strain, and even turned in one proof more elegant
than human brains had found before. When the same problems were given to
an engineer unfamiliar with that branch of mathematics, his verbalized
problem-solving technique paralleled that of JOHNNIAC. Asked if he had
been thinking, the engineer said he “surely thought so!”
In his interesting department in _Scientific American_, mathematical
gamester Martin Gardner describes a simple set of punched cards for
solving the type of logic problem discussed earlier in this chapter.
Using these cards and a simple digital type of manipulation, we happily
learn that Camille surely could. The problem is a simple, three-premise
type in two-valued logic and can be solved by any self-respecting
digital computer in a split second. A few demonstrations like this give
a rather disconcerting insight into our brain’s limitations and build
more respect for the computer’s intelligence.
When we hear of expensive computers apparently frittering away their
valuable time playing games we may well wonder how come. But games, it
turns out, are an ideal testing ground for problem-solving ability and
hence intelligence. Back in 1957, computer experts Simon and Newell
predicted that in ten years the chess champion of the world would be a
computer. Master players most likely laughed up their sleeves, and thus
far the electronic machine has done no better than play a routine game
against a human amateur. This, of course, is not a mean achievement.
Wise heads are supposed to have responded to the prediction with “So
what?”
[Illustration:
Photo at left from _Organization of the Cerebral Cortex_, by D. A
Sholl, J. Wiley and Sons. Right, _General Electric Research
Laboratory_
Photo at right shows a “crossed-film cryotron” shift register—an
advanced computer element. The separation of active crossovers shown
is comparable to the separation of nerve cells in the section of cat
brain shown at left.
]
Alex Bernstein of IBM worked out a program for the 704 computer in which
the machine looks ahead four moves before each of its plays. Even this
limited look ahead requires 2,800 calculations, and the 704 takes eight
minutes deliberating. Occasionally it makes a move the experts rate as
masterful.
Chess is a far more complex game even than it appears to those of us on
the sidelines. In an average game there are forty moves and each has
about thirty possibilities. So far this sounds innocuous, but
mathematics shows that there are thus 10^{120} possible moves in any one
game. This number is a 1 followed by 120 zeros, and to underline its
size it has been estimated that even if a million games a second were
played, the possibilities would not be exhausted in our lifetime!
Obviously human chess wizards do not investigate all possible moves.
Instead they use heuristic reasoning, or hunch playing, to cut corners.
The JOHNNIAC computer is investigating such approaches to
computer-playing chess, in a movement away from rigorously programmed
routines or “algorithms.” Algorithms are formulas or equations such as
the quadratic equation used in finding roots. If indeed the computer
does dethrone the human chess champ by 1967, it will be exceedingly hard
to argue that the machine is not thinking.
The word “heuristic” comes from the Greek _heuriskein_, meaning to
discover or invent. An example of what it is and how important it is can
be seen in the recent disproving of a famous conjecture made by the
mathematician Euler some 180 years ago. Euler was interested in the
properties of so-called “magic squares” in which letters are arranged
vertically and horizontally. While it is possible to arrange the letters
_a_, _b_, _c_, _d_, and _e_ in such a square so that all are present in
each row and in different order, Euler didn’t think such was the case
with a square having six units on a side. He tried it, visualizing
officers of different rank arranged in rows. Convinced that it would not
work, he extended his educated guess to squares having units of ten,
fourteen, and other even numbers not divisible by four. He didn’t
actually prove his conjecture, because the amount of paperwork makes it
practically impossible.
In 1901 a mathematician did try all the possible configurations of the
square of six units and found that Euler was indeed correct. It was
assumed that ten was impossible too, until 1958 when three American
mathematicians spoiled Euler’s theory by finding workable magic squares
having ten units per side. They did not do this by exhausting all the
possibilities, for such a chore would have been humanly impossible. In
fact, a computer labored for 100 hours and completed only a tiny
fraction of the job. The square-seekers concluded that it would take
even the high-speed computer upwards of a century to do the job, so
instead they used hunches or inspired guesses, working out a heuristic
for the task. The point of importance is that not only man, but the
computer as well, despite its fantastic speed, must learn to use
heuristic reasoning rather than blindly plowing through all possible
solutions. There are just too many numbers!
Computers play other games too, from tick-tack-toe and Nim, which it
plays flawlessly, to Go and checkers. Dr. Arthur Samuel of IBM has
taught the 704 computer to play checkers well enough to beat him
regularly, though Dr. Samuel, scientist that he is, admits he is not a
great checker-player. He has used two types of learning in the program:
“rote” and “generalization.” So far these have been used separately,
while human players use both types of learning in a game.
American scientists visiting Russia recently reported that the Russians,
like some of us, were amazed to hear that computer time was allotted to
the mere playing of games. The real goal in all this game-playing is to
learn how to do other more important things. Gaming is being applied to
war strategy and to business management. Corporation executives are
playing games with computers that simulate the operation of their firms,
both to improve methods and to learn about themselves and their
employees. A General Problem-Solver computer is being developed too; one
which can solve problems like the cannibals and the missionaries and
then do mathematical equations and other types of thinking. As was
pointed out, when the computer’s method of solving a problem is compared
with the protocol used by a person (by having him think aloud as he goes
through the problem) it is seen that both use pretty much the same
tricks and short cuts.
As the computer keeps closing the gap, we can push the goal back by
redefining our terms. This is much like dangling a carrot on a stick,
and with the computer doggedly taking the part of the donkey, it is a
pretty good technological flail. By making the true test of intelligence
something like artistic creativity, we can rule out the machine unless
it can write poetry, compose music, or paint a picture. So far the
computer has done the first two, and the last poses no particular
problem, though debugging the machine might be a messy operation. True,
the machine’s poetry is only about beatnik level:
CHILDREN
Sob suddenly, the bongos are moving.
Or could we find that tall child?
And dividing honestly was like praying badly,
And while the boy is obese, all blast could climb.
First you become oblong,
To weep is unctious, to move is poor.
This masterpiece, produced by a computer in the Librascope Laboratory
for Automata Research, is not as obscure as an Eliot or a Nostradamus.
Computer music has not yet brought audiences to their feet in Carnegie
Hall. The machine’s detractors may well claim that it has produced
nothing truly great; nothing worthy of an Einstein or Keats or Vermeer.
But then, how many of us people have?
There is yet another way we can ban the computer from membership in our
human society. While human beings occasionally think they are machines,
and Dr. Bruno Bettelheim has documented a case history of “Joey” who was
so convinced that he was a machine that he had to keep himself plugged
in to stay alive, no machine has yet demonstrated that it is consciously
aware of itself, as human beings are.
Machines are, hopefully, objective. Consciousness seems to be subjective
in the extreme; indeed, some feel that it is a thing one of us cannot
hope to convey as intelligence to another and thus has no scientific
importance. It is also noted that the thinking and learning processes
can be carried out with no need for consciousness of what we are doing.
An example given is that of the cyclist who learns, without being
“aware” of the fact, that to turn his machine left he must first make a
slight swing to the right in order to keep from falling outward during
his left turn. This observation in itself is not final proof of the
pudding, of course, unless we are aiming only to make a mechanical
bike-rider, but many of our other actions are carried out more or less
mechanically without calling attention to themselves. Just as certainly,
however, the thing called consciousness plays a vital role in human
thinking. Perhaps the machine must learn to do this before it can be
truly creative.
Although we have described some fairly “exotic” devices, it should be
remembered that the computers in use outside of the laboratory today are
fairly old-fashioned second-generation models. They have progressed from
vacuum tubes or mechanical relays to “solid-state” components. When
Artrons and neuristors and memistors and other more sophisticated parts
are standard, we can look for a vast increase in the brain power of
computers.
The Gilfillan radar ground-controlled-approach system for aircraft that
“sees” the plane on the radar scope, computes the proper path for it to
follow, and then selects the right voice commands from a stored-tape
memory seems to be thinking and acting already. The addition of eyes and
ears plus limbs and locomotion to the computer, foreseen now in the
photocell eyes of Perceptron, the ears of Cybertron, and dexterity of
Mobot and Hand, will move the computer from mere brain to robot.
Some people profess to worry about what will happen when the computer
itself realizes that it is thinking, calling to mind the apocryphal
story of the machine that was asked if there was a God. After brief
cogitation, it said, “_Now_ there is.” To offset such a chilling
possibility, it is comforting to recall the post-office electronic brain
that mistook the Christmas seals on packages for foreign stamps, and the
Army computer that ordered millions of dollars worth of supplies that
weren’t needed. Or perhaps it isn’t comforting, at that!
The question of whether or not a computer actually thinks is still a
controversy, though not as much so as it was a few years ago. The
computer looks and acts as if it is thinking, but the true scientist
prefers to reserve judgment in the spirit of one shown a black sheep
some distance away. “_This_ side is black,” he admitted, “but let’s
investigate further.”
------------------------------------------------------------------------
“_For forms of government let fools contest,
That which is best administered is best._”
—Pope
7: Uncle Sam’s Computers