Part 23

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.

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?”

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.”