Part 9
A graphic representation of the metamorphosis from symbolic to actual implementation of Boolean equations follows: The implication of importance is that logic applies equally well whether we are making a qualifying statement such as “A man must have strength _and_ courage to win a barehanded fight with a lion,” or wiring a defensive missile so that it will fire only if a target is within range _and_ is unfriendly.
In the early period of computer design the engineer was faced with the problem of building his own switches and gates. Today many companies offer complete “packaged” components—AND gates, OR gates, and the other configurations. This is the modular approach to building a computer and the advantages are obvious. The designer can treat the components simply as “black boxes” that will respond in a prescribed way to certain input conditions. If he wants, the engineer can go a step further and buy a ready-built logic panel consisting of many components of different types. All he need do to form various logic circuits is to interconnect the proper components with plug-in leads. This brings us to the point of learning what we can do with these clever gates and switches now that we have them available and know something about the way they work.
We talked about the computer adder circuit earlier in this chapter. It is made up of two half-adders, remember, with perhaps an additional OR gate, flip-flop, etc. Each half-adder is composed of two AND gates and an OR gate. So we have put together several basically simple parts and the result is a piece of equipment that will perform addition at a rate to make our heads swim.
There are other things we can do with Boolean logic besides arithmetic. A few gates will actuate a warning signal in a factory in case either of two ventilators is closed and the temperature goes up beyond a safe point; or in case both vents are closed at the same time. We can build a logic computer that will tell us when three of four assembly lines are shut down at the same time, and also which three they are.
[Illustration:
_General Electric Co., Computer Dept._
Electronic computers are built up of many “building blocks” like this one. ]
Logic problems abound in puzzle books, and many of us spend sleepless nights trying to solve them in our heads. An example is the “Farnsworth Car Pool” problem. Rita Farnsworth asks her husband if someone in his car pool can drive for him tomorrow so that she may use the car. Joe Farnsworth replies, “Well, when I asked Pete if he would take my turn he said he was flying to Kansas City today, but he’d be glad to drive tomorrow if he didn’t have to stay over and that his wife has been staying home lately and he will drive her car if she doesn’t go to work. Oscar said that since his own car is due back from the garage tomorrow he can drive it even if his wife does use hers, provided the garage gets his back to him. But if this cold of mine gets any worse I’m going to stay home even if those fellows have to walk to work, so you can certainly have the car if I don’t go to work.” This dialogue of Joe’s confuses Rita and most of us are in the same state.
[Illustration:
_Autonetics Division, North American Aviation, Inc._
Testing an assembled digital computer. ]
The instruction manual for BRAINIAC, a do-it-yourself computer that sells for a few dollars, gives a simple wiring diagram for solving Rita’s dilemma. Electrically the problem breaks down into three OR gates and one AND gate. All Mrs. Farnsworth has to do is set in the conditions and watch the indicator light. If it glows, she gets the car!
These are of course simple tasks and it might pay to hire a man to operate the vents, and ride to work on the bus when the car pool got complicated. But even with relatively few variables, decision-making can quickly become a task requiring a digital computer operating with Boolean logic principles.
[Illustration:
_Science Materials Center_
Problem in logic reduced to electrical circuits. ]
The Smith-Jones-Robinson type of problem in which we must find who does what and lives where is tougher than the car pool—tough enough that it is sometimes used in aptitude tests. Lewis Carroll carried this form of logical puzzler to complicated extremes involving not just three variables but a dozen. To show how difficult such a problem is, an IBM 704 required four minutes to solve a Carroll puzzle as to whether any magistrates indulge in snuff-taking. The computer did it the easy way, without printing out a complete “truth table” for the problem—the method a man would have to use to investigate all the combinations of variables. This job would have taken 13 hours! While the question of the use of snuff is perhaps important only to tobacconists and puzzle-makers, our technical world today does encounter similar problems which are not practical of solution without a high-speed computer. A recent hypothetical case discussed in an electronics journal illustrates this well.
A missile system engineer has the problem of modifying a Nike-Ajax launching site so that it can be used by the new Nike-Hercules missile. He must put in switching equipment so that a remote control center can choose either an Ajax system, or one of six Hercules systems. To complicate things, the newer Hercules can be equipped with any of three different warheads and fly either of two different missions. When someone at the control center pushes a button, the computer must know immediately which if any of the missiles are in acceptable condition to be fired.
This doesn’t sound like too big a problem. However, since there are twelve on-off signals to be considered, and since each has two possible states, there are 4,096 possible missile combinations. Not all these are probable, of course, but there is still sufficient variation to make it humanly impossible to check all of them and close a firing switch in the split second the control center can allow.
The answer lies in putting Boolean algebra on the job, with a system of gates and inverters capable of juggling the multiplicity of combinations. Then when the word comes requesting a missile launch, the computer handles the job in microseconds without straining itself unduly.
Just as Shannon pointed out twenty-five years ago, switching philosophy can be explained best by Boolean logic, and the method can be used not only to implement a particular circuit, but also to actually design the circuit in the first place. A simple example of this can be shown with the easy-to-understand AND and OR gates. A technician experimenting with an AND gate finds that if he simply reverses the direction of current, he changes the gate into an OR gate. This might come as a surprise to him if he is unfamiliar with Boolean logic, but a logician with no understanding of electrical circuits could predict the result simply by studying the truth tables for AND and OR.
Reversing the polarity is equivalent to changing a 1 to a 0 and vice versa. If we do this in the AND gate table, we should not be surprised to find that the result looks exactly like the OR table! It acts like it too, as the technician found out.
Boolean logic techniques can be applied to existing circuits to improve and/or simplify them. Problems as simple as wiring a light so that it can be turned on and off from two or more locations, and those as complex as automating a factory, yield readily to the simple rules George Boole laid down more than a hundred years ago.
Watching a high-speed electronic digital computer solve mathematical problems, or operate an industrial control system with speed and accuracy impossible for human monitors, it is difficult to believe that the whole thing hinges on something as simple as switches that must be either open or closed. If Leibnitz were alive, he could well take this as proof of his contention that there was cosmological significance in the concept of 1 and 0. Maybe there is, after all!
[Illustration:
_Industrial Electronic Engineering & Maintenance_
“Luckily I brought along a ‘loaner’ for you to use while I repair your computer.” ]
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“_Whatever that be which thinks, understands, wills, and acts, it is something celestial and divine._”
—Cicero
6: The Electronic Brain
The idea of a man-made “brain” is far from being new. Back in 1851, Dr. Alfred Smee of England proposed a machine made up of logic circuits and memory devices which would be able to answer any questions it was asked. Doctor Smee was a surgeon, keenly interested in the processes of the mind. Another Britisher, H. G. Wells, wrote a book called _Giant Brain_ in 1938 which proposed much the same thing: a machine with all knowledge pumped into it, and capable of feeding back answers to all problems.
If it was logical to credit “human” characteristics to the machines man contrived, the next step then was to endow the machine with the worst of these attributes. In works including Butler’s _Erewhon_, the diabolical aspects of an intelligent machine are discussed. The Lionel Britton play, _Brain_, produced in 1930, shows the machine gradually becoming the master of the race. A more physical danger from the artificial brain is the natural result of giving it a body as well. We have already mentioned Čapek’s _R.U.R._ and the Ambrose Bierce story about a chess-playing robot without a built-in sense of humor, who strangles the human being who beats him at a game. With these stories as models, other writers have turned out huge quantities of work involving mechanical brains capable of all sorts of mischief. Most of these authors were not as well-grounded scientifically as the pioneering Dr. Smee who admitted sadly that his “brain” would indeed be a giant, covering an area about the size of London!
The idea of the giant brain was given new lease by the early electronic computers that began appearing in the 1940’s. These vacuum-tube and mechanical-relay machines with their rows of cabinets and countless winking lights were seized on gleefully by contemporary writers, and the “brain” stories multiplied gaudily.
Many of the acts of these fictional machines were monstrous, and most of the stories were calculated to make scientists ill. Many of these gentlemen said the only correct part of the name “giant brain” was the adjective; that actually the machine was an _idiot savant_, a sort of high-speed moron. This opinion notwithstanding, the name stuck. One scholar says that while it is regrettable that such a vulgar term has become so popular, it is hardly worth while campaigning against its use.
An amusing contemporary fiction story describes an angry crowd storming a laboratory housing a “giant brain,” only to be placated by a calm, sensibly arguing scientist. The mob dispersed, he goes back inside and reports his success to the machine. The “brain” is pleased, and issues him his next order.
“Nonsense!” scoff most computer people. A recent text on operation of the digital computer says, “Where performance comparable with that of the human brain is concerned, man need have little fear that he will ever be replaced by this machine. It cannot think in any way comparable to a human being.” Note the cautious use of “little,” however.
Another authority admits that the logic machines of the monk Ramón Lull were very clever in their proof of God’s existence, but points out that the monk who invented them was far cleverer since no computer has ever invented a monk who could prove anything at all!
The first wave of ridiculous predictions has run its course and been followed by loud refutations. Now there is a third period of calmer and more sensible approach. A growing proportion of scientists take a middle-of-the-stream attitude, weighing both sides of the case for the computer, yet some read like science fiction.
Cyberneticist Norbert Wiener, more scientist than fictioneer, professes to foresee computerized robots taking over from their masters, much as a Greek slave once did. Mathematician John Williams of the Rand Corporation thinks that computers can, and possibly will, become more intelligent than men.
Equally reputable scientists take the opposite view. Neuro-physiologist Gerhard Werner of Cornell Medical College doubts that computers can ever match the creativity of man. He seems to share the majority view today, though many who agree will add, tongue in cheek, that perhaps we’d _better_ keep one hand on the wall plug just in case.
_Thinking Defined_
The first step in deciding whether or not the computer thinks is to define thinking. Far from being a simple task, this definition turns out to be a slippery thing. In fact, if the computer has done no more than demand this sort of reappraisal of the human brain’s working, it has justified its existence. Webster lists meanings for “think” under two headings, for the transitive and intransitive forms of the verb. These meanings, respectively, start out with “To form in the mind,” and “To exercise the powers of judgment ... to reflect for the purpose of reaching a conclusion.”
Even a fairly simple computer would seem to qualify as a thinker by these yardsticks. The storing of data in a computer memory may be analogous to forming in the mind, and manipulating numbers to find a square root certainly calls for some sort of judgment. Learning is a part of thinking, and computers are proving that they _can_ learn—or at least be taught. Recall of this learning from the memory to solve problems is also a part of the thinking process, and again the computer demonstrates this capability.
One early psychological approach to the man-versus-machine debate was that of classifying living and nonliving things. In _Outline of Psychology_, the Englishman William McDougall lists seven attributes of life. Six of these describe “goal-seeking” qualities; the seventh refers to the ability to learn. In general, psychologist McDougall felt that purposive behavior was the key to the living organism. Thus any computer that is purposive—and any commercial model had better be!—is alive, in McDougall’s view. A restating of the division between man and machine is obviously in order.
Dr. W. Ross Ashby, a British scientist now working at the University of Illinois, defines intelligence as “appropriate selection” and goal-seeking as the intelligent process _par excellence_, whether the selecting is done by a human being or by a machine. Ashby does split off the “non goal-seeking” processes occurring in the human brain as a distinct class: “natural” processes neither good nor bad in themselves and resulting from man’s environment and his evolution.
Intelligence, to Ashby, who long ago demonstrated a mechanical “homeostat” which showed purposive behavior, is the utilization of information by highly efficient processing to achieve a high intensity of appropriate selection. Intelligent is as intelligent does, no distinction being made as to man or machine. _Humanoid_ and _artificial_ would thus be meaningless words for describing a computer. Ashby makes another important point in that the intelligence of a brain or a machine cannot exceed what has been put into it, unless we admit the workings of magic. Ashby’s beliefs are echoed in a way by scientist Oliver Selfridge of Lincoln Laboratory. Asked if a machine can think, Selfridge says, “Certainly; although the machine’s intelligence has an elusive, _unnatural_ quality.”
“Think, Hell, COMPUTE!” reads the sign on the wall of a computer laboratory. But much of our thinking, perhaps some of the “natural” processes of our brains, doesn’t seem to fit into computational patterns. That part of our thinking, the part that includes looking at pretty girls, for example, will probably remain peculiar to the human brain.
_The Human Brain_
Mundy Peale, president of Republic Aviation Corporation, addressing a committee studying the future of manned aircraft, had this to say:
Until someone builds, for $100 or less with unskilled labor, a computer no larger than a grapefruit, requiring only a tenth of a volt of electricity, yet capable of digesting and transmitting incoming data in a fraction of a second and storing 10,000 times as much data as today’s largest computers, the pilots of today have nothing to worry about.
The human brain is obviously a thing of amazing complexity and fantastic ability. Packed into the volume Mr. Peale described are some 10 _billion_ neurons, the nerve cells that seem to be the key to the operation of our minds. Hooked up like some ultra-complicated switchboard, the network of interconnections stores an estimated 200,000,000,000,000,000,000 bits of information during a lifetime! By comparison, today’s most advanced computers do seem pathetically unimpressive.
We have discussed both analog and digital computers in preceding chapters. It is interesting to find that the human brain is basically a digital type, though it does have analog overtones as well. Each of the neurons is actually a switch operated by an electric current on a go/no-go, all-or-nothing basis. Thus a neuron is not partly on or partly off. If the electrical impulse exceeds a certain “threshold” value, the switch operates.
Tied to the neurons are axons, the long “wires” that carry the input and output. The axons bring messages from the body’s sensors to the neurons, and the output to other neurons or to the muscles and other control functions. This grapefruit-size collection of electrochemical components thus stores our memories and effects the operation we call thinking.
Since brain impulses are electrical in nature, we speak of them in electrical terms. The impulses have an associated potential of 50 millivolts, that is, fifty thousandths of a volt. The entire brain dissipates about 10 watts, so that each individual neuron requires only a billionth of a watt of power. This amount is far less than that of analogous computer parts.
A neuron may take a ten-thousandth of a second to respond to a stimulus. This seemingly rapid operation time turns out to be far slower than present-day computer switches, but the brain makes up for this by being a “parallel operation” system. This means that many different connections are being made simultaneously in different branches, rather than being sequential, or a series of separate actions.
Packaging 10 billion parts in a volume the size of a grapefruit is a capability the computer designer admires wistfully. Since the brain has a volume of about 1,000 cubic centimeters, 10 million neurons fit into a space of one cubic centimeter! A trillion would fit in one cubic foot, and man-made machines with even a million components per cubic foot are news today.
Even when we are resting, with our eyes closed, a kind of stand-by current known as the alpha rhythm is measurable in our brains. This current, which has a frequency of about 10 cycles per second, changes when we see or feel something, or when we exercise the power of recall. It disappears when we sleep soundly, and is analogous to the operating current in a computer. Also, there is “power” available locally at the neurons to “amplify” weak signals sufficiently to trigger off following branches of neurons.
Philosophers have proposed two general concepts of the human brain and how it functions. The _a priori_ theory presupposes a certain amount of “wired-in” knowledge: instincts, ideals, and so on. The other theory, that of the _tabula rasa_ or clean slate new brain, argues that each of us organizes an essentially random net of nerves into ordered intelligence. Both theories are being investigated with computers, and as a result light is beginning to be shed on the workings of our brains.
[Illustration:
_The Upjohn Company, Ezra Stoller Associates Photo_
“A moment at a concert” is diagrammed by brain model, showing eyes, ears, nerves, and structures analogous to brain. Picture at top represents perception. ]
There is another division of philosophical thought in the mechanistic versus _elan vital_ argument. In other words, is the entire mind to be found in its constituent parts, or is there an intangible extra something that really breathes life into us? Whatever the correct concept, the brain does record impressions it can later recall. No one yet knows just how this is done, but several theories have been advanced. One of these describes a “chain circuit” set up in a neuron network by messages from the body’s sensors. This circuit, once started, continues to circle through the brain and is on tap whenever that particular experience needs to be recalled. The term “reverberate” is used in connection with this kind of memory, seeming to be a good scientific basis for the poetic “echoes of the past.” Reverberation circuits also provide the memory for some computers.
Among other explanations of memory is that of conditioning the neurons to operate more “easily,” so that certain paths are readily traversed by brain impulses. This could be effected by chemical changes locally, and such a technique too is used in computers.
However the brain accomplishes its job, it is certain that it evolved in its present form as a result of the environment its cells have had to function in for billions of years. Its prime purpose has been one of survival, and for this reason some argue that it is not particularly well adapted to abstract reasoning. Although the brain can do a wide variety of things from dreaming to picking out one single voice amid the hubbub of noise at a social gathering—a phenomenon scientists have given the descriptive name of “cocktail party effect”—men like Ashby consider it a very inflexible piece of equipment not well suited to pure logic. As a test of your brain as a logical device, consider the following problem from the Litton Industries “Problematical Recreations.”
If Sara shouldn’t, then Wanda would. It is impossible that the statements: “Sara should” and “Camille couldn’t” can both be true at the same time. If Wanda could, then Sara should and Camille could. Therefore Camille could. Is this conclusion valid?
If your head starts to swim, you are not alone. Very few humans solve such problems easily. Interestingly, those who do, make good computer programmers.
_The Computer’s Brain_
Just as we have an anthropomorphic God, many people have done their best to endow the computer with human characteristics. Not only in fiction but also in real life, the electronic brains have been described as neurotic and frustrated on occasion, and also as being afraid and even having morning sickness! A salesman for a line of computers was asked to explain in understandable terms the difference between two computers whose specifications confused a customer. “Let’s put it this way,” the salesman said, “The 740 thinks the 690 is a moron!”
We can begin to investigate the question of computer intelligence by again looking up a definition. The word “compute” means literally to think, or reckon, with. Early computers such as counting sticks, the abacus, and the adding machine are obviously something man thinks with. Even though we may know the multiplication tables, we find it easier and _safer_ to use a mechanical device to remember and even to perform operations for us.