Part 21

“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.

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.

These homely devices do not possess sufficient “intelligence” to raise any fears in our minds. The abacus, for example, displays only what we might charitably call the property of memory. It has a certain number of rows, each row with a fixed number of beads. While it is not fallible, as is the human who uses it, it is far more limited in scope. All it can ever do is help us to add or subtract, and if we are clever, to multiply, divide, do square roots, and so on. If we are looking for purposive behavior in computing machines, it is only when we get to the adding machine that a glimmer appears. When a problem is set in and the proper button pushed, this device is compelled to go through the gear-whirring or whatever required to return it to a state of equilibrium with its problem solved.

So far we might facetiously describe the difference in the goal-seeking characteristics of man and machine by recalling that man seeks lofty goals like climbing mountains simply because they are there, while the computer seeks its goal much like the steel ball in the pinball machine, impelled by gravity and the built-in springs and chutes of the device. When we come to a more advanced computer, however, we begin to have difficulty in assessing characteristics. For the JOHNNIAC, built by Rand and named for John von Neumann, can prove the propositions in the Principia Mathematica of Whitehead and Russell. It can also “learn” to play a mediocre game of chess.

If we investigate the workings of a digital computer, we find much to remind us of the human brain. First is the obvious similarity of on-off, yes-no operation. This implies a power source, usually electrical, and a number of two-position switches. The over-all configuration of the classic computer resembles, in principal if not physical appearance, that of the human brain and its accessories.

As we have learned, the electronic computer has an input section, a control, an arithmetic (or logic) section, a memory, and an output. Looking into the arithmetic and memory sections, we find a number of comparisons with the brain. The computer uses power, far more than the brain. A single transistor, which forms only part of a neuron, may use a tenth of a watt; the brain is ahead on this score by a factor of millions to one.

Electronic switches have an advantage over the neuron in that they are much faster acting. So fast have they become that engineers have had to coin new terms like nanosecond and picosecond, for a billionth and a trillionth of a second. Thus, the computer’s individual elements are perhaps 100,000 times faster than those of the brain.

There is no computer in existence with the equivalent of 10 billion neurons. One ambitious system of computers does use half a million transistors, plus many other parts, but even these relatively few would not fit under a size 7-1/2 hat. One advanced technique, using a “2-D” metal film circuitry immersed in liquid helium for supercooling, hopefully will yield a packaging density of about 3-1/2 million parts per cubic foot in comparison with the brain’s trillion-part density.