Authors: Vernor Vinge
The trouble is, these tiny inner “languages” soon become incomprehensible, for a reason that is simple and inescapable. It is not the same as the familiar difficulty of translating between two different human languages; we understand the nature of
that
problem, which arises because human languages are so huge and rich that it is hard to narrow meanings down: we call that “ambiguity”. However, when we try to understand the tiny languages at the lower levels of the mind, we have the opposite problem—because
the smaller be two languages, the harder it will be to translate between them, not because there are too many meanings but too few. The fewer things two systems do, the less likely it is that something one of them can do will correspond to anything at all the other one can do.
Then, no translation is possible. This worse than mere ambiguity because even when a problem seems hopelessly complicated, there always can be hope. But, when a problem is hopelessly simple, there can’t be any hope at all.
Now, finally, let’s return to the question about how much a simulated life inside a world inside a machine could resemble our real life “out here”. My answer, as you know by now, is that it could be very much the same—since we, ourselves, already exist as processes imprisoned in machines inside machines! Our mental worlds are already filled with wondrous, magical, symbol–signs, which add to every thing we “see” its ‘meaning’ and ‘significance’. In fact, all educated people have already learned how different are our mental worlds than the “real worlds” that our scientists know.
Consider the table in your dining room; your conscious mind sees it as having familiar functions, forms, and purposes. A table is “a thing to put things on”. However, our science tells us that this is only in the mind; the only thing that’s “really there” is a society of countless molecules. That table seems to hold its shape only because some of those molecules are constrained to vibrate near one another, because of certain properties of force-fields that keep them from pursuing independent trajectories. Similarly, when you hear a spoken word, your mind attributes sense and meaning to that sound—whereas, in physics, the word is merely a fluctuating pressure on your ear, caused by the collisions of myriads of molecules of air—that is, of particles whose distances are so much less constrained.
And so—let’s face it now, once and for all: each one of us already has experienced what it is like to be simulated by a computer!
“Ridiculous,” most people say, at first: “I certainly don’t feel like a machine!”
But what makes us so sure of that? How could one claim to know how something feels, until one has experienced it? Consider that either you are a machine or you’re not. Then, if, as you say, you aren’t a machine, then you are scarcely in any position of authority to say how it feels to be a machine.
“Very well, but, surely then, if I were a machine, then at least I would be in a position to know that!
Hah. That is a typically human, thoughtless presumption. It amounts to claiming that,
“I think, therefore I know how thinking works.”
But as we’ve seen, there are so many levels of machinery between our conscious thoughts and how they’re made that to say such a thing is as absurd as to say, “I drive, therefore I know how engines work!”
“Still, even if the brain is a kind of computer, you must admit that its scale is unimaginably large. A human brain contains many billions of brain cells—and, probably, each cell is extremely complicated by itself. Then, each cell is interlinked in complicated ways to thousands or millions of other cells. You can use the word machine for that but, surely, that makes little sense because no one ever could possibly build anything of that magnitude!”
Certainly, most persons regard it as belittling to be compared to a machine; it is like being treated as trivial. And, indeed, such a comparison is indeed insulting—so long as the name “machine” still carries the old meanings it had in times gone by. For thousands of years, we have used such words to arouse images of pulleys, levers, locomotives, typewriters, and simple other sorts of things; similarly, in modern times, the word “computer” has evoked thoughts about adding and subtracting digits, and storing them unchanged in tiny so-called “memories”. Such terms no longer serve our purposes, because that tradition cannot describe how more complex machines could think like us. Accordingly, computer scientists have had to introduce a thousand new words for such ideas–and we’ve still barely started along that path.
As to the question of scale itself, those objections are out-of-date. They made sense in 1950, before any computer could store even a mere million bits. They still made some sense in the 1960s, when a million bits cost a million dollars. But, today, that same amount of memory costs but a hundred dollars (and our governments have even made the dollars smaller, too)—and there already exist computers with billions of bits. As I write this in the mid-1980s, some of my friends are building a computer that will be composed of more than a million smaller computers. When we finally discover how to make intelligent programs, the task of building the machines for them to inhabit will very likely be an already solved problem.
The only thing missing is most of the knowledge we’ll need to make such machines intelligent. Indeed, as you might guess from all this, the focus of my own research in Artificial Intelligence is to find better ways to relate structures to functions through the use of symbols. When, if ever, will that get done? Never say “Never”.
A Hugo and Nebula Award finalist for True Names, he is also the author of The Peace War, Grimm’s World, and a number of short stories. A mathematician and computer scientist, he has published articles in magazines such as Omni. He teaches at San Diego State University.
His illustrations have graced the pages of SF magazines such as Analog and Isaac Asimov’s SF Magazine. He has also done a great deal of scientific illustration for college texts, as well as general advertising illustration. He lives in Philadelphia, Pennsylvania.
Considered by many to be the father of Artificial Intelligence, he has written especially for this book an essay on the nature of intelligence, natural and artificial. He is the director of the Artificial Intelligence laboratory at the Massachusetts Institute of Technology.