THE AGE of INTELLIGENT MACHINES | Thoughts About Artificial Intelligence

February 21, 2001
author |
Marvin Minsky

One of the visionaries in the field of AI shares his thoughts on AI, from the beginning of the last decade. From Ray Kurzweil’s revolutionary book The Age of Intelligent Machines, published in 1990.

What is Intelligence?

What is intelligence, anyway? It is only a word that people use to name those unknown processes with which our brains solve problems we call hard. But whenever you learn a skill yourself, you’re less impressed or mystified when other people do the same. This is why the meaning of “intelligence” seems so elusive: it describes not some definite thing but only the momentary horizon of our ignorance about how minds might work. It is hard for scientists who try to understand intelligence to explain precisely what they do, since our working definitions change from year to year. But it is not at all unusual for sciences to aim at moving targets. Biology explores the moving frontier of what we understand of what happens inside our bodies. Only a few decades ago the ability of organisms to reproduce seemed to be a deep and complex mystery. Yet as soon as they understood the elements of how our gene strings replicate themselves, biologists wondered why it took so long to think of such a simple thing. In the same way each era of psychology explores what we don’t then know about processes in our brains.

Then, can we someday build intelligent machines? I take the answer to be yes in principle, because our brains themselves are machines. To be sure, we still know very little about how brains actually work. There is no reason for scientists to be ashamed of this, considering that it was only a century ago that we began to suspect that brains were made of separate nerve cells that acted somewhat like computer parts and that it is only half a century since we began developing technical ideas for understanding what such systems could do. These ideas are still barely adequate for dealing with present-day serial computers, which have only thousands of active components, and are not yet robust enough to deal with systems like those in the brain, which involve trillions of interconnected parts, all working simultaneously.

Nor do we yet know how to make machines do many of the things that ordinary people do. Some critics maintain that machines will never be able to do same of those things, and some skeptics even claim to have proved such things. None of those purported proofs actually hold up to close examination, because we are still in the dark ages of scientific knowledge about such matters. In any case, we have not the slightest grounds for believing that human brains are not machines. Because of this, both psychology and artificial intelligence have similar goals: both seek to learn how machines could do many things we can’t yet make them do.

Why are so many people annoyed at the thought that human brains are nothing more than “mere machines”? It seems to me that we have a problem with the word “machine” because we’ve grown up to believe that machines can behave only in lifeless, mechanical ways. This view is obsolete, because the ways we use the word “machine” are out of date. For centuries words like “machine” and “mechanical” were used for describing relatively simple devices like pulleys, levers, locomotives, and typewriters. The word “computer” too inherits from the past that sense of pettiness that comes from doing dull arithmetic by many small and boring steps. Because of this, our previous experience can sometimes be a handicap. Our preconceptions of what machines can do date from what happened when we assembled systems from only a few hundreds or thousands of parts. And that did not prepare us to think about brainlike assemblies of billions of parts. Although we are already building machines with many millions of parts, we continue to think as though nothing has changed. We must learn to change how we think about phenomena that work on those larger scales.

What Is Artificial Intelligence?

Even though we don’t yet understand how brains perform many mental skills, we can still work toward making machines that do the same or similar things. “Artificial intelligence” is simply the name we give to that research. But as I already pointed out, this means that the focus of that research will keep changing, since as soon as we think we understand one mystery, we have to move on to the next. In fact, AI research has made enormous progress in only a few decades, and because of that rapidity, the field has acquired a somewhat shady reputation! This paradox resulted from the fact that whenever an AI research project made a useful new discovery, that product usually quickly spun off to form a new scientific or commercial specialty with its own distinctive name. These changes in name led outsiders to ask, Why do we see so little progress in the central field of artificial intelligence? Here are a few specialties that originated at least in part from AI research but later split into separate fields and, in some instances, commercial enterprises: robotics, pattern recognition, expert systems, automatic theorem proving, cognitive psychology, word processing, machine vision, knowledge engineering, symbolic applied mathematics, and computational linguistics.

For example, many researchers in the 1950s worked toward discovering ways to make machines recognize various sorts of patterns. As their findings were applied to problems involved with vision, speech, and several other areas, those fields evolved their own more distinct techniques, they organized their own technical societies and journals, and they stopped using the term “artificial intelligence.” Similarly, an early concern of AI was to develop techniques for enabling computers to understand human language; this spawned a field called computational linguistics. Again, many ideas from artificial intelligence had a large influence among psychologists, who applied those ideas to their studies of the mind but used the title “cognitive psychology.”

I can illustrate how AI projects develop by recounting the research of James Slagle, who, as a graduate student at MIT in 1960, developed a program to solve calculus problems; he named it with the initials of “symbolic automatic integration.” Although there were many problems that SAINT couldn’t solve, it surpassed the performance of average MIT students. When he first approached this subject, most scientists considered solving those problems to require substantial intelligence. But after Slagle’s work we had to ask ourselves instead why students take so long to learn to do such basically straightforward things.

How did SAINT solve those problems? It employed about 100 formulas from the domains of algebra and calculus and applied to these about a dozen pattern-matching methods for deciding which formula might be most likely to help solve a given problem. Since any particular attempt might fail, the program had to employ a good deal of trial and error. If one method did not work, the program automatically went on to try another. Sometimes one of them would work, but frequently a problem was too hard for any single such method to work. The system was programmed in that case to proceed on to certain other methods, methods that attempted to split each hard problem into several simpler ones. In this way, if no particular method worked, SAINT was equipped with a great variety of alternatives.

Now we can make an important point. For years the public has been told, Computers do only what they’re programmed to do. But now you can see why that’s not quite true: We can write programs that cause the machine to search for solutions. Often such searches produce results that greatly surprise their programmers.

The idea of making programs search greatly expanded their powers. But it also led to new kinds of problems: search processes could generate so many possible alternatives that the programs were in constant danger of getting lost, repeating themselves, or persisting at fruitless attempts that had already consumed large amounts of time. Much research in the 1960s was focused on finding methods to reduce that sort of fruitless search. Slagle himself experimented with some mathematical theories of how to take into account both how much effort had been spent on each solution attempt and how much apparent progress had been made. Thus the SAINT program worked as well as it did, not merely because of its specialized knowledge about calculus, but also because of other knowledge about the search itself. To prevent the search from simply floundering around, making one random attempt after another, some of the program’s knowledge was applied to recognize conditions in which its other, more specialized knowledge might be particularly useful.

When SAINT first appeared, it was acclaimed an outstanding example of work in the field of artificial intelligence. Later other workers analyzed more carefully its virtues and deficiencies, and this research improved our understanding of the basic nature of those calculus problems. Eventually ways were found to replace all the trial and error processes in SAINT by methods that worked without any search. The resulting commercial product, a program called MACSYMA, actually surpassed the abilities of professional mathematicians in this area. But once the subject was so well understood, we ceased to think of it as needing intelligence. This area is now generally seen as belonging no longer to artificial intelligence but to a separate specialty called symbolic applied mathematics.

Robotics and Common Sense

In the 1960s we first began to equip computers with mechanical hands and television eyes. Our goal was to endow machines with the sorts of abilities children use when playing with toys and building blocks. We found this much harder to do than expected. Indeed, a scholar of the history of artificial intelligence might get a sense of watching evolution in reverse. Even in its earliest years we saw computers playing chess and doing calculus, but it took another decade for us to begin to learn to make machines that could begin to act like children playing with building blocks! What makes it easier to design programs that imitate experts than to make them simulate novices? The amazing answer is, Experts are simpler than novices! To see why it was harder to make programs play with toys than pass calculus exams, let’s consider what’s involved in enabling a robot to copy simple structures composed of blocks: we had to provide our robot with hundreds of small programs organized into a system that engaged many different domains of knowledge. Here are a few of the sorts of problems this system had to deal with:

  • The relation between the hand and the eye
  • Recognizing objects from their visual appearances
  • Recognizing objects partially hidden from view
  • Recognizing relations between different objects
  • Fitting together three-dimensional shapes
  • Understanding how objects can support one another to form stable structures
  • Planning a sequence of actions to assemble a structure
  • Moving in space so as to avoid collisions
  • Controlling the fingers of a hand for grasping an object

It is very hard for any adult to remember or appreciate how complex are the properties of ordinary physical things. Once when an early version of our block-building program was asked to find a new place to put a block, it tried to place it on top of itself! The program could not anticipate how that action would change the situation. To catalog only enough fragments of knowledge to enable a robot to build a simple blocklike house from an unspecified variety of available materials would be an encyclopedic task. College students usually learn calculus in half a year, but it takes ten times longer for children to master their building toys. We all forget how hard it was to learn such things when we were young.

Expertise and Common Sense

Many computer programs already exist that do things most people would regard as requiring intelligence. But none of those programs can work outside of some very small domain or specialty. We have separate programs for playing chess, designing transformers, proving geometry theorems, and diagnosing kidney diseases. But none of those programs can do any of the things the others do. By itself each lacks the liveliness and versatility that any normal person has. And no one yet knows how to put many such programs together so that they can usefully communicate with one another. In my book, The Society of Mind, I outline some ideas on how that might be done inside our brains.

Putting together different ideas is just what children learn to do: we usually call this common sense. Few youngsters can design transformers or diagnose renal ailments, but whenever those children speak or play, they combine a thousand different skills. Why is it so much easier for AI programmers to simulate adult, expert skills than to make programs perform childlike sorts of commonsense thought? I suspect that part of the answer lies in the amounts of variety. We can often simulate much of what a specializt does by assembling a collection of special methods, all of which share the same common character. Then so long as we remain within some small and tidy problem world, that specializt’s domain of expertise, we need merely apply different combinations of basically similar rules. This high degree of uniformity makes it easy to design a higher-level supervisory program to decide which method to apply. However, although the “methods” of everyday thinking may, by themselves, seem simpler than those of experts, our collections of commonsense methods deal with a great many more different types of problems and situations. Consider how many different things each normal child must learn about the simplest-seeming physical objects, such as the peculiarities of blocks that are heavy, big, smooth, dangerous, pretty, delicate, or belong to someone else. Then consider that the child must learn quite different kinds of strategies for handling solids and liquids; strings, tapes, and cloths; jellies and muds as well as things he is told are prohibited, poisonous, or likely to cut or bite.

What are the consequences of the fact that the domain of commonsense thinking is so immensely varied and disorderly? One problem is simply accumulating so much knowledge. But AI research also encountered a second, more subtle problem. We had to face the simple fact that in order for a machine to behave as though it “knows” anything, there must exist, inside that machine, some sort of structure to embody or “represent” that knowledge. Now, a specialized, or “expert,” system can usually get by with very few types of what we call knowledge representations. But in order to span that larger universe of situations we meet in ordinary life, we appear to need a much larger variety of types of representations. This leads to a second, harder type of problem: knowledge represented in different ways must be applied in different ways. This imposes on each child obligations of a higher type: they have to learn which types of knowledge to apply to which kinds of situations and how to apply them. In other words, we have to accumulate not merely knowledge, but also a good deal of knowledge about knowledge. Now, experts too have to do that, but because commonsense knowledge is of more varied types, an ordinary person has to learn (albeit quite unconsciously) much more knowledge about representations of knowledge, that is, which types of representation skills to use for different purposes and how to use them.

If this sounds very complicated, it is because it actually is. Until the last half century we had only simple theories of mind, and these explained only a little of what animals could do in the impoverished worlds of laboratory experiments. Not until the 1930s did psychologists like Jean Piaget discover how many aspects of a child’s mind develop through complicated processes, sometimes composed of intricate sequences of stagelike periods. We still don’t know very much about such matters, except that the mind is much more complex than imagined in older philosophies. In The Society of Mind, I portray it as a sort of tangled-up bureaucracy, composed of many different experts, or as I call them, “agencies,” that each develop different ways to represent what they learn. But how can experts using different languages communicate with one another? The solution proposed in my book is simply that they never come to do it very well! And that explains why human consciousness seems so mysterious. Each part of the mind receives only hints of what the other parts are about, and no matter how hard a mind may try, it can never make very much sense of itself.

Supercomputers and Nanotechnology

Many problems we regard as needing cleverness can sometimes be solved by resorting to exhaustive searches, that is, by using massive, raw computer power. This is what happens in most of those inexpensive pocket chess computers. These little machines use programs much like the ones that we developed in the 1960s, using what were then some of the largest research computers in the world. Those old programs worked by examining the consequences of tens of thousands of possible moves before choosing one to actually make. But in those days the programs took so long to make those moves that the concepts they used were discarded as inadequate. Today, however, we can run the same programs on faster computers so that they can consider millions of possible moves, and now they play much better chess. However, that shouldn’t fool us into thinking that we now understand the basic problem any better. There is good reason to believe that outstanding human chess players actually examine merely dozens, rather than millions, of possible moves, subjecting each to more thoughtful analysis.

In any case, as computers improved in speed and memory size, quite a few programming methods became practical, ones that had actually been discarded in the earlier years of AI research. An Apple desktop computer (or an Amiga, Atari, IBM, or whatever) can do more than could a typical million-dollar machine of a decade earlier, yet private citizens can afford to play games with them. In 1960 a million-bit memory cost a million dollars; today a memory of the same size (and working a hundred times faster) can be purchased for the price of a good dinner. Some seers predict another hundredfold decrease in size and cast, perhaps in less than a decade, when we learn how to make each microcircuit ten times smaller in linear size and thus a hundred times smaller in area. What will happen after that? No one knows, but we can be sure of one thing: those two-dimensional chips we use today make very inefficient use of space. Once we start to build three-dimensional microstructures, we might gain another millionfald in density. To be sure, that would involve serious new problems with power, insulation, and heat. For a futuristic but sensible discussion of such possibilities, I recommend Eric Drexler’s Engines of Creation (Falcon Press, 1986).

Not only have small components become cheaper; they have also become faster. In 1960 a typical component required a microsecond to function; today our circuits operate a thousand times faster. Few optimists, however, predict another thousandfold increase in speed over the next generation. Does this mean that even with decreasing costs we will soon encounter limits on what we can make computers do? The answer is no, because we are just beginning a new era of parallel computers.

Most computers today are still serial; that is, they do only one thing at a time. Typically, a serial computer has millions of memory elements, but only a few of them operate at any moment, while the rest of them wait for their turn: in each cycle of operation, a serial computer can retrieve and use only one of the items in its memory banks. Wouldn’t it be better to keep more of the hardware in actual operation? A more active type of computer architecture was proposed in Daniel Hillis’s Connection Machine (MIT Press, 1986), which describes a way to assemble a large machine from a large number of very small, serial computers that operate concurrently and pass messages among themselves. Only a few years after being conceived, Connection Machines are already commercially available, and they indeed appear to have fulfilled their promise to break through some of the speed limitations of serial computers. In certain respects they are now the fastest computers in the world.

This is not to say that parallel computers do not have their own limitations. For, just as one cannot start building a house before the boards and bricks have arrived, you cannot always start work simultaneously on all aspects of solving a problem. T would certainly be nice if we could take any program for a serial computer, divide it into a million parts, and then get the answer a million times faster by running those parts simultaneously on that many computers in parallel. But that can’t be done, in general, particularly when certain parts of the solution depend upon the solutions to other parts. Nevertheless, this quite often turns out to be feasible in actual practice. And although this is only a guess, I suspect that it will happen surprisingly often for the purposes of artificial intelligence. Why do I think so? Simply because it seems very clear that our brains themselves must work that way.

Consider that brain cells work at very modest speeds in comparison to the speeds of computer parts. They work at rates of less than a thousand operations per second, a million times slower than what happens inside a modern computer circuit chip. Could any computer with such slow parts do all the things that a person can do? The answer must lie in parallel computation: different parts of the brain must do many more different things at the same time. True, that would take at least a billion nerve cells working in parallel, but the brain has many times that number of cells.

AI and the World of the Future

Intelligent machines may be within the technological reach of the next century. Over the next few generations we’ll have to face the problems they pose. Unless some unforeseen obstacles appear, our mind-engineering skills could grow to the point of enabling us to construct accomplished artificial scientists, artists, composers, and personal companions. Is AI merely another advance in technology, or is it a turning point in human evolution that should be a focus of discussion and planning by all mankind? The prospect of intelligent machines is one that we’re ill prepared to think about, because it raises such unusual moral, social, artistic, philosophical, and religious issues. Are we obliged to treat artificial intelligences as sentient beings? Should they have rights? And what should we do when there remains no real need for honest work, when artificial workers can do everything from mining, fanning, medicine, and manufacturing all the way to house cleaning? Must our lives then drift into pointless restlessness and all our social schemes disintegrate?

These questions have been discussed most thoughtfully in the literary works of such writers as Isaac Asimov, Gregory Benford, Arthur C. Clarke, Frederick Pohl, and Jack Williamson, who all tried to imagine how such presences might change the aspirations of humanity. Some optimistic futurists maintain that once we’ve satisfied all our worldly needs, we might then turn to the worlds of the mind. But consider how that enterprise itself would be affected by the presence of those artificial mindlike entities. That same AI technology would offer ways to modify the hardware of our brains and thus to endlessly extend the mental worlds we could explore.

You might ask why this essay mixes both computers and psychology. The reason is that though we’d like to talk about making intelligent machines, people are the only such intelligence we can imitate or study now. One trouble, though, is that we still don’t know enough about how people work! Does this mean that we can’t develop smart machines before we get some better theories of psychology? Not necessarily. There certainly could be ways to make very smart machines based on principles that our brains do not use, as in the case of those very fast, dumb chess machines. But since we’re the first very smart machines to have evolved, we just might represent one of the simplest ways!

But, you might object, there’s more to a human mind than merely intellect. What about emotion, intuition, courage, inspiration, creativity, and so forth. Surely it would be easier simply to understand intelligence than to try to analyze all those other aspects of our personalities! Not so, I maintain, because traditional distinctions like those between logic and intuition, between intellect and emotion, unwisely try to separate knowledge and meaning from purpose and intention. In The Society of Mind, I argue that little can be done without combining elements of both. Furthermore, when we put them together, it becomes easier, rather than harder, to understand such matters, because, though there are many kinds of questions, the answers to each of them illuminate the rest.

Photo by Lou Jones

Marvin Minsky.


Photo by Lou Jones
Marvin Minsky