[Dreyfus 2002, Hubert L. Dreyfus & Stuart E. Dreyfus ,From Socrates to Expert Systems: The Limits and Dangers of Calculative Rationality, Regents of the University of California,2002 ]

It has been half a century since the computer burst upon the world along with promises that it would soon be programmed to be intelligent, and the related promise or threat that we would soon learn to understand ourselves as computers. In 1947 Alan Turing predicted that there would be intelligent computers by the end of the century. Now with the millennium only three years away, it is time for a retrospective evaluation of the attempt to program computers to be intelligent like HAL in the movie 2001.

Actual AI research began auspiciously around 1955 with Allen Newell and Herbert Simon's work at the RAND Corporation. Newell and Simon proved that computers could do more than calculate. They demonstrated that computers were physical symbol systems whose symbols could be made to stand for anything, including features of the real world, and whose programs could be used as rules for relating these features. In this way computers could be used to simulate certain important aspects intelligence. Thus the information-processing model of the mind was born. But, looking back over these fifty years, it seems that theoretical AI with its promise of a robot like HAL appears to be a perfect example of what Imre Lakatos has called a "degenerating research program".

A degenerating research program is one that starts out with a successful approach to a new domain, but which then runs into unexpected problems it cannot solve, and is finally abandoned by its practitioners. Newell and Simon's early work on problem solving was, indeed, impressive, and by 1965 Artificial Intelligence had turned into a flourishing research program, thanks to a series of micro-world successes such as Terry Winograd's SHRDLU, a program that could respond to English-like commands by moving simulated, idealized blocks. The field had its own Ph.D. programs, professional societies and gurus. It looked like all one had to do was extend, combine, and render more realistic the micro-worlds and one would have genuine artificial intelligence. Marvin Minsky, head of the M.I.T. AI Laboratory, predicted in 1967 that "within a generation the problem of creating `artificial intelligence' will be substantially solved." Then, rather suddenly, the field ran into unexpected difficulties. The trouble started, as far as we can tell, around l970 with the failure of attempts to program children's story understanding. The programs lacked the intuitive common sense of a four-year old. And no one knew what to do about it. [Insert birthday party, frames, and the commonsense knowledge problem.] [An old philosophical dream was at the heart of the problem. AI is based on an idea which has been around in philosophy since Descartes, that all understanding consists in forming and using appropriate symbolic representations. For Descartes these were complex descriptions built up out of primitive ideas or elements. Kant added the important idea that all concepts were rules. Frege showed that rules could be formalized so that they could be manipulated without intuition or interpretation. Given the nature of computers, AI took up the search for such formal rules and representations. Common-sense-intuition had to be understood as some vast collection of rules and facts.]

It simply turned out to be much harder than one expected to formulate, let alone formalize, the required theory of common sense. It was not, as Minsky had hoped, just a question of cataloguing a few hundred thousand facts. The common sense knowledge problem became the center of concern. Minsky's mood changed completely in the course of fifteen years. In 1982 he told a reporter: "The AI problem is one of the hardest science has ever undertaken." [CYC. Can you whistle and eat at the same time?]

Given this impasse, it made sense a year later for researchers to return to microworlds - domains isolated from everyday common-sense intuition - and try to develop theories of at least such isolated domains. This is actually what happened - with the added realization that such isolated domains need not be games like chess nor micro-worlds like Winograd's blocks world, but could, instead, be skill domains like disease diagnosis or spectrograph analysis.

Thus, from the frustrating field of AI has recently emerged a new field called knowledge engineering, which by limiting its goals has applied AI research in ways that actually work in the real world. The result is the so called expert system, enthusiastically promoted in Edward Feigenbaum's book The Fifth Generation: Artificial Intelligence and Japan's Computer Challenge to the World. Feigenbaum spells out the goal:

The machines will have reasoning power: they will automatically engineer vast amounts of knowledge to serve whatever purpose humans propose, from medical diagnosis to product design, from management decisions to education.

What the knowledge engineers claim to have discovered is that in areas which are cut off from everyday common sense and social intercourse, all a machine needs in order to behave like an expert is specialized knowledge of two types:

The facts of the domain - the widely shared knowledge ... that is written in textbooks and journals of the field and heuristic knowledge, which is the knowledge of good practice and good judgment in a field.

Using both kinds of knowledge Feigenbaum developed a program called DENDRAL. It takes the data generated by a mass spectrograph and deduces from this data the molecular structure of the compound being analyzed. Another program, MYCIN, takes the results of blood tests such as the number of red cells, white cells, sugar in the blood, etc. and comes up with a diagnosis of which blood disease is responsible for this condition. It even gives an estimate of the reliability of its own diagnosis. In their narrow areas, such programs give impressive performances. They seem to confirm the observation of Leibniz, the grandfather of expert systems. He observed that:

The most important observations and turns of skill in all sorts of trades and professions are as yet unwritten. This fact is proved by experience when, passing from theory to practice, we desire to accomplish something. Of course, we can also write up this practice, since it is at bottom just another theory more complex and particular.

And, indeed, isn't the success of expert systems just what one would expect? If we agree with Feigenbaum that: "almost all the thinking that professionals do is done by reasoning... " we can see that, once computers are used for reasoning and not just computation, they should be as good or better than we are at following rules for deducing conclusions from a host of facts. So we would expect that if the rules which an expert has acquired from years of experience could be extracted and programmed, the resulting program would exhibit expertise. Again Feigenbaum puts the point very clearly:

The matters that set experts apart from beginners, are symbolic, inferential, and rooted in experiential knowledge. ... Experts build up a repertory of working rules of thumb, or "heuristics," that, combined with book knowledge, make them expert practitioners.

So, since each expert already has a repertory of rules in his mind, all the expert system builder need do is get the rules out of the expert and program them into a computer.

This view is not new. In fact, it goes back to the beginning of Western culture when the first philosopher, Socrates, stalked around Athens looking for experts in order to draw out and test their rules. In one of his earliest dialogues, The Euthyphro, Plato tells us of such an encounter between Socrates and Euthyphro, a religious prophet and so an expert on pious behavior. Socrates asks Euthyphro to tell him how to recognize piety: "I want to know what is characteristic of piety ... to use as a standard whereby to judge your actions and those of other men." But instead of revealing his piety-recognizing heuristic, Euthyphro does just what every expert does when cornered by Socrates. He gives him examples from his field of expertise, in this case mythical situations in the past in which men and gods have done things which everyone considers pious. Socrates gets annoyed and demands that Euthyphro, then, tell him his rules for recognizing these cases as examples of piety, but although Euthyphro claims he knows how to tell pious acts from impious ones, he cannot state the rules which generate his judgments.

Socrates ran into the same problem with craftsmen, poets and even statesmen. They also could not articulate the principles underlying their expertise. Socrates therefore concluded that none of these experts knew anything and he didn't know anything either. That might well have been the end of Western philosophy, but Plato admired Socrates and saw his problem. So he developed an account of what caused the difficulty. Experts, at least in areas involving non-empirical knowledge such as morality and mathematics, had, in another life, Plato said, learned the principles involved, but they had forgotten them. The role of the philosopher was to help such moral and mathematical experts recollect the principles on which they acted. Knowledge engineers would now say that the rules experts - even experts in empirical domains - use have been put in a part of their mental computers where they work automatically. Feigenbaum says: When we learned how to tie our shoes, we had to think very hard about the steps involved ... Now that we've tied many shoes over our lifetime, that knowledge is "compiled," to use the computing term for it; it no longer needs our conscious attention.

On this Platonic view, the rules are there functioning in the expert's mind whether he is conscious of them or not. How else could one account for the fact that the expert can still perform the task? After all, we can still tie our shoes, even though we cannot say how we do it. So nothing has changed. Only now 2400 years later, thanks to Feigenbaum and his colleagues, we have a new name for what Socrates and Plato were doing: knowledge acquisition research. But although philosophers and knowledge engineers have become convinced that expertise is based on applying sophisticated heuristics to masses of facts, there are few available rules. As Feigenbaum explains:

An expert's knowledge is often ill-specified or incomplete because the expert himself doesn't always know exactly what it is he knows about his domain.

Indeed, when Feigenbaum suggests to an expert the rules the expert seems to be using, he gets a Euthyphro-like response. "That's true, but if you see enough patients/rocks/chip designs/instruments readings, you see that it isn't true after all," and Feigenbaum comments with Socratic annoyance: "At this point, knowledge threatens to become ten thousand special cases."

There are also other hints of trouble. Ever since the inception of Artificial Intelligence, researchers have been trying to produce artificial experts by programming the computer to follow the rules used by masters in various domains. Yet, although computers are faster and more accurate than people in applying rules, master-level performance has remained out of reach. [Arthur Samuel's work is typical. In 1947, when electronic computers were just being developed, Samuel, then at IBM, decided to write a checker playing program. He elicited heuristic rules from checker masters and programmed a computer to follow these rules. The resulting checkers program is not only the first and one of the best expert systems ever built; it is also a perfect example of the way fact turns into fiction in AI. Feigenbaum, for example, reports that "by 1961 [Samuel's program] played championship checkers, and it learned and improved with each game." In fact, Samuel said in an interview at Stanford University, where he is a retired professor, that the program did once defeat a state champion, but the champion "turned around and defeated the program in six mail games." According to Samuel, after 35 years of effort, "the program is quite capable of beating any amateur player and can give better players a good contest." It is clearly no champion. Samuel is still bringing in expert players for help but he "fears he may be reaching the point of diminishing returns." This does not lead him to question the view that the masters the program cannot beat are using heuristic rules; rather, like Plato and Feigenbaum, Samuel thinks that the experts are poor at recollecting their compiled heuristics. "The experts do not know enough about the mental processes involved in playing the game," he says.

The same story is repeated in every area of expertise, even in areas unlike checkers where expertise requires storing large numbers of facts, which should give an advantage to the computer.] In each area where there are experts with years of experience, the computer can do better than the beginner, and can even exhibit useful competence, but it cannot rival the very experts whose facts and supposed heuristics it is processing with incredible speed and unerring accuracy.

In the face of this impasse, in spite of the authority and influence of Plato and 2400 years of philosophy, we must take a fresh look at what a skill is and what the expert acquires when he achieves expertise. We must be prepared to abandon the traditional view that run from Plato to Piaget and Chomsky that a beginner starts with specific cases and, as he becomes more proficient, abstracts and interiorizes more and more sophisticated rules. It might turn out that skill acquisition moves in just the opposite direction: from abstract rules to particular cases. Since we are all experts in many areas, we have the necessary data.

Many of our skills are acquired at an early age by trial and error or by imitation, but to make the phenomenology of skillful behavior as clear as possible let's look at how, as adults we learned new skills by instruction.