From Socrates to Expert Systems

From Socrates to Expert Systems:

The Limits and Dangers of Calculative Rationality

Hubert L. Dreyfus

and

Stuart E. Dreyfus

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 1970 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.

The Kite Story

Today was Jack's birthday. Penny and Janet went to the store. They were going to get presents. Janet decided to geta kite. "Don't do that," said Penny. "Jack has a kite. He will make you take

it

back."

1.

Note the things about the story that are not explicit but which we know nonetheless. The presents were for Jack. The kite was a present, etc. An intelligent story understander should figure this out. These problems could be partly resolved by storing information in the computer in a data structure called a birthday party frame. The frame incorporates information such as that at birthday parties people give presents to the person being honored; that people generally buy presents, typically at stores, etc.

2.

This helps solve part of the problem, but what about the italicized

it

in the last sentence? Grammatically it should refer back to the last mentioned kite, namely the kite Jack already had. But we know that this kite is not the one that Jack will make Janet take back; it will be the

new

kite that goes back if he already has one. Any four-year-old will get this right. But how is the language-understanding computer going to know?

3.

Perhaps we can begin by adding the information that people do not want to receive more than one thing of the same kind. But what frame does this go into? It doesn't seem to be about birthday parties, household objects, or rules about gifts. Even worse, the rule at issue isn't even true. It is false in the case of dollar bills, marbles, cookies, etc. But even these exceptions have exceptions: for example, someone has too many marbles or a giant cookie; but even these exceptions have exceptions, as in the case of a cookie monster.

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...