What Computers Still Can't Do: A Critique of Artificial Reason [Paperback]

The fact is that cognitivism is hotly contested by serious thinkers in many disciplines, but Shallit's name-calling (and the comparison of cognitivism's serious critics to creation scientists) smacks of an abdication from serious engagement and argument.

Dreyfus's revised edition is a fine piece of work, worthy of serious intellectual discussion and confrontation. His many aarguments against Fodor, Chomsky, Simon and others have great merit. It is unfortunate that some folks simply close their eyes and argue from authority. But appeals to (even 'scientific') authority wear thin when left to stand alone!!

Basically there are two types of mistakes made by Minsky and many others:
1. believing they were getting close to understanding human thought,
2. repeatedly announcing same to the world.

The philosophy of Dreyfus in the first 300 pages is largely concerned with fallacious assumptions made by AI researchers. Finally in the last 50 pages (350 page book) he settles down and gives us some interesting concepts that should be understood if we are to seek AI at the human level. He develops the concept of "nonformal behavior" - which we humans usually learn by generalizing examples and following intuition without use of formal rules. Examples: chess at the gestalt master level, and disambiguation of broken sentences.

Dreyfus acknowledges the possible importance of neural network architectures, but dismisses them as outside the scope of his critique. He touches on the poor idea of AI trying to program a full functioning adult, and further carries out a critique of machine learning ("reinforcement learning").

The most important point he makes is that of nonformal behavior -- the non-logical almost Zen-like process that humans must go through. The irony is that we have to struggle with our nonformal thinking to do simple formal tasks such as long division; whereas the computer must struggle with its built-in hard logic to attempt nonformal tasks such as pattern recognition.

The book is for the most part quite dated, but nevertheless, it is very worthwhile reading for anyone in a serious pursuit of machine intelligence. My criticism of his style is just that. I have only a minor criticism of the intelligent content and his restrictions in scope.

But what a patchwork it has become. Every ten years, the book has a new section and a new introduction bolted on to it. But not just bolted on to the end, or on to the beginning.

No reader is given an easy path through the text: neither the reader who is new to the material (and who wants to read through the text in chronological order), nor the reader who has read the previous incarnation of the book (and who wants to pick up just the new parts).

The message of the book, too, is unfortunately weakened by its subjective stance, and its invocation of the personalities from the debate.

However, the book still remains a refreshingly different contribution to the world of AI research, an important warning, and a greatly worthwhile read.