- Series: MIT Press
- Paperback: 299 pages
- Publisher: A Bradford Book; Reprint edition (January 6, 1989)
- Language: English
- ISBN-10: 0262580950
- ISBN-13: 978-0262580953
- Product Dimensions: 5.9 x 0.6 x 9 inches
- Shipping Weight: 14.4 ounces (View shipping rates and policies)
- Average Customer Review: 4.2 out of 5 stars See all reviews (5 customer reviews)
- Amazon Best Sellers Rank: #901,339 in Books (See Top 100 in Books)
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Artificial Intelligence: The Very Idea Reprint Edition
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From Library Journal
This interesting book stands out from the many other new titles on the topic of artificial intelligence. Its philosophical treatment, which includes coverage of automatic formal systems, semantics, and the development of the various theories of thinking, presents an approach taken by few other AI works. The author ties together this philosophical treatment with clear explanations of how computer-based AI efforts operate and what this might hold for us in terms of future potential. He raises some thought-provoking questions and freely admits that the answers are not as clear-cut as some computer experts would have us think. This book should appeal to a wide audience. Hilary D. Burton, Livermore National Labs., Livermore, Cal.
Copyright 1986 Reed Business Information, Inc. --This text refers to an out of print or unavailable edition of this title.
A delightfully well written book highlighting many of the deep and important issues forming the foundation of artificial intelligence and, in fact, of all cognitive science.(Contemporary Psychology)
[An] amusing and wide ranging study... As well as containing concise and sharp definitions of fundamental issues in the philosophy of logic and meaning, the book has excellent summaries of basic computer architectures and hot topics in AI research.(Times Literary Supplement)
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Top customer reviews
John Haugeland's book has a number of virtues that make it a good introduction to classical AI. First, its accessibility. I have a strong background in philosophy, but I have almost no background in philosophy of mind, computer science, or cognitive science. Even without that background I found the ideas presented in this book to be both lucid and powerful. The chapter that examines computer architecture was the most challenging but Haugeland still manages to present the ideas clearly.
Second, Haugeland is honest about the serious difficulties that classical AI runs into when trying to produce realistic models of human thought. However, as I realized very quickly reading this book, even the failures of classical AI are illuminating. We learn a great deal about human thought by examining the ways in which computers fail to realistically mimic our thought. By seeing what computers are (at the present moment) incapable of doing we get some insight into what we are capable of doing. I think human thought progresses by following paths to their natural conclusion and, even when we reach dead ends, we have learned something about why that path was a dead end. So, I think it is great that there are people taking the basic hypthothesis that human thought is rational symbol manipulation as far as it will go. Even if the version of AI presented in this book turns out to be a dead end we will know more about why it is a dead end if we pursue it to the end. If we simply dismiss it on a priori grounds we gain nothing.
Third, Haugeland himself is not dogmatic one way or the other, which is extremely refreshing. He clearly finds the prospect of AI exciting but he does not consider it inevitable. He sees the problems and challenges clearly and he does not hide them from the reader or adopt a defensive posture. It is easy to dismiss the idea of AI, but the easy dismissal of AI is generally based on our intuitive understanding of how our thought functions, and our intuitions are often wrong. One of the great things about AI research is that it often presents us with really startling, counter-intuitive hypotheses about how our minds function. And finally, Haugeland never pretends we know more than we do and he leaves the reader with lots of unsolved problems begging for thought and future research (which is a good thing).
All in all, if you are looking for a good, accessible, exciting introduction to classical AI, Haugeland's book is a great place to start.
Buy this book if you want to understand the underlying philosophical motivations for the widespread view that thoughts are inner representations. Buy Haugeland's collection of essays, entitled "Having Thought", if you want to engage with Haugeland's own alternative view of the mind.
So Haugeland's story is that of a particular theory of mind that held predominance for several decades (what the author himself dubs "good, old-fashioned artificial intelligence" or "GOFAI", p. 112) but is now gradually being superceded. His introduction to this story concludes with a description of the Turing test and a justification for its use, and a brief statement of the efficacy of describing a system in different-even contradictory-ways through different "organizational levels". (p. 9) Of all the ideas presented in the book, this last one has the greatest promise for applicability beyond GOFAI.
Chapter 1, "The Saga of the Modern Mind", is a condensed bit of intellectual history. Haugeland introduces the philosophical children of the Copernican revolution-Hobbes, Descartes, and Hume-and the ways they grappled with understanding the world of the mental with the ideas that had proven so effective in the physical sciences. We soon encounter the "paradox of mechanical reason": if reason is the meaningful manipulation of symbols, and meanings are not physical entities, then how can machines manipulate them? (p. 39)
Chapter 2 serves as an extended definition of "Automatic Formal Systems", that is, computers. This material is the most challenging in the text, but the important concepts (formal games, digital systems, medium independence, etc.), are well-described, except for finite playability. The students I tutored through this work found it impossible to determine just what point was being made, and so did I.
How does one assign meanings-connections to the "real", outside world-to the symbols that a computer manipulates? This question is taken up in Chapter 3, "Semantics"-and answered, it seems, by sleight-of-hand. Haugeland gives to this the name "the formalist's motto": "if you take care of the syntax, the semantics will take care of itself". (p. 106) Neither I nor my students found this simple resolution at all satisfying. In every example of a formal game that the author presents, whatever semantic interpretation it has is provided from outside the system.
Chapter 4, "Computer Architecture", charts the milestones of computing. It begins with the analytical engine, and lauds Babbage's single-handed invention of programming without noting, however, that a human mind does not resemble the tabula rasa of a computer's memory bank. Moving quickly to the twentieth century, we get insightful descriptions of Turing machines, von Neumann machines (which turn out to be the kind of computer we are accustomed to), the mind-bending tree-structured LISP machines, and Newell's pragmatic production machines.
Chapter 5, "Real Machines", might be better titled "Real Problems". Haugeland presents some of the brick walls that AI research has run into. These can be grouped into the phenomenon of the combinatorial explosion: in order to interact with the real world in a manner that demonstrates "common sense", an AI must have access to an impossibly large store of information (while accessing what it needs in due time), and be able to consider an equally impossibly large set of potential courses of action. (p. 178) Methods to restrict what the AI has to consider, such as the focus on "micro-worlds", result in a system with no sense. Haugeland acknowledges these problems, and offers nothing but hope in scientific and technological progress to answer them.
Chapter 6, "Real People", develops means by which the sense that humans exhibit, and machines are far from realizing. Dennett's intentional stances and Grice's conversational implicatures are intelligent-if partial-characterizations of perspicuous reasoning. They are, however, frustratingly slippery for computer programmers, so it's not surprising that Haugeland, with some exasperation, groups them together under the "nonasininity canon": "An enduring system makes sense to the extent that, as understood, it isn't making [a rear] of itself." (p. 219) I feel that, if a reader has followed the author this far, then he or she deserves better than this.
Yet Haugeland and his colleagues are bound to feel frustration. Computers are electromechanical in nature, while humans are neurochemical. Computers can engage in numerical calculation with speed and precision, while most people find mathematics to be their most difficult school subject. Computers are tools that we devised to assist us. Human behavior was forged in the four-billion cauldron of evolution, and psychologists have barely begun to sort out the seething stew of vestigial loves, hates, and motivations that shape our behavior. And honest cognitive science will admit that humans and supercomputers are each masters of two separate, very different worlds. At the end, Haugeland finally admits this possibility-without contemplating the alternatives to the computation theory of might that this possibility demands.