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Title: What Is Thought? by Eric B. Baum ISBN: 0-262-02548-5 Publisher: MIT Press Pub. Date: 01 January, 2004 Format: Hardcover Volumes: 1 List Price(USD): $40.00 |
Average Customer Rating: 4.22 (9 reviews)
Rating: 3
Summary: What Is What Is Thought?
Comment: Those who are not yet convinced that the brain is a computing mechanism, or who believe that mysticism is required to explain thought, will find quite a bit of value in this book. The book surveys numerous areas of Computer Science, AI, and even a bit of biology, in an attempt to build a case for the brain as a computing mechanism. The book also wades into evolution to try to explain how it came to be so. The scope of the book is ambitious.
Anyone with a background in AI or Cognitive Science will likely find "What is Thought" disappointing as it has little new to say. I fall into this category, and I find a number of aspects of this book unsatisfying.
This is a long book in which there is a short book struggling to get out. The author's main thesis, that the brain is a modular computing mechanism that is the result of evolution, is repeated numerous times at considerable length to the point of tedium. While the author shows his thesis to be consistent with numerous observations, it is never developed to any greater depth. In fact, one of the author's conclusions is that we may never understand the inner workings of the brains "subroutines" because, as a result of evolution, they are now so "compressed".
The author rarely defines his terms. Merely replacing the words "compressed" and "compact" by the word "concise" would enhance the clarity of this book considerably. The author also seems to be of the opinion that generalization, which is the result of "compressed" representations, is the essence of understanding. This view is inadequate for explaining our abilities to plan our own actions and predict the actions of other agents, for example.
Because of the informal, breezy style, the book comes across as an introduction for novices or a position paper rather than a scholarly work. While some may enjoy this style, I find it lacks a certain satisfying clarity and crispness needed for a convincing presentation of such an abstract topic.
Rating: 5
Summary: Is Evolution The Secret To Intelligence?
Comment: Why can humans rapidly carry out tasks, such as learning to talk or recognizing an object, that seem intractable for computers?
According to Eric Baum, the human brain is much like a computer, but it runs programs that are different from the ones usually
written by human computer programmers. The programs run by the brain are insightful or ``compressed''; they have built in
a good deal of knowledge or ``understanding'' about the nature of the world. Human programmers have difficulty generating such efficient or compressed programs (except for limited special purposes), because to do so requires vast computing resources, far beyond what one can accomplish with pencil and paper or even with presently available computer assistance.
The key to understanding intelligence, according to Baum, is the theory of evolution; in the process that brought humans into being, evolution cycled through many billions of generations of organisms, in the course of which, in effect, vast computational resources were brought to bear on the problem of generating useful algorithms. The real secret to thought is thus stored in our DNA, which preprograms us with algorithms that
are more efficient and powerful than the ones usually available to computer scientists.
With this starting point, Baum proposes answers to many old riddles. Our sense of ``self'' reflects our origin in an evolutionary struggle for survival toward which all components of our biology are directed. ``Free will'' is a useful approximation because of the great complexity of our brains (and our limited knowledge about them) and the concommitant difficulty of predicting a person's behavior. Baum illustrates his arguments with numerous examples drawn from biology, psychology, and computer science; the material is generally quite interesting, though at times perhaps too detailed for a casual reader. His arguments are surprisingly persuasive, and, while certainly no expert, I suspect that Baum is closer to the mark than most of the old and new classic writers on these problems.
Rating: 5
Summary: Review of "What is Thought"
Comment: Eric Baum's recent book "What is Thought" is a must-read for anyone interested in artificial intelligence or cognitive science and neuroscience. In the highly saturated area of "consciousness books" this one stands out as one likely to be remembered and referenced much longer than the others. One reason for this is the absolute clarity with which he argues the hard AI position, that the mind is the result of the computer program that is not just run by the brain, but a result of the brain's very architecture produced by several billion years of evolution, the original and ultimate genetic program. The major thesis of the book is that "meaning" should be considered to be identical to a compact description of the data, such as the sensory input from the external world. One example he gives is the compact description of a set of data as falling on a line. This is, of course, a completely operational definition of semantics, but I think a useful one. This leads to the conclusion that meaning is intrinsically determined by the interaction of the world with the architecture of our 100 billion neuron brain as produced by the action of a mere 30,000 genes in generating its architecture. He does not ignore learning and culture, of course, but the point is that, at least at this point in our evolution, most of the compaction is already in the structure. Baum's credentials for many of these speculations come from his solutions to several classical AI problems, such as "Blocks World" using genetic programming techniques. The most successful of these are embodied in an artificial economy model call "Hayek" that solves the credit assignment problem well enough to have advanced solutions to such complex problems considerably. The description of the Hayek system is worth reading in its own right for those interested in various AI approaches to these classical problems, although I found these sections somewhat sparse in details for trying to implement the code. What Baum is very clear about is the formidable challenge of producing, in any current computer system, an equivalent compact description of data similar to that for which humans have evolved. Thus, from first principles, we cannot expect any current AI system to display anything like the ability to generate common sense meaning from the world that has been produced by the great genetic program that is the evolution of the human brain on earth, because the number of equivalent learning cycles (on the order of 4 billion years times the number of example animals) is so many orders of magnitude greater for biological brains than artificial ones. But there is hope in the future from Moore's law of the continued increase in computer power. If you accept these arguments about the vast computational power embodied in our brain's structure, then our inability to comprehend issues such as "qualia" and the feeling of having free will are to be attributed to simple ignorance, a quantitative difference, rather than to more mystical qualitative boundaries. This is consonant with arguments previously eloquently made by the philosopher Dennett, among others. Whether you are for or against such a hard AI position, this book makes its case more honestly, eloquently, and in more detail than any other I have read. Besides the lack of detail for implementation in the discussion of the Hayek system for solving classic AI problems such as Blocks World, one other complaint I have is the lack of reference to some previous work. For example, although Baum does not borrow in any direct way from the CopyCat work of Hofstadter and Mitchell, in spirit, at least, the set of autonomous agents in Hayek sound a lot like codelets and other elements in the CopyCat system, and I don't see why Baum could not have referenced that. I also believe that the reduction of data to a compact description as being equivalent to meaning is slightly incomplete. I think such a compact description is equivalent to an instinct, or an intuition. The embodiment of the compact description that can be manipulated within a system of such descriptions is what actually generates meaning, and the equivalent of thought.
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Title: The Birth of the Mind: How a Tiny Number of Genes Creates the Complexities of Human Thought by Gary F. Marcus ISBN: 0465044050 Publisher: Basic Books Pub. Date: 16 December, 2003 List Price(USD): $26.00 |
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Title: The Quest for Consciousness: A Neurobiological Approach by Christof Koch ISBN: 0974707708 Publisher: Roberts and Co. Pub. Date: March, 2004 List Price(USD): $45.00 |
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Title: Consciousness: An Introduction by Susan J. Blackmore ISBN: 019515343X Publisher: Oxford University Press Pub. Date: October, 2003 List Price(USD): $39.95 |
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Title: The Algebraic Mind : Integrating Connectionism and Cognitive Science by Gary F. Marcus ISBN: 0262632683 Publisher: MIT Press Pub. Date: 01 March, 2003 List Price(USD): $17.95 |
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Title: The Fabric of the Cosmos: Space, Time, and the Texture of Reality by Brian Greene ISBN: 0375412883 Publisher: Knopf Pub. Date: 10 February, 2004 List Price(USD): $28.95 |
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