Rating: 
Summary: Thoughtful
Review: The first half of this book is an overview of the field of artificial intelligence that might be one of the best available introductions for people who are new to the subject, but which seemed fairly slow and only mildly interesting to me.
The parts of the book that are excellent for both amateurs and experts are chapters 11 through 13, dealing with how human intelligence evolved.
He presents strong, although not conclusive, arguments that the evolution of language did not involve dramatic new modes of thought except to the extent that improved communication improved learning, and that small catalysts created by humans might well be enough to spark the evolution of human-like language in other apes.
His recasting of the nature versus nurture debate in terms of biases that guide learning is likely to prove more valuable at resisting the distortions of ideologues than more conventional versions (e.g. Pinker's).
His arguments have important implications for how AI will progress. He convinced me that it will be less sudden than I previously thought, by convincing me that truly general-purpose learning machines won't work, and that much of intelligence involves using large quantities of data about the real world to choose good biases with which to guide our learning.
Rating:
Summary: A philosophical journey
Review: "What is thought" is a philosophical journey through results of decades of research in machine learning, information theory, complexity theory etc. It uses scientific results -- and at times the author's research -- to analyze similarities and differences with the natural learning process called evolution. Today, we are witnessing a powerful shift in all branches of science from trying to find analytical solutions toward self-evolving computer based modeling and simulation. This book is not a tutorial on various scientific topics whose results it uses, rather it tries to convey an understanding of the mind, consciousness, and the scientific environment.
Rating:
Summary: A philosophical journey
Review: "What is thought" is a philosophical journey through results of decades of research in machine learning, information theory, complexity theory etc. It uses scientific results -- and at times the author's research -- to analyze similarities and differences with the natural learning process called evolution. Today, we are witnessing a powerful shift in all branches of science from trying to find analytical solutions toward self-evolving computer based modeling and simulation. This book is not a tutorial on various scientific topics whose results it uses, rather it tries to convey an understanding of the mind, consciousness, and the scientific environment.
Rating:
Summary: A deep and brilliant book
Review: Baum's book aims -- and in my estimation succeeds brilliantly -- at illuminating what we know and don't know about computation and the modeling of mind: memory, learning, perception, reasoning, etc. Baum summarizes the main perspectives of various schools of thought on the topic, notably including both proponents of the artificial intelligence enterprise as well as critics, plus neural, sociobiological, psychological and philosophical points of view. He summarizes the main results of computer science and shows their relevance to mind. Best of all, the book is very well-written, and despite the fact that it includes considerable technical depth, it does not presuppose prior knowledge of the subject and should therefore be accessible to a broad audience.
Rating:
Summary: A deep and brilliant book
Review: Baum's book aims -- and in my estimation succeeds brilliantly -- at illuminating what we know and don't know about computation and the modeling of mind: memory, learning, perception, reasoning, etc. Baum summarizes the main perspectives of various schools of thought on the topic, notably including both proponents of the artificial intelligence enterprise as well as critics, plus neural, sociobiological, psychological and philosophical points of view. He summarizes the main results of computer science and shows their relevance to mind. Best of all, the book is very well-written, and despite the fact that it includes considerable technical depth, it does not presuppose prior knowledge of the subject and should therefore be accessible to a broad audience.
Rating:
Summary: Thought deconstructed...
Review: Dr. Baum's book on deconstructing thought is an excellent illustration of a mathmatician's perception of the mind. It is engaging, thoughtful, clear headed, and eye opening. The book does not talk down to the non-technically minded reader. I am looking forward to the author's next work, and would like to see an integration of this work with clinical neurophysiologic vignettes from real patients.
Rating:
Summary: Review of "What is Thought"
Review: 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.
Rating:
Summary: Review of "What is Thought"
Review: 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.
Rating:
Summary: Can a machine think?
Review: I thoroughly enjoyed this book. This book defines the subject well and intelligently brings in much well presented material from many areas, in particular, computational biology. I found the book to give one of clearest answers I have seen to just why the the subject is so tricky, and so fascinating. Machine thought, in many diverse forms, is inevitable.
Rating:
Summary: Can a machine think?
Review: I thoroughly enjoyed this book. This book defines the subject well and intelligently brings in much well presented material from many areas, in particular, computational biology. I found the book to give one of clearest answers I have seen to just why the the subject is so tricky, and so fascinating. Machine thought, in many diverse forms, is inevitable.
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