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In What Is Thought? Eric Baum proposes a computational explanation of thought. Just as Erwin Schrodinger in his classic 1944 work What Is Life? argued ten years before the discovery of DNA that life must be explainable at a fundamental level by physics and chemistry, Baum contends that the present-day inability of computer science to explain thought and meaning is no reason to doubt there can be such an explanation. Baum argues that the complexity of mind is the outcome of evolution, which has built thought processes that act unlike the standard algorithms of computer science and that to understand the mind we need to understand these thought processes and the evolutionary process that produced them in computational terms.
Baum proposes that underlying mind is a complex but compact program that corresponds to the underlying structure of the world. He argues further that the mind is essentially programmed by DNA. We learn more rapidly than computer scientists have so far been able to explain because the DNA code has programmed the mind to deal only with meaningful possibilities. Thus the mind understands by exploiting semantics, or meaning, for the purposes of computation; constraints are built in so that although there are myriad possibilities, only a few make sense. Evolution discovered corresponding subroutines or shortcuts to speed up its processes and to construct creatures whose survival depends on making the right choice quickly. Baum argues that the structure and nature of thought, meaning, sensation, and consciousness therefore arise naturally from the evolution of programs that exploit the compact structure of the world.
- Sales Rank: #2057255 in Books
- Published on: 2006-01-20
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x 1.00" w x 7.00" l, 1.73 pounds
- Binding: Paperback
- 492 pages
Review
There is no problem more important, or more daunting, than discovering the structure and processes behind human thought. What is Thought? is an important step towards finding the answer. A concise summary of the progress and pitfalls to date gives the reader the context necessary to appreciate Baum's important insights into the nature of cognition.
(Nathan Myhrvold, Managing Director, Intellectual Ventures, and former Chief Technology Officer, Microsoft)
A major work. Berger offers an elegant examination of issues that have been in controversy for the last forty years and that have been and are being discussed by the best philosophers of language. But where others have tended to offer piecemeal solutions, Berger offers a unified account based on a small set of principles.
(Gilbert Harman, Department of Philosophy, Princeton University)
A book that is admirable as much for its candor as its ambition.... If What is Thought? can inspire a new generation of computer scientists to inquire anew about the nature of thought, it will be a valuable contribution indeed.
(Gary Marcus Science)
... [Should] engage general readers who wish to enjoy a clear, understandable description of many advanced principles of computer science.
(Igor Aleksander Nature)
Eric Baum's book is a remarkable achievement. He presents a novel thesis -- that the mind is a program whose components are semantically meaningful modules -- and explores it with a rich array of evidence drawn from a variety of fields. Baum's argument depends on much of the intellectual core of computer science, and as a result the book can also serve as a short course in computer science for non-specialists. To top it off, What is Thought? is beautifully written and will be at least as clear and accessible to the intelligent lay public as Scientific American.
(David Waltz, Director, Center for Computational Learning Systems, Columbia University)
In his enjoyable and informative book The Evolution of Morality, Richard Joyce distinguishes between explaining how natural selection might explain socially useful behavior in animals and what more is needed to explain morality, with its thoughts about right or wrong, in human beings. Contrary to what others have said, Joyce argues plausibly that, to the extent that our moral concepts and opinions are the results of natural selection, there is no rational basis for these concepts and opinions.
(Gilbert Harman, Department of Philosophy, Princeton University)
About the Author
Eric B. Baum has held positions at the University of California at Berkeley, Caltech, MIT, Princeton, and the NEC Research Institute. He is currently developing algorithms based on Machine Learning and Bayesian Reasoning to found a hedge fund.
Most helpful customer reviews
25 of 27 people found the following review helpful.
Interesting but replete with hasty argumentation
By John Harpur
The main thesis of this book, asserted repetitively, is that the mind is a computer program. Once this is borne in mind, pardon the alliteration, most of the book is reduced to an argument in its favour, rather than an investigation into its credibility. The book often reaches for blunt assertions to support its positions and only afterwards begins a slight retracing of steps. For example, we are told that inductive bias and learning algorithms are coded into the genome. It is obvious, bit of speculation on DNA, evolution and algorithms and out comes the result!
In his observance of Occam's Razor, the author confuses the appeal of the simplest explanatory hypothesis with the belief that he has found such. The discussion of neural networks leaves aside recurrent networks, which are probably more biologically plausible than competitors.
Likewise the idea that the brain essentially 'runs' compressed programs due to evolutionary endowments is unconvincing and philosophically leaky.
I don't want to be over critical of the book as it has brought together many interesting strands of work, but it just has not woven them into anything interesting. There is little new here, whether from modularity or evolutionary programming constraints on neural activity. A lot of it is speculative and several of the key themes are discordant due to under analysis of their assumptions.
Several of the elaborations verge on the frivolous. For example, there is a particularly woolly argument linking the learning of Scheme to "what goes on in constructing our understanding of the world" (p. 222). Likewsie in discussing awareness and consciousness, the author relies on the use of 'main' in C to metaphorically explain how information might come together in the brain (p. 413-415). All kinds of reification fallacies come to mind, leaving aside the thinnes of the argument.
The bottom line is that the book pursues a strong cognitivist program (the brain is a computer) without convincingly examining various sides of the argument. I was certainly no wiser off at the end of it.
21 of 28 people found the following review helpful.
What Is What Is Thought?
By Jeff Becker
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.
15 of 25 people found the following review helpful.
Some interesting ideas here...but speculative at many places
By Dr. Lee D. Carlson
Rapid progress is now being made in the field of neuroscience, and this progress is not merely in theory, but also in laboratory measurements, thanks to the advances in magnetic resonance imaging. Also, advanced and practical applications in artificial intelligence are now a reality. Indeed, applications of artificial intelligence in the business environment are skyrocketing, and there is every indication that this will continue. Still though, the nature of human thought remains somewhat of a mystery, which is a kind of irony, given that intelligence is imputed to humans even without understanding fully what is really going on in the human mind when it is engaged in problem solving, reasoning, planning, or myriads of other activities. We do have non-human intelligent machines, but they are not considered to be by most, with the sole reason being that we can understand the nature of their problem-solving abilities. Will we then continue to view the human mind as exhibiting intelligence once we have deciphered its workings?
This book gives many different insights into the problem of human thinking and just what are its origins. Although written for the "popular" audience, much can be gained from reading it regardless of the reader's background. It does indulge in speculation frequently and reasoning by analogy, and it skirts at the ill-defined boundaries of philosophy, but it is worth taking the time to read in detail.
The author has a very specific view of intelligence as is readily apparent when he remarks that human intelligence has the ability to "understand" in many domains. Machines in his view though do not have this ability, but are "brittle", and cannot tackle different problems on the fly the way humans can. But does "understanding", as we frequently impute it to humans, have to accompany successful problem solving? Why is it that we are prejudiced in the requirement of "understanding" when we characterize an entity as intelligent? And is the ability to answer questions from many domains or contexts, however vaguely they are presented, really indicative of intelligence or understanding? The author wants to clarify the notion of "understanding", this to be one of the goals in the book. He asks whether there is some "quantity called understanding" that will serve to distinguish mechanical computation from thought. The author is expressing great insight in bringing this question to light, as it has long been a prejudice that machines are merely engaging in syntactical manipulation, and unable to deal with the "semantics" or understanding of the "meaning" behind the symbols.
The thesis of the author is very straightforward, namely that Occam's razor, as he defines it, serves as the foundation for human reasoning and the mind. The criterion of simplicity is formulated using Kolmogorov complexity, which is currently the most popular one, at least in the computer science community. Most interesting though is the author's view on compression, in that the human mind functions by using essentially compressed programs. Compression to him is the key to understanding, in fact is equal to it. Compressed descriptions are the origin of understanding, and the human brain has, through evolution and reinforcement learning, acquired very adept programs for a myriad of tasks relevant for human survival.
The author's arguments are interesting, but he frequently uses arguments by analogy rather than backing them up with empirical research. More use must be made of the research in neuroscience and psychology before claims can be made on the functioning of the human brain. Too much philosophical discussion has invaded the author's arguments, and this weakens his case in many places in the book. It is the opinion of this reviewer that those engaged in research into artificial intelligence, neuroscience, and closely related fields should declare a moratorium on philosophical speculation and argumentation. The conceptual spaces generated by philosophical speculation are too large to be practical, as they contain too much information, which is constantly expanding with time because of the lack of side constraints, or "inductive bias."
Since the efficacy of the human brain is the result of evolutionary pressures, one would naturally ask what role the genetic code would play. The author answers this question in very simple terms, namely that the generation of mind was due to the training of a compact program. This compact program was encoded as DNA, and evolution was a training process for this program. It took four billion years of this training to be compactified into an expression residing in the DNA. This viewpoint is an interesting one, and it sounds very plausible, but again, it still needs to be supported with empirical evidence.
Much use in the book is made of results from computational learning theory because of the author's belief that inductive bias is the crucial to learning in complex environments. `Inductive bias', as he views it, and how it is viewed by researchers in computational learning theory, is a certain preference in learning one concept rather than another. Certainly it is true, and it has been shown by research in computational learning theory, that inductive bias is useful in pruning the search space and can assist in omitting useless information that is not pertinent to the problem at hand. However, the author's view is much stronger regarding the role of inductive bias: he is claiming that it is absolutely essential for learning in complex environments and therefore that other approaches to learning in such environments will not be as efficacious. His views are thus at odds with certain results in computational learning theory regarding the absence of a "free lunch" in randomized algorithms (as reinforcement learning is). The author is claiming, perhaps without meaning to, that learning algorithms that incorporate inductive bias will give essentially a free lunch. The only way out of this difficulty might be to acknowledge that the learning processes used by the brain are still being subjected to evolutionary pressure and hence that the learning processes now being used are not optimized, and are undergoing modification (however slowly).
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