If you're interested in a concise and empirically grounded look at how organisms experience themselves and the world, this is a definite. There is also a nice look into the future with Paul speculating what may come from the study of neuroengineering with humans. This book is especially appealing in light of the fact that our population is being exposed to the novel findings in medicine, neuroscience, and all other fields that play a part in the NC perspective. If you're interested in this book, you may also want to check out the movie, "AI."

A book for a limited audience. You have got to be interested in some really seminal, currently unresolved issues of how the great invention of the ALU (arithmetic logic unit) still employed in every computer built to the present day, was a compromise effort by this genius. His thought was to model the human brain, and the ALU succeeded in modeling just a small part, but he was totally frustrated and unsatisfied by the result--for good reason. He points out that the very language of the human brain has not yet been discovered--the orders of magnitude by which its process and results exceed the merely digital high speed comparator we call a computer (my apologies to Bill Gates!) clearly demonstrate the existence of a logic and a mathematics, the simplest rules of which as yet defy all our efforts to understand its workings, while we experience its results every time we think. Depth of logical levels, and depth of arithmetic levels necessary to achieve the requisite results we obtain from our Crays and our PCs are scorned by the human brain in a radical simplicity as yet undiscovered (not in that it does it, but in how it does it: therefore he postulates the existence of a radically, essentially different math and logic inherent in its workings). He lays out the discoveries of Turing, McCullough and Weiner in a brilliant tour de force of known (1955)neurological and cybernetic discoveries, and how they charted his course in creating the ALU. He compares analog and digital and mixed models of computing but (in my opinion) oversimplifies the digital aspect of thinking and memory, deeming them to be the route used by the human brain in performing its unruffled magic. He closes by posing two questions that express the wonderment faced by a high level intelligence when accosted by the facts he was unable to wrap mental arms around: 1)"what essential inferences about the arithmetical and logical structure of the computing machine that the nervous system represents can be drawn from these ...conflicting observations? and 2)what are the logics and mathematics in the central nervous system [that must be]structurally *essentially* different from those languages to which our common experience refers? His fellow researcher, Warren McCullough similarly closed out his life and research by repeating a question that plagued him all his life: What is a number, that a man can know it, and a man that he can know a number?

This is a great book that pushed the limits of his time; his swan song, to be delivered as the Yale Silliman lecture, but never was, due to Von Neumann's tragic untimely death in his early fifties.

Perhaps the most famous and often quoted line in this remarkable book appears on page 39, where von Neumann declares that "The most immediate observation regarding the nervous system is that its functioning is prima facie digital."

The "prima facie" modifier is commonly taken to mean von Neumann saw the brain as "obviously digital," or "patently digital," and that it therefore must resemble a digital computer. But as you read the rest of the book, you quickly discover that this is not what John von Neumann intended. Von Neumann uses words cautiously and precisely, and to him, "Prima facie" means exactly what it says: "on its face."

In 1956, the brain appeared digital. But von Neumann thought this impression might be superficial. He thought that deeper biological investigation might well demonstrate that the nervous system is not, in fact, digital, or not completely digital. He believed it might work in some more sophisticated way, and suggests that perhaps some intermediate signaling mechanism, a hybrid between analog and digital, might be at work in the brain. For this and other reasons he actively resisted labeling the brain as a digital computer.

In the mid 90s, evidence began to appear that von Neumann was probably right to reserve his judgment. These curious new results show that a single nerve impulse is somehow able to convey information to the brain. This signal seems distinctly un-digital. A number of theories have popped up, some attempting to explain this whopping new mystery, others attempting to explain it away. But its impact on neurophysiology, and on conventional computer models of the brain, is pretty shocking. Not to say, devastating. (See Spikes, by Rieke et al, for a readable account of this story.) When the smoke clears, it would not be surprising if people go all the way back to John von Neumann, looking for traction, fresh starting points, and for von Neumann's wonderfully broad sense of what is possible in neurobiology - a sense we have evidently lost to progress in the years since he wrote this splendid essay.

Von Neumann did not include in this book his interesting views on the nervous system of the eye. He was an early adopter of visual memory systems in digital computers, and he was evidently intrigued by the way the retinal cells of the eye are arranged to look backward, that is, toward the screen of the back wall of the eye. Possibly he thought the retinal cells saw back there a thin film diffraction pattern. You can find his interest in the nervous system of the eye remarked in his brother Nicholas Vonneumann's book, John von Neumann as seen by his Brother, and this reminiscence is also paraphrased in Poundstone's Prisoner's Dilemma. Finally, some of the worldly story of von Neumann, his digital computers, and their role in the creation of the hydrogen bomb can be found in MaCrae's biography.

Perhaps the most famous and often quoted line in this remarkable book appears at the beginning of Part II, where von Neumann declares that "The most immediate observation regarding the nervous system is that its functioning is prima facie digital."

The "prima facie" modifier is commonly taken to mean von Neumann saw the brain as "obviously digital," or "patently digital," and that it therefore must resemble a digital computer. But as you read the rest of the book, you quickly discover that this is not what John von Neumann intended. Von Neumann uses words cautiously and precisely, and to him, "Prima facie" means exactly what it says: "on its face."

In 1956, the brain appeared digital. But von Neumann thought this impression might be superficial. He thought that deeper biological investigation might well demonstrate that the nervous system is not, in fact, digital, or not completely digital. He believed it might work in some more sophisticated way, and suggests that perhaps some intermediate signaling mechanism, a hybrid between analog and digital, might be at work in the brain. For this and other reasons he actively resisted labeling the brain as a digital computer.

In the mid 90s, evidence began to appear that von Neumann was probably right to reserve his judgment. These curious new results show that a single nerve impulse is somehow able to convey information to the brain. This is distinctly un-digital. A number of theories have popped up, some attempting to explain this whopping new mystery, others attempting to explain it away. But its impact on neurophysiology, and on conventional computer models of the brain, is pretty shocking. Not to say, devastating. (See Spikes, by Rieke et al, for a readable account of this story.) When the smoke clears, it would not be surprising if people go all the way back to John von Neumann, looking for traction, fresh starting points, and for von Neumann's wonderfully broad sense of what is possible in neurobiology - a sense of possibilities we have evidently lost in the years since he wrote this splendid essay. He is eloquent on the problem of selecting a memory "organ," and evidently thought the worst choice would be a neuron.

Von Neumann did not include in this book his interesting views on the nervous system of the eye. He was an early adopter of visual memory systems in digital computers, and he was evidently intrigued by the way the retinal cells of the eye are arranged to look backward, that is, toward the screen of the back wall of the eye. Possibly he thought the retinal cells saw back there a thin film diffraction pattern. You can find his interest in the nervous system of the eye remarked in his brother Nicholas Vonneumann's book, John von Neumann as seen by his Brother, and this reminiscence is also paraphrased in Poundstone's Prisoner's Dilemma. Finally, some of the worldly story of von Neumann, his digital computers, and their role in the creation of the hydrogen bomb can be found in MaCrae's biography.

This thin book is jam-packed with rigorous philosophical prose on quite interesting topics relating to contemporary discussions on perceptual knowledge, self-knowledge, and the M-B Problem. I highly recommend this (along with his Neurophilosophy) and Sellars' EPM.

Churchland is a U Pitt veteran, so this book is relevant to current Sellars-McDowell-Brandom discussions in the philosophy of mind. I would not say that this book presents his main arguments in favor of eliminative materialism (which is good).

The most important material here, in my opinion, is in Ch 2: "The Plasticity of Perception" (section 3--the conceptual exploitation of sensory information) and Ch 3: "The Plasticity of Understanding" (section 8--meaning and understanding; section 11--intertheoretic reduction and conceptual progress).

I also recommend: Rosenthal, The Nature of the Mind (Oxford UP); Searle, Rediscovery of the Mind; as well as Sellars and Bas Van Frassen; Lance-O'Hawthorne, Language of Grammar (Cambridge UP).

The book comes in two parts. The part one, which takes up more than a half of the whole book, explains what recurrent neural networks are and how those can be used to explain our own cognitive functions. This is generally a good introduction, I think. His style is casual, and we see certain smugness you normally expect at a college lecture, e.g., introducing certain authorities as his friends and presenting the picture his own daughter and the medial and lateral brain stereographs of his wife (Patricia Churchland). Like other popular science books, however, his description of neural nets is far from precise but let's not expect too much from a book of this kind. Unlike what some of our reviewers below suggested, he minimizes the use of scientific jargons and when he use such jargons he explains what those are. The first part was overall very much enjoyable to read.

You cannot expect it to be a fully philosophical book, though. His new epistemological framework arises from this newest perspective the theory of neural networks has created. To know what neural nets are is immensely important. Let's remind ourselves of a classic work in cognitive science and neurobiology. It's David Marr's _Vision_. There Marr expresses the view that physical (hardware) implementation is quite irrelevant. Now we know this is not true. To understand why this is so one may have to consult the part one.

The problem area is the part two. The chapter 11 was full of hopes and lots of blah-blah-blah's that bore you to hell. What's interesting, and makes you slightly angry, is his explanation of consciousness. Perhaps that is because Churchland's argument seem amazingly simple. But, to think about it, it has to be simple. Otherwise it cannot be a reduction. If you want to argue against reductionism, you need to bring up some form of dualism. In fact, this is what Searle does. Searle's arguments are not directed agains neural networks. His favorate scapegoat is symbolic computation. But this is something researchers have done away with a long ago. I personally think Searle never really understood what neural nets are.

What's not really satisfactory are these: Some will find he never really defeated Nagel and Jackson. I should agree with those who think so. If ever he did, his argument lacked logical clearity or I am very dumb. He is not successful in constructing a model of consciousness, either. The problem is, he thinks he is. Like Newton did, and Euclid earlier, he tries to create a set of descriptive axioms to come to grip with consciousness. But unlike Euclid, Netwon, and Einstein (remember his two postulates), some of his axioms require a first-person perspective. (ref. pp. 213-214) For example, to verify that consciousness disappears in deep sleep, somebody obviously has to go to bed. However imprecise, MEG maybe used to detect conscious activities in a live brain. But there exists no 3rd-person method to verify consciousness is a single unified experience. Churchland has been successful in explaining a lot but I think we still have a long way to go. And his descriptive theory is not adequate.

Plus, there is a misprint in page 230 of the softcover edition. The "o%cial" should be read "official".

I've not come across a more sensible and lucidly written philosophy book. The author loves and deeply believes in science. He shows to my satisfaction that the hard sciences can answer many humanities questions or make them clearly pointless. The chapters on vector processing are still not quite as scientific as the author would like them to be, but the book overall has significantly improved my understanding and appreciation of human and mammalian minds. Since Amazon doesn't do it, here is the table of contents: (1) The little computer that could: the biological brain, (2) Sensory representation: the incredible power of vector coding, (3) Vector processing: how it works and why it is essential, (4) Artificial neural networks: imitating parts of the brain, (5) Recurrent networks: the conquest of time, (6) The neural representation of the social world, (7) The brain in trouble: cognitive dysfunction and mental illness, (8) The puzzle of consciousness, (9) Could an electronic machine be conscious?, (10) Consequences for language, science, politics and art, (11) Neurotechnology and human life. Looking at the Index at the back, the entry that occurs on the most pages in the book is "prototype" which in this book means pretty much the same as what some other authors call a paradigm.

Developments in neuroscience over the last decade will doubtless be compared to the Copernican revolution, totally changing the way educated citizens think of human nature. Churchland provides an invaluable guide to recent research into neural networks, surveying the social, moral, legal and philosophical implications of contemporary neuroscience. While not giving as much emphasis to either the emotional dimension of cognition as Damasio (DESCARTES ERROR) or its social origins and ethical function (see my BEYOND RELATIVISM), Churchland's focus on neural networks has compensating advantages, especially for anyone interested in learning how to think about the way the brain works when we think. Must reading for philosophers and social scientists who are aware that the human brain is neither a blank slate nor a serial computer

If you are an eliminative materialist then you need help! Not that there aren't some interesting observations in this book - see the chapter with new data on "filling in" - but Churchland's tired example of Maxwell's discovery of electromagnetic waves only demonstrates how subjective the entire world of science really is. A more interesting example might be Maxwell's equations and how they relate to entropy, but I suspect that Churchland's actual knowledge of physics is more on the level of Betty Crocker's knowledge of microwaves...

As for neural nets: go read Perlovsky! I find it odd that Churchland, who loudly proclaims nets as the future of AI, doesn't appear to have read any of Perlovsky's papers; but I suspect he's too busy waving magnets in his living room generating EM waves.

A good collection of essays by recognized leaders in a burgeoning field of philosophy. Some are only useful if what the article is discussing is quite familiar to the reader. This holds in particular for some of the articles on qualia and the article on R. Penrose. It could also be said that the article on Dennett could have been marginally better if the last part, concerning his motivations, were snipped.

This book is dated when it comes to AI coverage. Among other things, it talkes briefly about the backpropagation algorithm , invented some 15++ years ago. While this book is about philosophy, it would be nice to have an updated version of this book giving a short overview of how the AI field is borrowing more and more ideas from natural evolution and real neural networks. Backpropagation is a specific (and really usefull ) algorithm, and sparked a new wave of excitement about artificial neural networks in the mid 80s. Still, one problem is that the algorithm, as far as I know, is not biology plausible. More recent criticism agains the algorithm would be really really usefull. A short overview of _recent_ AI progress in language understanding/image understanding among other things, would also improve this book.

Also, the book contains a chapter on neuroscience. I found it pretty hard to follow all the details here, because of the technical term used. But remember,- its not the easiest subject around, and carefull reading through the chapter will help.

The more philosophical part of this book is interesting, but to be honest its not my favorite subject, and I didnt know much about dualism and other philosophical problems before reading this book. Well, as a master degree student in artificial intelligence, I probably should have been more interested in philosophy, and in some areas this book is an eyeopener.

The mind-body problem, as it is called in Western philosophy, still has the attention of philosophers, despite centuries of debate. It will no doubt occupy more of philosophers time in the upcoming decades due to the resurging interest (and advances) in artificial intelligence. But the goal of most research in A.I. is now geared towards computational algorithms that are able to learn and can discover new knowledge or data patterns. The "hard A.I." problem, that of creating conscious machines, is not top priority it seems.

But philosophers will continue with the analysis of the nature of conscious intelligence, and the author is one of these. Interestingly though, and correctly, he asserts that progress in this analysis has been made, and he notes that philosophy has joined hands with psychology, artificial intelligence, neuroscience, ethology, and evolutionary theory in making this progress. And this will no doubt continue as advances in these fields are made, and the 21st century will see the advent of the "industrial philosopher". Once thought to be a purely academic profession, the ethical considerations behind genetic engineering and the legal rights of thinking machines will require the presence of philosophers in the rank and file of engineers, technicians, and managers. And because of this, these philosophers, and their coworkers will themselves have considerable knowledge outside their own field.

Again, the refreshing feature of this book is that the author believes that philosophy has made considerable process on the nature of mind. This was done, he says, by understanding the mind's self-knowledge, by providing a much clearer idea of the nature of the different theories of mind, and by clarifying the sorts of evidence that must be acquired in order to distinguish between these different theories. Empirical evidence, he states, has enabled the making of these distinctions much more rational and scientific. But he is careful to note that the evidence is still ambigious, and much work still needs to be done before the these ideas can be differentiated with more clarity. He discusses in detail the different theories of dualism and materialism. An entire chapter is devoted to discussing substance dualism, property dualism, philosophical behaviorism, reductive materialism, functionalism, and eliminative materialism. The author asks readers to start anew and throw away their convictions while analyzing these conceptions of mind and matter.

For the author, the mind-body problem cannot be solved without considering three problems: 1. Semantical: The meaning of ordinary common-sense terms for mental states. 2. Epistemological: The problem of other minds and the capacity for introspection. 3. Methodological: The proper methodology to use in constructing a theory of mind. Entire chapters are devoted to these, and after reading them the reader entering the debate on the mind-body problem for the first time will have an over-abundance of food for thought.

An entire chapter is spent on the topic of artificial intelligence. If this book were updated, this chapter would probably have to be considerably expanded, in that many advances have been made in A.I. since this book was first published. Research in A.I. has been rocky, and many promises that were unfullfilled were made in the past about it. But now it seems a more rational and realistic attitude is taken about the claims of A.I. Most everyone involved in it understands that it is an enormously complex problem, and have concentrated their efforts on building intelligent machines from a piece-meal, microscopic approach, i.e. from solving the simplest problems first before tackling the more difficult ones.

A chapter is also devoted to neuroscience. Thanks to imaging technologies and other approaches to mapping the brain, this field has mushroomed in recent years. The author only gives a cursory overview of the brain and the nervous system in this chapter, due no doubt to lack of space. The reverse engineering of the human brain has been pointed to by some researchers in artificial intelligence as being the best hope for building intelligent machines. The dramatic increases in chip technology and bus design have made this belief certainly more feasible. It remains to be seen, via actual empirical research, whether the reverse engineering of the human brain, and then its subsequent implementation in electronic devices, will indeed result in the rise of intelligent machines.

Whatever the future of artificial intelligence and neuroscience, the mind-body problem will no doubt be of interest to philosophers for decades to come. It will be fascinating to see what kinds of conceptual frameworks and methodologies will be employed in attempts to solve this problem. Without doubt some new ideas would be welcome in this regard, as proposals for solutions to the mind-body problem seem to be stuck in a local minimum. But, as the author argues well for, the solution will bring in many areas and possibly some radical ideas, all supported by painstaking experimentation.

It is quite ridiculous that someone should use Paul Churchland's Matter and Consciousness for a graduate class in philosophy of mind. Paul Churchland, for one, never intended it to be so, and certainly was not writing for such an audience. Having said that, Matter and Consciousness qualifies as one of the best brief introductions to pertinent issues in philosophy of mind. Do note, however, that Paul Churchland's focus is philosophical rather than psychological or cognitive. The book begins with a discussion of the mind-body problem and various standard proposed solutions, i.e. various forms of dualism, mind-brain identity theory, functionalism and the like. Each school of thought is presented in an orderly fashion, beginning with a brief outline of the general solution with a couple of examples, then proceeding to sections on the advantages and disadvantages of the school of thought in question.

Now, as with all truly introductory surveys of academic disciplines, the discussions in Matter and Consciousness are superficial from the perspective of more mature students. However, its brevity and clarity make it probably the best introductory text to philosophy of mind around. I read Matter and Consciousness in a single sitting over a cup of tea, and vouch for its accessibility.

Matter and Consciousness also has sections on the psychological, computational and neuroscientific side of things, and although much of the scientific material is dated, these sections still give the uninformed reader a general flavor of ongoing work in those areas, and much to contemplate.

If Matter and Consciousness is being used for an introductory course on philosophy of mind, I would suggest augmenting the material in Matter and Consciousness by selecting appropriate readings from Lycan's Mind and Cognition: An Anthology. Matter and Consciousness was written quite awhile ago, when work in parallel distributed processing in AI was just being resurrected, and way before the embodied cognition revolution. Therefore, it would be an excellent idea to look at section 4 (Mind as a Computer: Machine Functionalism) of Kim's Philosophy of Mind for a fairly theoretical introduction to the ideas behind artificial intelligence, brief selections from Russell and Norvig's introductory AI text or Winston's AI text for an understanding of standard techniques (i.e. search, neural networks, production systems and the like) in AI, and Andy Clark's introduction to the foundations of AI, "Philosophical Foundations", in Artificial Intelligence (Handbook of Perception and Cognition) edited by Margaret Boden.