Artificial Intelligence Books

Nuclear North Korea extorts Japan, terrorist rockets rain down on Israel. "I, Robot" is not a retelling of Isaac Asimov's science fiction classic, but a different metaphor entirely, as Tokyo Police Inspector Suzuki Haruto rigidly follows his own internal rules - much like Asimov's own robots. Haruto stumbles upon a massive arms deal and ultimately falls into a situation, clouded by his love, where he will either save our world or destroy it. "I, Robot" is deftly written and will grab you from first page to last while holding links to Asimov's tale. "I, Robot" is highly recommended for science fiction fans and community library collections for the genre.

A really fun book to read. Good story. Events take you all around the world. Technology is awesome, ending even better - I wasn't expecting it.

Watch Video Here: http://www.amazon.com/review/R1UQZOENOZ7EVH This is the book trailer for this book from YouTube. It is quite faithful to the book although the nuclear explosion is not over Tokyo but further offshore. I think more books should have these quick trailers so you can decide if you want to read the book

Watch Video Here: http://www.amazon.com/review/R2LBQG3VWNYYY0 This is the I,robot book trailer from YouTube. This trailer (unlike lots of movie trailers you see in the theatre and then get disappointed when you actually see the movie) is faithful to the book, although the nuclear bomb doesn't explode over Tokyo but quite a distance offshore (although it nearly sinks a Japanese destroyer). And, indeed the book... actually our entire world... does depend on the love one man has for a woman. Nope, his finger is not on a nuclear bomb. I'm not going to give away the ending here but it's about rules, keeping in the spirit of the Asimov original of this title, and what happens when the rules are applied. Great book trailer. I think every book should have one so you can decide if you want to read the book.

As an avid reader, I was entranced to find this hiddent gem among my collegue's recommendations. It is a beautifully written intellectual soujourn that probes the past advances and compares them to the current day technological advances. Sounds dry?? It's not. It's a poetic journey about what inventive advancements have meant in the past, and what they mean to the modern day intellectual. If you are in the mood for something that stretches your mind and enriches your soul, treat yourself to this rare gem of a book.

This book is a direct relative of Pirsig's "Zen etc" although neither author may agree. This author pens the words that are already in your mind.

This book was a beautiful read. The subject matter,comparing the Computer Age to the Industrial Revolution, was extremely interesting. It was fascinating to see the economic, cultural and technological similarities. As an added bonus, the author has a beautiful way with words, and therefore reading this book was a pleasure as well as being intellectually stimulating. I was captivated from the opening chapter on Alexandria. Highly recommended, and I am hard to please!

With careful research and amazing insight this author details for us, through the work of various people through the caenturies, how our present day computers were born. Through charming and poignant vinettes we learn of their lives and their work. From there, the author brings us to the delimmas the Information Revolution poises for us. A delightfully good read; an excellent liberal education. The vignettes are inspiring; the dicussion of the economics involved is thought-provoking. An outstanding first book.

Is there such a thing? And if so how has this contributed to our confusion in defining ourselves? Pick one: man, beast or Zombie. Notice that I said pick one while Malik's title MAN, BEAST, AND ZOMBIE implies that the choices are not mutually exclusive; choosing all three is a valid selection. I'll defer to Malik and simply say that he's convincing with his arguments in this well written and thoughtful book.

Malik's main purpose with this book is to show that much of our current thinking about human nature is incorrect. The focus is on evolutionary theory, sociobiology, evolutionary psychology, and cognitive science. Malik highlights the areas within each field that are seemingly in agreement on what makes us human, but the real value of this book, and what Malik does exceptionally well, is show how the abiding contradictions are largely steeped in politics and that by understanding this we can emerge with a clear idea of human nature. Far from arguing that science has contributed to a dehumanizing vision of ourselves and that genetic determinism and Darwinism is off, Malik says it's "mostly right" but that "when it comes to the science of Man" things are different. Malik shows how one can support Darwinism but still have a humanistic view of our nature. He's certainly not saying that science is a social construction, but he also does not agree with Daniel Dennett who explains all mental and social aspects of humanity in mechanistic terms as adaptations of evolution. In Malik's capable hands the divide between evolutionary psychology and sociobiology is illuminated and is seen in terms of a philosophical and political argument, but one that is still about the same underlying evolutionary truth. The same can be said for the seeming uncrossable chasm between evolutionary psychology and cognitive ethology. Malik himself takes a position. He sides with Dennett and says that animal behavior tells us nothing about human nature and that studying modern hunter-gatherers can't tell us much about stone-age man. He spends a bit of time refuting Jared Diamond's arguments and pretty much ignores cognitive ethologists. Malik believes that the idea of "self" or consciousness is created by language and thus defines what makes humans unique. Malik's view however is no more than just another position, as is any other, on the same philosophical/political spectrum.

This book is a very useful contribution to the ongoing debate about human nature. It is eloquent in arguing against a deterministic, materialistic, and mechanistic view of humanity. Equally cogently argued is Malik's belief that we should steer clear of an overly humanistic view that borders on mysticism. I'm not disappointed that Malik doesn't (or can't) define an ideal resting point, as it simply proves that reality remains a mix of both the physical and that which is in the consciousness. And where we place reality is still a function of where each of us sits on that all important philosophical/political spectrum.

Many people assume that the only ones who flinch from reductionist accounts of human nature are religious believers who 'lust after skyhooks' pace Dennett and are afraid of losing a sense of mystery, or have a godlike view of human nature they are anxious to preserve. Not so. This book, certainly one of the best introductions and critique of Darwinian theories of human nature on the market, shows that you do not have to be religious to feel concerned (and rightly so) about the extravagant claims of some 'Universal Darwinists' when it comes to what makes us human. Kenan Malik takes us on a fascinating journey into history, revealing the roots of the current obsession with dehumanizing views of human nature. Especially after the 2nd World War many people lost all faith in human decency and thus were more disposed to view people as 'zombies' or 'beasts', essentially survival machines with no higher qualities. Also contributing to the dehumanizing was the struggle of evolutionary biologists to defend the legitimacy of evo-bio as a 'real science' against the imperialist reductionism of molecular biology.

Malik makes observations which should not be overlooked or taken for granted by anyone interested in what it means to be human. He rightly observes that at the root of the current confusion over human nature is our lack of a way to conceive of ourselves as both subject and object; as a subject we are (presumably) social, reflexive, rational beings who have real responsibility and agency, but as objects we are obviously biological machines, made of hydrocarbons and molded through natural selection. To study human nature scientifically is to encounter this paradox at its most profound, since in this case we are both the subject performing the inquiry and the object of our investigation. He is surely right that while human beings are immanent in nature, in the sense that we and our minds are products of biological evolution, we are also in some sense transcendent to it, as revealed by our ability to do science. For many modern thinkers the temptation is just too great to deny human transcendence and view human beings solely as objects, even though this view is self-refuting: if we are just biological machines obeying the dictates of genes and culture, how do we know that science isn't just another adaptive fiction? How we make sense of ourselves as rational creatures?

Interestingly, although Malik makes telling, scientifically informed (he is a research psychologist) critiques of current trends in evolutionary psychology and stresses the need to hold a view of human nature adequate to our self-understanding as rational, responsible creatures, he does not go very far in resolving the paradox he reveals. He makes some interesting remarks on the need for a theory of 'social selection', the semiotic capacities of language and the 'extended mind' all of which are probably in the right direction, but his own account of human distinctiveness falls short of his own goal. Clearly we still have a long way to go in our study of human nature.

The one glaring omission in this otherwise magisterial manifesto is attention to religious perspectives on human nature. Beliefs about the soul are mentioned only in passing in his historical analysis, and Malik does not consider the possibility that religious perspectives, such as the Christian theory of human nature, might go a long way towards resolving the paradox of object/subject distinction. Indeed, Malik almost betrays a religious orientation himself, but in the end affirms his belief in the Enlightenment ideal of human goodness, which may be, in the words of Jeffrey Burton Russell, "the most counterfactual idea in human history".

All in all an enormously important, controversial book which has not received its due attention because of the celebrity-mongering of Darwinian superstars like Steven Pinker and Jared Diamond. One can only hope that more people will read this book and start asking questions before the view of man-as-zombie or man-as-beast becomes too firmly entrenched in our cultural understanding, with possibly disastrous consequences. Finally, it has great potential, which is not recognized by its author, to harmonize religious and scientific perspectives on human nature. Our self-understanding as rational, responsible creatures is simply not up for grabs, something that religious voices in the science-religion dialogue have been stressing for decades. Another highly recommended, indispensable read.

First-rate guide to the history and current status of human nature. Overall it's depressing, which I took to be accurate reporting.

The first 100 pages are wonderful. Malik's history of human nature up to the mid-20th century I found brilliant, extremely insightful, the best account of that history I could imagine. Just those 100 pages would make this an extremely useful and valuable book. He does go at a fair clip, though, so it might not mean much to someone altogether new to the material. But it's clearly expressed and it makes a masterful refresher to the resources propping up our current notions of human nature.

Great, I thought. I'm in the hands of the perfect guide--well-informed, intelligent, sensitive--to the next 50 years, to which Malik gives the next 200 pages, bringing the story up-to-date.

Those 200 pages were a slog. They seemed rambling and repetitive. The subject matter seemed trivial compared to what had come before. I wondered why he and I were bothering with it. Where was the meat and potatoes?

And that, I think, is the real story of this book. There is no meat and potatoes any more. The tradition's stopped, and Malik's failure to make the story gripping is a due reflection of that---he's reporting fairly. As he describes it, the main intellectual activity over the past 50 years---at least as far as science is concerned--has been coming up with paradoxes and pitting one paradox against another, like boys playing scissors, paper, stone in the schoolyard. "You attack mine, and I'll attack yours, and we both get to publish," something like that. But who else, Malik seems to feel, needs to care? He does due diligence, but his heart's not in. So he regurgitates one minor variant on determinism after another, ranging from beast to zombie and back again, to each of which he makes not very convincing objections. He does, though, explain several times why this all matters, what's at stake when we shrink human nature down to a one-inch square box.

Most disappointing to me were the final two chapters where he gives his own account of the rudiments of human nature. Clearly he's master of the material, both the history and the current theories. But he's unable to break out of the box limiting the theories he criticizes. He says, on the one hand, that human nature can come only from either genes or culture (including socialization) or a combination of both. But then he says humans can "transcend" those, without explaining where that ability comes from. He seems to assume that this is a universally shared belief. Coming from him, I felt I had to assume it is indeed universally assumed.

So, no magic bullet, no penicillin, but a thorough round-up of where we stand today with respect to human nature. Not a pretty picture.

Ever since Richard Dawkins preached modern 'Neo-Darwinism' in his book, 'The Selfish Gene', a tremendous debate has been raging in academic departments, and amoungst the general public, as to how influential the entities we call 'genes' are in determining human nature. .....

Those who know their history will recall that the current debates about genetics seem disturbingly close sometimes to the ideas about race, genetics and human nature in the early 20th century which ultimately culminated in nightmarish and barbaric events such as the forced sterilisation of 'unfit' people, even in bastions of freedom like America and Europe, and in Nazi Germany, the attempted extermination of an entire people solely on the base of their 'race.' Malik's study attempts to understand the intellectual and historical basis of these ideas, and updates them in light of recent scientific developments in evolutionary biology.

Malik carefully traces the historical outlines of the debate over exactly what role inheritance plays in human nature, drawing on a remarkably broad and eclectic base of history, philosophy, biology, anthropology and psychology. Malik carefully argues a human nature is not entirely determined by ones genes, but is rather something constructed from both one's genetic inheritance and culture.

What makes this book so good is that Malik presents a balanced assessment of this controversial issue-'nature vs nurture'- without descending into the dismissive, arrogant and narrow viewpoint of an idealogue. His wonderful assessment of one area, sociobiology, and the tragic and colourful human figures who invented it, is just one fine example out of many. It makes a refreshing change from Dawkins or Dennett, or their creationist/constructionist enemies, who seem to base their works on dismissive rhetoric rather than the good, solid argument coloured with sound historical understanding and an awareness of the human condition that characterises Malik.

This book is thoroughly enjoyable and highly recommended for insight into the debates about evolutionary psychology around today.

Netlab implements in Matlab most algorithms presented in Christopher Bishop's excelent book Neural Networks for Pattern Recognition

Nabney's book is an indispensable guide if you want to go into the inner workings of Netlab.

Recommended.

Amazingly compact book and MATLAB toolbox that provides an exceptionally clear introduction to a core collection of pattern recognition tools. This text and the related MATLAB toolbox ostensibly accompany Chris Bishop's text _Neural networks for pattern recognition_ and brilliant as that book is, this is the perfect supplement that does so much more than just implement the algorithms (and not just for MATLAB users but for anyone who appreciates the merits of learning by doing). From the introductory chapters on MATLAB syntax and optimization (clearer and more useful than the _Numerical Recipes_ version) you know you are in for a breathless ride but the examples and demos are perfectly chosen to illustrate the relative merits of the different approaches under consideration.

The chapter titles are

1. Introduction
2. Parameter optimisation algorithms
3. Density modelling and clustering
4. Single layer networks
5. Multi-layer perceptron
6. Radial Basis functions
7. Visualization and latent variable models
8. Sampling
9. Bayesian techniques
10. Gaussian Processes

The MATLAB code is elegant and well-commented and lends itself to endless tweaking and experimentation. I wish I had written this book. Congratulations to the author and hope there is another book on the way.

This is actually a must-have book for those who want to study pattern recognition.

i first bought the book by Bishop (Neural Network for Pattern Recognition) and anyone who have read it can tell u how excellent that book is. This book has a little bit less theory and more on implementation which is perfect for me. This book include all the topics covered in Bishop and then some. How the book is organized, and how concise, easy to understand the material is at the same amazing level as Bishop's. I believe implementing and practicing things u learn is key to understanding them.. if you just look at how things are implemented, things would suddenly become 10 times clearer for you.. often to your own amazement (that you can actually understand all those stuff). this book is extremely useful even if u dont have matlab (just look up the syntax at mathworks web site), cuz matlab code is straightforward to understand. and the material included is very up to date and cutting edge indeed. i highly highly recommend it.

I have spent the last six months of my life learning as much as I can about parsing. I own half a shelf of compiler books, and I have flipped through the pages of half a shelf more.

No other book approaches the clarity and comprehensiveness of this book.

When you try to read most literature about parsing, authors tend to throw around a lot of terms without explaining them. What exactly is a "deterministic" parser, a "canonical" parser, a "directional" parser? Grune and Jacobs explain every one of these distinctions lucidly, and put all known algorithms in context of how they compare to the rest of the field. How do the algorithms compare in what languages they can parse, how fast they are, and how much of the work can be done ahead of time? The book addresses all of these trade-offs, but doesn't stop at asymptotic complexity: in chapter 17 (the comparative survey), they note that general parsers may be a factor of ten or so slower than deterministic methods, even though both are linear. This high-level overview and comparative survey are something I was desperately seeking, and I've found nothing comparable to them anywhere.

There is also a lot of important background information that other authors tend to assume you know: for example, did you know that when authors say "LL" they almost always mean "strong LL" unless they specifically say "full LL?" Are you totally clear on the difference between strong LL, simple LL, and full LL? If you're not sure, Grune and Jacobs will give you all the explanation you need to fully understand.

This book strikes a perfect balance between breadth and depth. All significant algorithms are covered, most with enough detail to fully understand and implement them, but Grune and Jacobs punt on less practical material like proofs or rigorous formal descriptions. That information is never more than a citation away though, thanks to the 417-entry annotated bibliography, which gives you not only references to source material but a paragraph or two describing their key results.

I couldn't be happier about adding this book to my bookshelf of compiler books -- it quickly became the book I refer to most often, and I thank Grune and Jacobs for this superb guide to this vast and diverse field of computer science.

After searching all over for a way to understand the translation field and looking the dragon book and all, this is a great find. I am a practicing software engineer with training in electronics (good old forgotten days) and did not like math classes. This book is a great way to make this topic approachable for a practicing industry developer. Admittedly is a difficult read but if you want to understand something it needs the effort. If you cannot read the # * + etc in the other compiler books this books makes it comprehendible.

The first edition is available at Grune's web site but this very much expanded second edition is not.

just google the first author. he has the pdf version online.

For someone with a little background and a lot of determination, this book provides a good basic grounding in the issues of tomographic reconstruction and the basic mathematical tools involved. Discussion starts slowly, with a chapter that establishes the vocabulary and notation of the signal processing involved. The next three chapters discuss non-diffracting cases, where the radiation that senses the body structures is not appreciably deflected by them, as is the case for CAT, PET, and SPECT. This includes discussion of the sensors, illuminators, and their geometries, on up to helical scans and complex sensor geometries. It also includes confounding effects, like the wavelength dependent nonlinearities in absorption of X-rays and how they affect beam transmission and the final image produced.

This chapter includes only brief menton of MRI, because of the very different physics behind it, and of ultrasonography, because of the diffractive and refractive features of the radiator and tissues being examined. Likewise, little mention is made of the reasons for different modalities or techniques for merging their results.

The final chapters address the special problems of ultrasound, digging as far in as the wave equations and the common approximations that make the wave equations at least somewhat practical as tools for solution. These chapters also address more advanced and computationally exhorbitant algorithms, though not in nearly the detail that back-projection got in the earlier chapters.

This book first appeared in 1988, which seems like centuries ago in the time scale of tomography algorithm development. Even the 2001 update is aging, and it never really went into the Feldkamp algorithms now widely in use. The discussion of sonography seems sketchier than discussion of the X-ray based modalities, and MRI newer exotica get little if any attention. That's fine, though. It's a big field, and the authors do reasonably well at defining and addressing the area they intended to cover. The working algorithm developer won't get much from this classic. The target audience today is probably a grad student or industrial practitioner who's been thrown in at the deep end. As long as its limits remain clear, this is a helpful introduction for readers with the math skills and time needed to extract its value.

-- wiredweird

This book is one of the clearest introductions to tomography. I use it as a text in my course, and my students have also liked it.

This is still a great text on the principles of tomographic imaging. Buy it!

This book, which has become a classic, is a must for anyone who wishes to study tomography.

Charge coupled device (CCD) count the number of electrons within each pixel. Each number is stored, so the whole image can be represented by a series of numbers. Computers can see my means of this device and attempt functional replication of the eye. In a color image the numbers represent both the hue and intensity of the pixel.

One of the earliest vision problems to be subject to machine recognition was hand-writing technology. Character segmentation is important because printed characters can be of different size and can be separated by neighbor characters by different distances. The PDA made handwriting recognition an important field of research. The recognition system possess information about how the characters were written, writing direction and the writing order of the strokes and match with the shape of stored characters. In 1960, Israel Gelfand, at the USSR Academy of Science developed a successful natural handwriting technology. Stefan Pachikov founded paragraph International which SGI later buys. NHR technology underlying idea is that fact that cursive handwriting is a series of movements made by a writing instrument. Each movement can be represented by one more more of eight elements that are sufficient to describe all the trajectories of the pend found in the cursive letter of the Roman alphabet. The analytical word recognizer is based on a database of symbol prototypes and neural network generalized pattern recognition schemes and training.

Human Face recognition differentiates unique physical attributes about a person face, the different heights, depths, and weights. Computer vision systems can pick peoples face out of a crowd almost instantaneously and measure various features of that face and compare the measurements with those faces stored in the database. Everyones face has distinguishable features for example peaks and troughs. There are about 80 of these features on the human face, including distance between the eyes, the width of the nose and the depth of the eye sockets. The computer after measuring the face creates a numerical number representing the face. Usually 14 to 22 of the 80 features in a face print is enough to complete the recognition process. Video surveillance system search for face in Low resolution image of the scene and switches to a high resolution search when a head-like has been spotted. Once a face is detected, the system determines then determines the position, size and pose of the head. The image of the head is then scaled up or down in size and rotated in the same size and pose employed for faces in the system's database. The most successful recognition system can match faceprints at 60 million per minute.

MobileEye acts as a silent driver assisting with Forward looking, side mirror, and in cabin recognition. MobileEye can detect cars moving into the passing lane, distance ranges, and switch attention by changing colors indicating possible collision objects, pedestrians moving into the travel lane, and off-road path finding. The recognition software can watch passenger position and make decision for airbag deployment. Cameras on the side mirror can watch blind spots and warn for sudden merges into the passing lane by other cars. Side mirror recognition differentiates between cars not within collision and those who are. Forward looking recognition system can recognize markings on the road. "The system fits a three-parameter road model that accounts for lateral position, slope and curvature. The curvature parameter is used for increasing the warning reliability under curved roads and for estimating time to lane crossing."

The ears of a computer are microphones, devices that contain some sort of diaphragm that vibrates in concert with audible sound. The vibrations are converted to electrical signals, which can be displayed as a waveform on a screen or measured electronically. Speech recognition is recognizing waveforms. Different people can say the same word with different pitches, speeds, and intensities; all these variation change how the word is said. Dynamic time warping has the affect of stretching or compressing segments of the speech sound in a word, in order to make the waveform easier to match with a store waveform. A technique called Hidden Markov Models HMMs are used to recognize phoneme strings and calculate summed values for all possible combinations of the sounds. The highest probabilities phoneme string is selected. Visual recognition systems are being used to watch lip movement and use context feedback to improve speech recognition.

It's been about 50 years since the word Artificial Intelligence was coined. Since then there have been a number of television shows and movies about AI, but in real life AI has yet to produce a young boy to life an even quasi-normal life.

Behind the scenes however, research has been going on to develop the sub-systems needed as a foundation of AI. In this book the author describes what's going on in computers about such critical areas as vision, speech, taste, smell and so on.

The big problem, and what's covered in most of the book are what you might call the thinking components. How do computers think? How do they play games such as chess? Or one of the hot new items, play soccer. Then there are real problems like getting the computer to write fiction? Can a computer be programmed to transpose bits and bytes into thought, or love?

There have been a number of books lately on robotic activities you can do at home. This one is a description of the state of the art in the research labs around the world.

Robots Unlimited: Life In A Virtual Age by David Levy (leader of the winning team of the Loebner Prize Competition in 1997) is a highly researched and historically impressive documentation devoted to the past fifty years of research and development in Artificial Intelligence and Robotics. As an informative and superbly written study, Robots Unlimited offers readers an outstanding historical survey and a seminal reference to the many intricacies of an ever-escalating modern science in these specialized fields, as well as knowledgeable and intuitive predictions of what the future may bring for robotic and artificial intelligence breakthroughs. Very strongly recommended to all students of Robotics, Artificial Intelligence, and relevant technological advancements, Robots Unlimited gives its readers a complete and expert analysis and collection of such a popular and innovative science.

Throughout the last five decades, fed by both curiosity and military requirements, the design and construction of robots has occupied the time of many researchers, and involved the spending of hundreds of millions of dollars. In this book the author presents an overview of robotics for a semi-popular audience, beginning with a fairly detailed summary of the early history of artificial intelligence. It should be remembered that robotics is but one subfield of artificial intelligence, and that the latter field encompasses much more than the building of humanoid-looking machines. And interestingly, when one compares the research forty or even fifty years ago with what is going on at the present time, it is readily apparent that the differences are more of quality rather than quantity.

But intelligent machines do not have to take the form of humanoid robots. Hollywood and science fiction novels are partly responsible for this attitude, as are the philosophers, who insist upon the Turing test as being a genuine test for machine intelligence. It is evident when reading the book, especially the last part, that the author will not be convinced of the existence of intelligent machines until they do most, if not all, of the things that humans do. This includes the ability to make love, the ability to reproduce, the possession of legal rights, the possession of consciousness, and the ability to feel emotion and fall in love. A machine taking the form of a humanoid robot that was able to do all of things would certainly qualify as being intelligent. But there are many other types of machines, some of which exists today and are working in the field, that qualify as being intelligent, even though it is a different type of intelligence than what most humans are used to (or would acknowledge as such).

This observation raises another issue that is noticeably lacking in this book, as well as in the history of artificial intelligence in general. This issue involves the adoption of a quantitative definition of machine intelligence that will allow its measurement. If one is to judge the progress in artificial intelligence, it is necessary to define criteria, possibly informal, for assessing to what degree one machine is more intelligent or of higher quality than another. The criteria must also be able to distinguish an intelligent from a non-intelligent machine. The Turing test is not entirely suitable as a criterion, since it emphasizes, somewhat myopically and exclusively, human intelligence as being the most objective measure.

After careful study of the history of artificial intelligence, in this book and many others, as well as research papers, and through the development and practical use of `algorithms' that are deemed to be intelligent in some way, this reviewer arrived at an informal classification scheme for intelligent machines. Sometimes this scheme allows the quantitative measurement of machine intelligence, a `machine IQ' if you will, but usually it classifies machines according to what they can do, and to the degree that the machines require assistance from another machine (human or not).

For example, one could label a machine `Type-1' if it is an ordinary calculating machine, unable to learn or check its answers, or unaware of its environment. Type-1 machines are uninteresting from the standpoint of artificial intelligence research. A `Type-2' machine can find answers to domain-specific problems and check these answers according to standards given to it from another machine. Type-2 machines essentially need `tutors' or some kind of assistance to evaluate or continue learning. The chess playing machines described in this book, such as Deep Blue and Deep Thought, could be classified as Type-2 machines. The Pinkerton music-creating machine is also Type-2 as are the rule-based music-creating machines discussed in the book.

`Type-3' machines are able to check their answers to domain-specific problems and make judgments as to the quality of these answers, and do independently of any external standards. The Samuel checkers playing machine and the NeuroGammon and TD-Gammon backgammon playing machines described in this book could be classified as Type-3 machines, as would the `metagame' machines that can learn how to play a game given only the rules. Also Type-3 is the bridge-playing COBRA machine, and the Poki poker-playing machine, the Thaler Creativity Machine, the BRUTUS storytelling machine, all of which are discussed in the book.

A `Type-4' machine is one that is able to judge the quality of its answers to domain-specific problems and then propose theories or explanations that subsume these problems. Type-4 machines are thus machines that one could use to conduct scientific research for example. The EMI music-making machine discussed in the book is a Type-4 machine, due to its ability to analyze the structure of the music presented to it, and then extract the composer's style from it. Type-4 machines have been used in automated drug discovery, although this use is not discussed in this book.

Next are the `Type-5' machines, which are able to solve problems in more than one domain, but with their interest in solving these problems is instigated by an external inquirer, i.e. they do not possess any innate curiosity. The `commonsense reasoning' machines of Cycorp, Inc, which are discussed in the book, are examples of Type-5 machines. It is their ability to solve problems in more than one domain that makes Type-5 machines of great interest to many in the artificial intelligence community. Many in fact do not believe a machine is truly intelligent unless it can think in more than one domain.

A `Type-6' machine can express curiosity and creativity, can solve problems without any external instigation, and can develop theories or explanations around these problems. The author discusses several types of machines in the book that could be classified as Type-6, if one omitted the ability to find solutions without being instigated by an external machine or human.

Lastly, there are `Type-7' machines, which can self-manage and self-replicate, and are also Type-6. Self-replication is discussed in the book, but there are no machines to date that are Type-7.

This book aims at rigorours and deep treatment of statistical learning and is divided into three parts :

(I)THEORY OF LEARNING AND GENERALIZATION;

(II)SUPPORT VECTOR ESTIMATION OF FUNCTIONS;

(III)STATISTICAL FOUNDATION OF LEARNING THEORY'

For anyone intending to dive into this topic intriguing readers shull find their task rather not simple when exploring this mathematical exposition.This is because of the mature nature behind the basic theory .In order to gain most of the benefit ,interested and even involved researchers are urged and should assume all the requirements for a vast and solid mathematical background.

I Think the book constitutes a respectful and organized 'exhibition' that you will not find in any other place. Althought there are excellent books discussing SVMs and Machine-Learning/ Intelligence,eventually all emenate from the theory.Regarding the book rating it is was not rated upon how much you retrieve as concepts, but how well the propositions offer a precious appreciation of the substantial theory.In otherwords, this book is not the place for a first time learning, but it is serves as a bridge between interrelated elements of such incredibly growing area.

For the book: "The Nature of Statistical learning Theory" also by Vapnik you can find a review by Vladimir Cherkassky in The IEEE TRANSACTIONS ON NEURAL NETWORKS VOL. 8, NO. 6, NOVEMBER 1997 .

Vapnik and Chernovenkis extended the Glivenko-Cantelli Theorem in their work on classification and statistical learning. Vapnik in recent texts has described a form of nonparametric statistical inference based on approximating functions and the Vapnik-Chernovenkis dimension.

In an earlier book published by Springer-Verlag he develops the basics of the theory. However to keep the mathematical level excessible to computer scientists and engineers he avoided the mathematical proofs needed for mathematical rigor. This text is an advanced text that provides the rigorous development. Although the preface and chapter 0 give the reader a idea of what is to come the rest of the text is difficult reading.

The theory has been quite successful at attacking the pattern recognition/ classification problem and provides a basis for understanding support vector machines. However Vapnik sees a much broader application to statistical inference in general when the classical parametric approach fails.

If you have a strong background in probability theory you should be able to wade through the book and get something out of it. If not I recommend reading section 7.9 of "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. That will give you an easily understandable view of the VC dimension. Also sections 12.2 and 12.3 of their text will give you some appreciation for support vector machines and the error rate bounds obtainable for them based on the VC dimension.

Vapnik and Chernovenkis extended the Glivenko-Cantelli Theorem in their work on classification and statistical learning. Vapnik in recent texts has described a form of nonparametric statistical inference based on approximating functions and the Vapnik-Chernovenkis dimension.
In an earlier book published by Springer-Verlag he develops the basics of the theory. However to keep the mathematical level excessible to computer scientists and engineers he avoided the mathematical proofs needed for mathematical rigor. This text is an advanced text that provides the rigorous development. Although the preface and chapter 0 give the reader a idea of what is to come the rest of the text is difficult reading.

The theory has been quite successful at attacking the pattern recognition/ classification problem and provides a basis for understanding support vector machines. However Vapnik sees a much broader application to statistical inference in general when the classical parametric approach fails.

If you have a strong background in probability theory you should be able to wade through the book and get something out of it. If not I recommend reading section 7.9 of "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. That will give you an easily understandable view of the VC dimension. Also sections 12.2 and 12.3 of their text will give you some appreciation for support vector machines and the error rate bounds obtainable for them based on the VC dimension.

The field of statistical learning theory has not only seen considerable advances in the last fifteen years, it has also found many applications, some of these appearing in commercial packages. It is now classified as a subfield of artificial intelligence, and as such gives an alternative, and frequently more general viewpoint on such topics as pattern recognition, regression estimation, and signal processing. The author of this book is one of the originators of statistical learning theory, and has written a book that will give the mathematically sophisticated reader a rigorous account of the subject. Most of the main results are proven in detail, but the author does find time to include insightful discussion on the origins and intuition behind the concepts involved in statistical learning theory.

Along with a brief introduction, the book consists of three parts, the first being an overview of the statistical theory of learning, the second giving the details of the now widely used support vector machines, and the last one (the most sophisticated mathematically) giving the statistical foundations of learning theory. In writing the book, the author wants to put forward a new approach to dependency estimation problems having their origin in learning theory, and being able to deal with the ?curse of dimensionality?. The origins of the subject lie in the pattern recognition problem and the Glivenko-Cantelli problem in statistics. Both of these problems were discovered to be essentially the same, and the author?s task is to use their similarities to construct a general theory of statistical inference and (inductive) learning. Indeed, a new induction principle, called ?structural risk minimization? (SRM) is paradigmatic in the book, along with the now ubiquitous VC dimension, the latter of which originates in the author?s early research. Both the SRM and the VC dimension illustrate the tension between the need for high accuracy and the need for the minimization of error in data sets.

The learning problem, as the author sees it, is the problem of selecting the correct dependence on the basis of empirical data. Two approaches to this problem are discussed, the first using a ?risk functional?, and the second involving the estimation of stochastic dependencies and the consequent solution of integral solutions. Both of these approaches are modeled in terms of a general model of learning from examples, which consists of a data generator, a supervisor, and a learning machine. The learning machine can either imitate the supervisor or identify how the supervisor operates. These two methods are different, the author says, in that the first one searches for the best prediction based on the data, while the second one attempts to approximate the operator representing the supervisor. Both approaches are studied in the book, with the first one being the easier of the two, while the second involving the solution of ill-posed problems. The author views the learning process in terms of choosing the right function from a given function collection.

Both perceptrons and their generalizations, neural networks, are briefly discussed in the book, along with the back-propagation method. The author gives reasons why he does not think neural networks are well-controlled learning machines, such as the existence of local minima, the slow convergence of the gradient method, and the choice of scaling factors. These problems serve as motivation for the introduction of support vector machines, which are introduced as optimal separating hyperplanes. Support vector machines take input vectors into a high-dimensional feature space via a nonlinear mapping, and an optimal separating hyperplane is then constructed in this feature space.

Similar to the need for neural networks to generalize well, separating hyperplanes must do the same, and due to the large dimensionality of the feature space, a hyperplane that separates the training data may not generalize well. In addition, the large dimensionality of the feature space makes the construction of the hyperplane computationally demanding. The author shows that optimal hyperplanes, found using various mathematical techniques such as quadratic optimization, do generalize well. Also, as the author points out, the explicit form of the feature space need not be known, since only the inner products between the ?support vectors? and the vectors of the feature space need to be calculated. The calculation of the inner product is done with the insight gained from Mercer?s theorem, which gives the existence of a kernel function such that there exists a feature space where this function generates the inner product. This inner product in feature space allows the construction of a decision function that is nonlinear in the input space but that is equivalent to a linear function in the feature space. Different choices of the kernel function give different types of learning machines. The author discusses three examples of support vector machines for pattern recognition: polynomial, radial basis function, and two-layer neural network support vector machines. An entire chapter is spent on the problem of digit recognition using support vector machines.

To fully answer the question whether machines can think seems to presuppose the question "what is thinking?" In other words, how will we know when a machine thinks? Will it tell us? Will it compose sonnets? The eponymous "Turing test" attempts to unravel this paradox. To greatly simplify, the test states that if a human interpreter, alone in a room, cannot distinguish answers given by a machine, in a separate obscured room, from answers given by a human being, in a third obscured room, then the machine must have human-like intelligence. Alan Turing, often credited with the invention of the now ubiquitous computer, proposed these criteria in a 1950 paper called "Computing Machinery and Intelligence." He was not trying to invent a computer, even when he provided an unmistakable model for one, the "Turing Machine," in a 1936 paper. Instead, he sought to model the computable aspects of the human mind. The mathematician Hilbert's work gave rise to the "Entscheidungsproblem," or the problem of "decidability." Answering this problem, as the young Turing did, also led to his conceptual blueprint of what we now know as a computer. Nonetheless, the human mind remained Turing's focus, and that's why he's represented in "The Great Philosophers" series. Arguably, his predominant question was "what is thinking?" or, at least, "how does the mind compute?" His answers had far-reaching implications for the philosophy of mind, amongst other disparate fields. Early twentieth century mathematical logic, then seen as "the quest for truth" by eminent philosophers such as Bertrand Russell, was Turing's starting point.

This book's author, Andrew Hodges, also wrote an earlier, much longer, biography called "Alan Turing: The Enigma." Hodges uses this diminutive book to update some of the thoughts presented in that earlier 1983 biography. This 1999 book, a follow-up of sorts, traces Turing's thought from early adulthood to his sad and tragic suicide in 1954. Though some 58 pages long, it feels comprehensive. Apart from "The Turing Machine," "The Universal Machine," "The Turing Test," and his early development, the breezy text covers Turing's travails with homosexuality, his cryptographic feats during World War II, his conception of a discrete state machine, his thoughts on ESP, his brief but somewhat uneventful run-in with Ludwig Wittgenstein in 1937, and reactions to his work by Roger Penrose, a skeptic concerning "mechanical intelligence." Throughout, Hodges refers to Turing as a "natural philosopher" in that he ignored many of the demarcations that still silo academia, such as the distinction between "pure" and "applied" mathematics. Though this attitude led to some of his greatest intellectual feats, it also made him somewhat cryptic to academia. To this day, Turing's work defies solid categorization. Nonetheless, his influence on modern life remains indisputable, though many consider, controversially, von Neumann the "real" inventor of the computer (his EDVAC predates Turing's ACE by one year). In any case, anyone searching for a good overview of Turing's thought and influence will find it here. And although the text sometimes becomes very technical, it thankfully never becomes inaccessible.

Alan Turing met a sad end, as described in this book's final pages. Blackmailed and arrested for then illegal homosexual activity, he took "nature altering" drugs rather than face prison. Thereafter barred from a normal life, he ate an apple laced with cyanide in 1954. The sardonic syllogism he wrote, included in the book, provides a tragic but apt summary for Turing's later life. More than fifty years later, his ideas and influence continue to spread as computers dominate the everyday lives of millions. Artificial Intelligence also considers him a forbearer. This small book exposes not only why Turing was a great philosopher in classic and modern senses, but how he indubitably shaped today's world and culture.

Look, sometimes you just don't want to spend days or weeks of your life getting to know some famous personage in intellectual history. At heart, you're lazy, and you're somewhat cheap too. So what better way to get a brief overview of Alan Turing than by Andrew Hodges' cheap and concise book on said? Well, I couldn't think of any, so I picked this up 53 page gem on a whim. It's a historical overview of Turing's career with balanced attention to his thought. With the exception of about 8 pages that only will profit those who have had some experience with what's called the "Halting Problem" in symbolic logic, this is a very readable book. What is a Turing Machine and why are they important to the modern notion of computers? Why is Turing considered the inventor of computational theory, even if not the outright inventor of the computer? (And this last claim is somewhat debatable, as the book points out.) What was Turing doing for the British Government during the war? Why did Turing get fired from his job? There are all sorts of little tidbits of information here, even about his sex life. Ho ho! Also in the book is some discussion of whether a computer can be made to think. Naturally, some of Turing's more interesting comments are quoted on this topic, and Hodges gives attention to the more recent ideas of Roger Penrose, a philosopher whose ideas on artificial consciousness have been influential on the contemporary scene. Okay, you got the time to read 53 pages, and for not more money than a good McDonald's meal, you could be reading it in a day or so if you'll just click the...ordering button...

This is a superb, yet brief overview of Turing, his life and his math. Although this is a sophisticated approach to the man and his work, the writing is readily accessible by a lay person, like myself. One can get a clear flavor of the importance of his work and how his Turing machine model is not just the framework for Bill Gate's wealth but also as a profound extention of the Undecidable problem first addressed by Godel.

Very good summary of the work of Alan Turing: his influence on mathematics (where he tried to replace the notion of 'provable' by 'computable') and on the development of the computer.
For me, this little book proves that most of Turing's work has been countered by Roger Penrose. For Penrose, the human mind is capable of the uncomputable, while Turing treats the human brain as a computable machine.
The discussion Turing had with Wittgenstein on the 'liar' paradox has been solved by Tarski (see his difficult book 'Logic, Semantics, Metamathematics').
Obviously, Turing did not play in the same league as the one of geniuses like Gödel or Russell.
Also good information on his tragic personal life.

I picked this text for my virtual reality course here at the Electronic Visualization Laboratory and found it to be an excellent, well written, comprehensive introduction to the field.

It is interesting this book, since gives a complete visualization of the current virtual reality. In form didactics it travels all the fields of the VR, not serving alone for a neophyte, also for somebody that the VR knows. Very good book
Hugo Neira S

I received this book shortly after it was published. Since then it has served well as a reference for my students working in my VR research group, as well as being very enlightening for me as well.
I will be teaching a course on VR the next two spring semesters at Valparaiso University, and will be using this text.
The book does a great job of spanning the current VR technology out there, as well as addressing issues for development. I'd recommend it for VR researchers, as well as those teaching VR at the undergrad or grad level.

Understanding Virtual Reality" is the definitive, authoritative, and exhausive exploration of the field by two insiders and practioners, Sherman and Craig. Virtual reality, a uniquely viewer-centric, large field-of-view, dynamic display technology has evolved over the past decade in many physical formats, driven by many software applications using a variety of operating systems, computers, and specialized libraries. Sherman and Craig capture them all in this substantial volume.

Most writing about virtual reality involves summarizing and interpreting interviews and demos, with massive doses of the speculative and the spectacular, and lots of historical fuzziness. Sherman and Craig, however, lived in the world of actual VR production at the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, where corporate researchers, educators, scientists, and artists make use of this technology in their daily work. They have personally suffered with VR tech and benefited greatly from access to it as well as to amazing amounts of computing, engineering, and scientific talent. They were held to real deadlines of corporate contracts, scientific conference demonstrations, and the design of IMAX productions. While they were doing all this, they were also writing this book. As a result, "Understanding Virtual Reality" has the integrity and feel of a long-term, eyewitness account and a personal journal, because these production-oriented researchers were documenting the times contemporaneously, rather than trying to reconstruct the details years later.

I know all this because I was their group leader for a couple of years in the mid-90's at NCSA, and their colleague in VR the years before and after. I co-invented the CAVE hardware, among other things, with Dan Sandin at the University of Illinois at Chicago, in 1991.