Artificial Intelligence Books

In this nearly twenty year old book, the author contends that advancing technology and the force of economic competition will lead inevitably (and in a span of mere decades) to a world in which machine intelligence vastly exceeds human intelligence. In chapters 3 through 6 the author gives a fascinating look at some of the possible features of that transhuman, post-biological world. Those chapters are as interesting and thought-provoking as any that have appeared in more contemporary treatments. Where the book does show it age, however, is in the first three chapters. There the author reviews the history of computer technology, and then succumbs to the shop-worn refrain of many classical AI researchers - "If only we had a computer that is 100 (or 1000 or 10000) times as powerful as today's machine, then we could program a human-equivalent intelligence". He even predicts on page 23 that "a general-purpose robot usable in the home" will be available within ten years. Well, today we have the computer power he was hoping for and still no general-purpose robot. Bottom line: if you want a fascinating look at what a world with superintelligent machines might be like, then buy this book and start reading at chapter 4. If you are interested in how we might actually achieve such a world then consider buying a copy of "On Intelligence" by Jeff Hawkins.

I picked up this book, expecting to learn a little bit about where we're headed with our computers, and the consequences therein. I learned all that--but I got even more than I bargained for. I have to say, the prologue in and of itself blew me away. I had never quite thought of humans as the first step in a bigger evolution. I read this book six months ago, and I haven't been able to get the implications of it out of my head since. If you're looking for the big answers--like "Why are we here?" and "What's the point?"--you may be like me and find more in here than in more traditional spiritual texts.

This book does a great job of exploring the future of robots, artificial intelligence, the human mind, and human identity. A few parts of it seem dated, but most of what the book describes seems likely to happen this century and to surprise the large fraction of the population which still hasn't given any thought to the possibilities this book describes.

This is a hard Hitting Strong AI book. It's the the land mark book the drew the line in the sand. If you wont to know what the strong AI position is this is the only book you have to read.

You wont feel special after reading this book... So much for being on the top of the evolutionary ladder

Moravec writes a good book but I think his ideas are a tad to far out there. He doesn't take into account the possiblility of people not wanting to have their minds transfered to machines. He appears to assume that it is inevitable. I personally agree with his goals but I suspect that the majority of the population would be strongly opposed.

Satisficing as in searching for "good enough" actions rather than the optimal ones is one of the many contributions Herbert Simon is most famous for, in addition to being one of the parents of Artificial Intelligence. Whether the topic is management decision making, economics, traveling, teaching, or computer programs, this Renaissance Man had a life that many or most would envy.

Herbert Simon is a true example to exemplify the benefits of multi-discipline efforts as one subject does not always know the answers. If anyone is interested in building their own latticework of mental models then the life of Mr. Simon is one to emulate. As this is not your average biography, expect to be challenged as the reading may take you to subjects that you are unaware of or have been exposed to. However, this is what makes the experience worth the trip as my many notes and earmarks attest to.

As his life was about scientific discovery to quote, "I have sketched the theory of scientific discovery to which my study of these problems has led me. It is not a theory of global rationality but one of human limited computation in the face of complexity". Yes, we live in an evermore complex world and I am glade I have some of Mr. Simon's mental models to guide me through it.

If you are a graduate student of management, this is must for you. Simon is an icon in the field of decision-making. Much of his work has inspired contiuned research in the field of decision-making. An extraordinary man with extraordinary vision.

The late Herbert Simon was a veritable renaissance man. His autobiography, "Models of My Life," discusses the single thread that underlined all of his intellectual conquests in artificial intelligence, sociology, cognitive science, psychology and economics. This one thread, animated by philosophical positivism and ripe scientific thirst, was his deep obsession with modeling and researching decision-theoretic behavior.

It's interesting to note that even though decision theory (how intelligent agents percieve and act upon choices amid various modalities) serves as the impetus for Simons work, he uses "Models" instead of "Model" in the book's title. This is no accident. For you see, beautifully fitting of his memoir, this book delves into how Simon's one passion was his "heuristic" in choosing which of many paths he could have taken througout his life. The upshot: Simon's own life emulated the heuristic search (in AI) that he helped invent! Consequently, this lead him all over the globe, from Wisconsin to UChicago to Berkeley to Carnegie Mellon to China.

This book is also about the times of Simon: the positivistic turn in social sciences, the scientific fermet of the 1950's, the cultural tumult of the 60's, the death of behaviorism and the rise of cognitivism -- all along, peppered with intrigue of the politics of academia. Although the writing can get quite dry at times, his book is highly recommended.

This intellectual autobiography is not just a chronology of the particulars of a great intellectual life. It is a wonderful opportunity to obtain a coherent overview of the views and contributions of one of the twentieth century's great thinkers. Simon - political scientist with his Ph.D. from University of Chicago, founder of the new and emerging area of artificial intelligence, cognitive psychologist, Cargegie-Mellon faculty member, Nobel laureate for economics (1978), and contributing philosopher of science - was no dilitante. This book, which is written is a very accessible style, reveals the integrity and evolution of his thinking, which can be extracted only with relative difficulty from his large and diverse literary corpus. It is also an inspiring book for any person with an active intellectual life in science or philosophy.

Thomas J. Hickey, www.philsci.com

Herbert Simon's research contributes to human knowledge in many different areas, including economics, artificial intelligence, cognitive psychology, and organizational behavior. In each of the mentioned areas, his contributions are ranked among the most important and influential that even a scientist who focuses solely in one area finds hard to achieve. The testimony is the top awards that the community in each discipline bestowed upon him--the Nobel prize is only one of which.

The secret of this interdisciplinary success is that he is, in his own word, a "monomaniac", studying only one thing--human decision process--for fifty years. The field of his own choosing is not bounded by usual academic disciplines, however, and he did study it from many different aspects, from the levels of individual cognition to organizational decisions, using tools as varied as mathematics, computer simulations, and human subjects.

This book detailed his own account of the various aspects of his life, personal and professional, in a sincere and direct prose. From the childhood that undoubtedly helped set the tone for his later accomplishments, the way he managed and nurtured new academic thoughts that later grown into full-fledged disciplines (artificial intelligence, cognitive science, and, less prominently, bounded rationality), to the philosophy of working and living including brief exposures to familial life, we can learn tremendously from hise xperience, decisions, and actions.

How could he achieve as much as he did? We can glean several lessons from his stories. He collaborated extensively. He learned a great deal from the outstanding individuals he respected. He had a love for truth and rigor in reasoning. An empiricist who firmly believed that any valid theory must be based on empirical facts, he did not hesitate to fight against widely held beliefs conflicting with facts. His work on bounded rationality which helped earn him the Nobel Prize is an outstanding case which his stubborn, and valid, arguments against mainstream theories brought a valuable alternative viewpoint to the world. Strong passion and the ability to break out of the mold and stand tall under storms are important characteristics exemplified by many past giants, including Galileo, Columbus, and Einstein.

Not just a normal autobiography, but the story of a distinguished life we all can learn from.

This is an OK book for a intro level tutorial. From reading some of the other reviews here I bought it thinking it would be what I needed. If you want indepth information on using Robo Help this book is not it. All functions are not covered just basic ones are.

I've been a Technical Writer for over a dozen years, and have spent over a decade writing on-line help for various systens, and I can honestly say that this is the one book that you need to buy if you are going to use RoboHelp as your Help Authoring Tool (HAT) for the World Wide Web.

RoboHelp (RH) has dominated the WinHelp Authoring field for many years, and is extending its expertise into the area of web-based help. Those who have used RH before need to learn some new concepts, and those writing help for the first time are well advised to find a native guide to help with the unfamiliar territory.

Fortunately, John Hedtke and Brenda Huettener are skilled and expert guides, providing the information that users need when they need it (and sometimes before they know they need it; the chapter on "Creating a Documentation Plan" is a must-read for anyone who wants to reduce the number of headaches they might encounter. It's not JoAnne Hackos' _Managing Your Documentation Projects_, but they do give you the information you need to get the work done without feeling stupid or overwhelmed.

This is a book that you keep close at hand to answer the "how do I do ____?" questions that always come up when creating a documentation set. The clear and easily followed explanations of concepts and techniques are well-illustrated with screenshots, and there is just enough theory for users to understand why things have to happen in a particular way.

As I said at the top, if you are going to use RoboHelp as your Help Authoring tool for the Web, this is the book to get. Even if you are not going to be using RH, the book is worth getting for the Document Development and Help Logic material it contains.

John Hedtke and Brenda Huettner are knowledgeable professionals in the online Help field, and RoboHelp for the Web really showcases their knowledge. I'd recommend this book to any online Help developer. Newbies will benefit from the simple explanations and step-by-step procedures in the book. More experienced technical writers will find themselves learning quite a bit from latter chapters.

RoboHelp for the Web is a fantasically helpful book if you want to build an online help system. Both the authors are pros, and the book is written in an informative and easy-to-comprehend style. The book has information valuable to beginners and veterans alike. I really liked the clear explanations for adding images and special effects, not to mention templates, skins, context-sensitive help, and how to work with Microsoft HTML Help Windows and WebHelp Enterprise Windows. I?ve been following the writing of John Hedtke for many years because of his clear prose and concise explanations. He and Brenda Huettner have written an invaluable guide. This is a definite MUST HAVE for anyone working with online help systems.

As a former instructor on how to create and manage help projects as well as a help developer with over a decade of experience in the trenches, I can unequivocally state that "RoboHelp for the Web" is an invaluable resource for anyone involved in help development: beginning developers, seasoned developers, developers returning to the fold after forays into other technologies, managers, and consultants.

Of course I expected complete how-to information, and the book did not let me down. But there's so much more.

Beginners will welcome explanations of the advantages, disadvantages, and most likely uses for the various types of help.

Seasoned veterans will appreciate the extensive sidebars and tips on how to handle technology issues associated with new server-based features.

"Returning" developers will find it easy to discover what's new and/or different.

Managers and consultants will embrace the "Advantages to Using Online Help" section - it's great source material for selling executives on the idea of moving from printed to online doc.

And the section on "Creating a Documentation Plan" is a must-read for anyone who needs to "sell" any documentation project to management or clients.

People aren't computers. Human beings live in a real world of scarcity and constraint. Even though time and information may be scarce, human beings must make high-stakes decisions. Probability and logic offer models for the thought process of choosing between alternatives, but decision makers often do not have enough hours, data and skill to use these sophisticated approaches. Fortunately, some rough and ready cognitive shortcuts perform as well as or better than the most elaborately sophisticated models - at least in the real world context of limited information and time. Working with the ABC Research Group, authors Gerd Gigerenzer and Peter M. Todd explore some of those shortcuts, called "heuristics." They discuss in length and depth a series of experiments that demonstrate the value of heuristics. This is not light reading. It requires a level of comfort with academic style, mathematics and symbolic logic. Readers unfamiliar with cognition literature may find it a struggle - but we believes that those who persevere will find enough new insight to make the effort worthwhile.

Gerd Gigerenzer is probably THE guru in heuristics - the art of picking a few shorthand rules to help us make complex decisions - but I found other works of his (Adaptive Thinking, Bounded Rationality) pretty dry going. By contrast this volume - a collection of stimulating, sometimes provocative essays by members of the ABC Research group (the centrer for Adaptive Behavior and Cognition) explores many facets of Heuristics and even puts heuristics to the test against other more computationally complex techniques to pick the outcomes for several complicated problems. Time and again, the simple rules of thumb out-perform the grindingly thorough statistical routines. For anyone researching human behaviour, the implicit challenge is to stop asking huge batteries of questions - and to look instead for the little telltale rules by which people navigate their complicated environments.

I'd say that if you were just starting to look at heuristics, then this this volume of essays would be a good starting point. Even if you skip the super-technical pieces, there's plenty of thought provoking material written in a lively, unexpectedly "human" style.

It's really meant for a technical audience since this stuff is so cutting edge, but you shouldn't wait till the results appear in Time magazine. The experiments and writing are very easy to understand, very clear. And you will be amazed by the simple ways in which our brain takes shortcuts in reasoning -- both making it stupid and making it smart. Be careful next time you try to reason using probabilities, you're better off using frequency.

My own background is in philosophy, where this type of work has been very important in undermining the assumption that humans are rational. We aren't. You should probably read Kahnemann and Tversky's books before coming to this though, since this work adds an interesting spin to the old irrationality debate: maybe some of it is GOOD for us!

Whether you like a book depends on what information you're looking for. i make computer models of human behavior so this book, which is easy to read but filled with concrete solutions and lots of supporting dat, was near-perfect for me

As a note, i'm picky when it comes both to writing and thinking. And i hate most books written by academics. Even the ones with good information (eg, Fodor's Modularity) are hard to read and filled with confusing, field-specific words. Not this book. It's really well written. Written in plain English, very few assumptions, very thorough analysis, lots of self-criticism, lots and lots of data (OK, that part is boring and can be skipped, but it's comforting to know it's there)

What's it about? Common AI, psych and economic decision and learning algorithms (decision trees, neural nets, Bayes, multiple linear regression, etc.) are compared to several absurdly simple algorithms the authors believe real humans use. The various approaches are compared and evaluated on the basis of performance, accuracy on training data, accuracy on test data (generalization) and amount of input data required. Tests are on the standard UC Irvine data learning test sets. Comparisions, outcome explanations and relevance to the human mind and the real world are provided. Explanations and analysises are easy to understand and pretty convincing

i've decided to use a lot of what was in this book in my software, things that have made my agents more natural and easier to implement. i absolutely love this book

As someone interested in the practice and theory of decision making, I came upon this book via a number of "listmania" lists that reccomend it. The first few chapters got me excited about the subject matter. The authors promised to present a new model for decision making, one that was simple, and one that works.

The ensuing pages compare several theoretical models, such as Multiple Linear Regression and Dawes Rule to their own Take the First and Take the Best models.

Most of the tests were simulated on a computer. You would feed each decision making model into the computer, and then feed in various data for it to make decisions on. One popular test is "Which is the most populated German City." The computer had data on various German Cities with populations over 100,000. It also had several indicators, such as whether it has a soccer team, or a rail system, or is a state capital. The system would present two cities, with the indicators, and the decision making model would figure out which was the most populous one.

Right now I'm in a chapter called "Bayesian Benchmarks for Fast and Frugal Heuristics." It's about halfway through the book, and I'm not sure I'll finish. While the second half sounds interesting, this book is highly academic and the authors are concerned with presenting proofs for everything they say, in detail. Sort of like a victorian novel that starts of by telling you what it's going to tell you, and then tells you several times. I may skim it because I do find the subject matter intereting.

I certainly don't regret buying this book, having mathematical models for decision making is certainly handy (as someone interested in AI), but I wouldn't call it light reading, nor would I reccomend it to a manager interested in the decision making process.

I found much more interesting "Sources of Power" by Gary Klein. Indeed, I consider Sources of Power to be one of the most informative and most entertaining books I've ever read, and wish more like it existed.

In summation, I found this book to be highly academic and theoretical. If you are a human being interested in the decision making process as it is carried out by humans, I reccomend the more hands-on Sources of Power by Gary Klein. If you are interested in simple, statistical models for decision making (the kind you can teach a computer), then pick up this book.

In my opinion, there are two types of data
mining books.

The first type such as by Hand et' al, Han,
Witten etc focus on the techniques.

The second type which this book falls into
focuses on how to apply the techniques.
I like this book more than other books
of the same type such as the one by
Herb Edelstein because it has a detailed
case study that is built upon throughout
the book.

This book is a good example of how to apply
data mining. It is obvious the authors have
done data mining in industry, otherwise they
wouldn't have a section in the book on:
"Mapping Business Questions To Data Mining
Tasks".

Highly recommended.

I believe Stephen Eick, Cheif Technology Officer of Visual Insights best put into words in the Advance Praise section of the book that "This book is a wonderful contribution and important resource for anyone building visual data mining systems. It combines down-to-earth, practical advice with thoughtful examples."

In addition, Michael Berry of Data Miners, Inc states "As this book shows, visualization plays an important role in every step of the data mining process. Soukup and Davidson take the reader through every detail of this process, providing sample SQL code for each practical example. In fact, much of their advice on project planning and data extract, transformation and cleaning is applicable to all data mining projects, visual or not."

I found the eight step VDM methodology applicable to data mining my own data. Highly recommended.

Most data mining books focus on the algorithms.

This book takes a different tack. It discusses
using the algorithms and visualization within a data mining
project. Alot of the book focuses on the "darker
side" of data mining: data preparation, model
performance and deploying your model once it is
built and tested. There are two chapters on
algorithms but they mainly focus on how to visualize
the model, its performance, expected vs actual
performance.

The book is well written and easy to follow. The
highly detailed retention case study is a nice addition.
One small critisim is that the authors get a little
to much on a soap box when discussing how to justify
to management a data mining project.

The examples are terrific, including those in color---and the business-oriented examples and cases are practical and detailed. The author also does a good job of covering tough areas, like verifying accuracy of visualizations, and selecting the correct data sets for analysis.

I've been doing data mining for years, but have only recently begun working with a visual tool (better left unnamed), that I found frustrating to use. This book has been really helpful in giving me the lay of the land on visual mining techniques and tools, and insight into the right kind of tool for the work I do. Thanks for helping me get on the right track!

This book a a great starting point for studying Artificial Intelligence. For those with a Computer Science background, the book is a quick read that will show how theories such as data structures and search algorithms apply to the different areas of AI. For those without a background in computers, the book will take longer to read and for deeper understanding of some subjects other texts may need to be consulted. However, it is still one of the easiest-to-understand books on AI as most are extemely lengthy and detailed beyond the scope of what most beginners are able to understand.
The book is well written and explains complicated topics in plain English. Figures are used effectively to explain certain concepts. An extremely helpful feature is that every chapter is summarized and further references on that topic are given with a short description of the strength and weaknesses of each reference.
I would definitely recommend this book to those who want to learn about AI. Its a great starting point that can lead you in the right direction if you want to study a particular topic in further detail.

This is a wonderfully compact introduction to the basic concepts of Artifical Intelligence. You probably aren't going to be able to go and write your own AI after reading this but at least you'll have enough background to read a more detailed text and some of the scientific literature out there. If you've picked up other AI books and felt lost then start here, you won't regret it.

This book is a fine introductory text on AI. It covers all major subjects in the field and it is very clear and elaborates on the problems in a very direct and simple manner. If you are looking for an introductory text, then you found it by now.

This is a very readable introductory text. Its coverage of topics is surprisingly good for such a slender volume. I especially liked the chapter on searching--the examples are very clear.

This book is a fine introductory text on AI. It covers all major subjects in the field and it is very clear and elaborates on the problems in a very direct and simple manner. A very fine book as an introductory text.

Provided good basic understanding of AI with example program, CLIPS, enclosed( floppy disk), well written with understandable examples

An excellent text for a first course in Expert systems.

The authors give a good cursory background on knowledge representation within AI. Chapters 7 - 12, present CLIPS syntax and usage. The information in these chapters helped me to develop a simulation involving four intelligent agents, colloborating via a blackboard. The book provides no details with respect to CLIPS object-oriented capabilities (COOL), but this is something that can be learned through CLIP's accompanying documentation. I highly recommend this book to anyone wanting to know more about expert systems or CLIPS.

This book gives a reasonable, detailed and seriuous account on expert system. A detailed presentation of the CLIPS expert-system language, along with the interpreter, is provided.

Excellent for starting work on expert systems as part of integrated software packages.

The 4th edition has remarkable changes compared to the 3rd edition, which was written in 1990. Almost all the references are highly up-to-date, new trends in the AI field and new applications leveraging expert systems are introduced all through this book. The content ranges from an overview to the technology of Expert Systems, the basic scientific foundation of Expert Systems and the CLIPS tool for implementing expert systems. There are two chapters about reasoning under uncertainty and inexact reasoning, which are the ways the AI systems are heading. Things like semantic web, its advantage and problems are very well sorted out in the first chapter. The authors put a good deal of humour into the text all through this book, the formal logic stuff is not that boring as it usually appears in other book. I wish I had had this book as my first book for formal logic/deduction systems. The mathematical mechanisms for things like resolution/deduction are explained with plenty of well-arranged diagrams and tables to help readers understand better. Every chapter comes with well-designed assignments. An excellent text book for introductory AI courses (I believe a motivated high-school kid will have no problem to understand the book since it is so well written). And also a good reference for those who need to look up basic concepts while engineering expert systems. I'm constructing an expert system at the moment, got this book 2 weeks ago and it's already dog-eared on my desk. For its targeted audience, this book is truly the best-of-breed. Highly recommended.

I am quite satisified with what I am reading. As a non-scientist, I can see how this evolutionary theory and this particular theory relates to life in general and its applications and conditions. The introduction is quite important and explains how the theory of evolution (correct or not is not necessary) is used to help with mathematical computations and solve mathematical problems.

What is implied in Christian Jacob's book is quite broad and revolutionary. In color plate 2, page 288, "independent populations climbing peaks" reminds me of the recent findings of a particular species of parrot (thought extinct in South America since 1910)living currently on some Volcano in South America by residents there by accident. Yet, through evolutionary models, one can track species and perhaps any existence.

What is most interesting is what is implied--that tracking can be used to oppress or cause extinctions (such as poaching) and so it could be implied that randomness would occur by species under these conditions to avoid tracking such as avoiding evolutionary tracking. Randomness would therefore be an evolutionary process to survive. Finding randomness could suggest a species or whatever is possibly trying to avoid being tracked through evolutionary tracking. Evolutionary tracking is relatively new and so this is why there is perhaps little randomness (or maybe none has been discovered yet), but randomness is a possibility that seems to be a probable result and not clearly an original play--since so far, not much randomness if any has been found.

If one turns Color Plate 3 on page 289 upside down, one is perhaps presented with an ocean. Recently, life that does not need oxygen to exist has been found living deep in an ocean. The life was most likely dicovered without using evolutionary tracking models, though the models would suggest something is there though what has evolved or not is not clear and would need more elaborate formulas and charts of various sorts. That ocean life found uses other sources as reported by this science news wire:
[URL]
So, Color Plate 3 is quite interesting as it would suggest that in addition to the findings of the scientists, there are at least two other entities there they have not discovered...

It is as though the models and formulas can be applied in a variety of instances due to lack of randomness and that evolutionary tracking has so far not been avoided with randomness...

This suggests early birth of Earth and evolution of all or most species and things on Earth, evolutionary tracking not done until recently by Earthlings or others, or that evolutionary tracking so far is used for good and not to cause extinction or disruption...

I would say the book is written for scientists mainly and there are a number of formulas, but they do apply to the humanities.

Like the previous reader, I must applaud this book. While I know Mathematica well, I am no mathematician and I have no patience for books that do not explain the foundations of their programs or functions.

The author here, Jacob, does an excellent job of introducing the reader gradually to the different concepts of simulating evolution. As you can download the Mathematica notebooks and run them on your own computer, this quickly becomes a fun and interactive book.

The book starts with simple selection processes for reproduction. Select shapes, colors or features and see a next generation evolve! This can be a fun game. See breeding and mutation be used to search for an optimum of a three-dimensional function, where the reader knows the global optimum, while different "populations" try to find it by evolutionary methods-mutating or breeding to a different spot, which they evaluate and according to its height be successful in the passing of their genes or not. Other fun chapters include evolutionary production of mobiles and flowers. The culmination is in the evolution of algorithms. This evolves small programs for searching for food in a maze. The successful programs "breed," "mutate," and reproduce, while the unsuccessful ones starve and die. The result is a complex path toward better algorithms for searching for food.

Part of the value of this book for me is that it really shows the limits of evolutionary analysis. You can simulate the successes--the butterflies that do manage to change colors to avoid falling easy prey when the environment changes; the evolutionary mechanisms that find the global optimum of a function-but there is no concrete way to determine or describe their efficiency ex ante. This is a major failure of evolutionary analysis generally, rather than a drawback of the book. If anything, the book deserves credit for making this failure understandable, although Jacob does not spend time exploring or solving the problem of determining evolutionary fitness.

[To put it in an example, suppose there are two evolutionary mechanisms. An organism can evolve by mutation or by reproduction. Mutation is the random change of some individuals in the population, and the change makes them either more or less successful in their environment. Reproduction means parents producing an offspring by mixing their features, and the different offspring will have different degrees of success in their environment. We can simulate their operation in a hypothetical environment, by for example, saying that the background foliage changes color and organisms have different probabilities of being eaten by predators depending on their color. We run the simulation and see which evolutionary mechanism adapts to the new environment faster and better. Nevertheless, we cannot conclude that the evolutionary mechanism that won this test will win every test. Needless to say, when designing evolutionary systems this conclusion is crucially necessary. If we are designing a computer search program, should we have it "mutate" or "reproduce"? Since we do not know the challenges it will face (the changes in the environment that it must overcome) we cannot evaluate its success ex ante.]

With the caveat of not exploring measurements of the success (fitness) of different evolutionary mechanisms, this is a spectacular book. It is worth comparing it with the books of the various biologists, who simply offer examples of evolutionary changes from the past or hypotheses of evolutionary explanations for various phenomena. Those are speculations of amateurs compared to the experimentation and verification that Jacob's approach offers. That the field is not ready for rigorous conclusions is unfortunate, but something that is no fault of this author.

This book provides a thorough survey of evolutionary computation techniques, including genetic algorithms, genetic programming, evolutionary programming, and evolution strategies. The author uses mathematica to illustrate the examples. If you know mathematica, you'll find this unique angle to be invaluable, but even if you don't know mathematica, if you're familiar with any programming languages, or matlab, maple, etc., you should be able to make the connections. The figures in this book have to be the most illustrative examples offered in any evolutionary computation text to date. The text is easy to read and very informative.

This book is poorly structured and lacks depth. It trivialises aspects of EAs, especially representational issues. I understand the emphasis is on teaching the reader how to program GAs using mathematica but I would not recommend this book as an introduction to GAs. The visualisation aspects of Mathematica applied to EAs are useful, but I would definitely recommend Melanie Mitchell's Introduction to Genetic Algorithms or one of Koza's books for those interested in GP.

As a Mechanical Engineer with just a side interest in AI, I find most books in this field need heavy attention after the first few pages. Not so with this one. Its a great piece of work. I was kind of skeptical at first since this is billed as a translation from German, but it reads really well... and once you get the notebooks in Mathematica...its almost imposible to get anything else done, youll be hooked. Looking forward to more titles in this field from Jacob.

There are certainly a few nuggets to be extracted from this volume but it is not a very compelling read. The MIT slant is obvious due to the multiple inclusions of Peter Senge. Yes there is a reprint of the seminal Balanced Scorecard article from the HBR included in this compilation but I really considered most of the papers included in this collection to be extremely uninteresting. Many of the articles provide nothing other than a state of affairs for knowledge management and while they are well researched they are totally dated. Anyone who has read a relatively recent book on the subject of KM will be familiar with the content contained within this volume. Furthermore many of these articles can be found free of charge on the internet as they were published far and wide at their inception. Sure it touches on the major components of knowledge mangement but in my opinion I found the case work to be so general that any *term of the moment* could be substituted for knowledge management. Spend your cash elsewhere.

I am the editor for this book and I thought it would be helpful to include an overview of the target audience and highlights of the included works in the collection.

This collection is a targetted at leaders in government, industry, or academia who are interested in starting or evaluating a knowledge management program, are currently implementing a knowledge management program, or are simply interested in expanding their understanding of knowledge management.

Featured works include:

Introduction by Margaret Wheatley on, "Can Knowledge Management Succeed Where Other Efforts Have Failed?"

A reflection by Peter Senge on what has been learned since his seminal, "The Leader's New Work: Building Learning Organizations"

Dr. David J. Skyrme on "Developing a Knowledge Strategy: From Management to Leadership"

An introduction by Bipin Junnarkar, CKO of Gateway, on "Sharing and Building Context"

A reflection by Hirotaka Takeuchi and Ikujiro Nonaka on what has been learned since their seminal work, "The Knowledge Creating Company"

Dorothy Leonard on "Tacit Knowledge, Unarticulated Needs and Empathic Design in New Product Development"

Dr. Karl-Erik Sveiby on "Measuring Intangibles and Intellectual Capital"

Dr. Nick Bontis on "Managing Organizational Knowledge by Diagnosing Intellectual Capital"

This book offers a learning-centered introduction to the field of knowledge management. Each of the three sections (Strategy, Process, Metrics) sets the tone with an opening essay by a well known authority in the field. Several previously unpublished essays that develop the chapter follow each opening piece. This convenient plan makes it possible for time-pressed readers to get the gist of the matter by reading only three or four essays in the area that most concerns them. It also allows readers with a consuming interest in the subject to get all of the details they could possibly desire. Some of the essays are accessible; some are quite heavy going, laden with jargon and dense academic prose that only a specialist could decipher. Thus, we are grateful that the editors have made it so easy for readers to find what they need to know in this well-organized, thorough study of the field of knowledge management.

At the start of each episode of the mysterious, brain-twisting 1960s spy/science fiction series, The Prisoner, Patrick McGoohan would declare: "I will not be pushed, filed, stamped, indexed, briefed, debriefed, or numbered!" This could well be the rallying cry for the perspective on knowledge management taken by the contributors to this 451-page volume. The 18 pieces are gathered into three groups covering strategy, process, and metrics. Although the volume can certainly serve well as a general introduction to knowledge management, the editors make no bones about their distinctly learning-centric (as distinct from information-centric) perspective that they take.

The information-centric approach, which has been dominant in the field until recently (and still is among consultants with IT systems to sell), emphasizes knowledge as explicit, and as susceptible of being captured, stored, and processed. The contributors to this book instead emphasize the continuous generation, acquisition and application of knowledge in its human and cultural context. This perspective permeates each of the essays and all three of the sections. Those sections begin with a classic work then move onto more contemporary thinking along compatible lines.

The "Strategy" section, which begins with two pieces by Peter Senge, examines the motivation for knowledge management and explores how to structure a knowledge management program. Takeuchi and Nonaka's classic paper, "Theory of Organizational Knowledge Creation" opens the "Process" section, which looks at how managers can implement knowledge management effectively, applying it to help make existing practices more effective and to speed up organizational learning. The final section on Metrics covers the use of the Balanced Scorecard, the measurement of intangibles, and metrics for knowledge sharing.

Busy executives need not be deterred by the length of this book. They can read the opening classic pieces, then look only at those following pieces with the most relevance to their concerns and circumstances. Margaret Wheatley's introduction, "Can Knowledge Management Succeed Where Other Efforts Have Failed?", is well worth reading for her concise and lucid account of the common beliefs in organizations that have caused problems for KM. These include beliefs that organizations are machines, only material things are real, that only numbers are real, that you can only manage what you can measure, and that technology is the savior.

I am the editor for this book and I thought it would be helpful to include an overview of the target audience and highlights of the included works in the collection.

This collection is a targetted at leaders in government, industry, or academia who are interested in starting or evaluating a knowledge management program, are currently implementing a knowledge management program, or are simply interested in expanding their understanding of knowledge management.

Featured works include:

Introduction by Margaret Wheatley on, "Can Knowledge Management Succeed Where Other Efforts Have Failed?"

A reflection by Peter Senge on what has been learned since his seminal, "The Leader's New Work: Building Learning Organizations"

Dr. David J. Skyrme on "Developing a Knowledge Strategy: From Management to Leadership"

An introduction by Bipin Junnarkar, CKO of Gateway, on "Sharing and Building Context"

A reflection by Hirotaka Takeuchi and Ikujiro Nonaka on what has been learned since their seminal work, "The Knowledge Creating Company"

Dorothy Leonard on "Tacit Knowledge, Unarticulated Needs and Empathic Design in New Product Development"

Dr. Karl-Erik Sveiby on "Measuring Intangibles and Intellectual Capital"

Dr. Nick Bontis on "Managing Organizational Knowledge by Diagnosing Intellectual Capital"

Genius SF writer Rudy Rucker's new book is fantastic, and just in time, too! He writes, among other things, that we are
presently in the midst of a third global intellectual revolution. The
first came with Isaac Newton: the planets obey physical laws. The second
came with Charles Darwin: biology obeys genetic laws. In today's third
revolution, says Rucker, we are coming to realize that even minds and societies
emerge from interacting laws that can be regarded as computations.
Everything is a computation. Cool!

Does this, then, mean that the world is dull? Far from it. The
naturally-occurring computations that surround us are richly complex.
For example, a tree's growth, the changes in the weather, the flow of daily news, a
person's ever-changing moods --- all of these computations share the
crucial property of being gnarly. Although lawlike and deterministic,
gnarly computations are --- and this is a key point --- inherently
unpredictable. The world's mystery is preserved.

Mixing together anecdotes, graphics, and fables, Rucker teases out the
implications of his new worldview, which he calls "universal
automatism." His analysis reveals startling aspects of the everyday
world, touching upon such topics as chaos, the Internet, fame, free
will, and the pursuit of happiness. More than a popular science book,
this book is a philosophical
entertainment that teaches us how to enjoy our daily lives to the
fullest possible extent.

This book is Rudy Rucker's latest mind-child, and incorporates all the many threads of his very active and acute intellect. For those who have read some of his earlier books on math (esp. the superb Infinity and the Mind), this latest work builds on the vital importance of approaching the cosmos from the perspective of computability. Rucker's thinking on this is precise and playful, a rare and valuable combination. He extends the ideas toward applications of how to view your own "lifebox," and offers great suggestions for booting up an expanded appreciation of your reality. Highly recommended.

This is one of the most fascinating books I have ever read. Rudy Rucker is an accomplished science fiction author and popularizer of mathematics and computer science. In this book he seems to bring together everything he has written in the past while playing with constructing a coherent world-view and philosophy of life. It works quite well. (Rucker is such a fascinating writer that my son was going to apply to San Jose State just to take classes from him. The book's revelation that Rucker is retiring disappointed my college-bound son and left him scrambling for other schools.)

Any description of this book with less complexity than the book itself will do the book an injustice. If you're a fan of Rudy Rucker, of infinity, or of mathematical and speculative philosophy, you MUST read this book. Students of the social sciences may have some difficulty wrapping their minds around the computational science ideas, but this book is an essential part of understanding what it means to be human.

Rucker has structured the book well. Each chapter is prefaced with a piece of microfiction that illustrates the concepts to come. The chapters begin with an annotated outline that relates the concepts discussed. Ideas are reconnected with earlier mentions in the book as well as preceding ideas.

Rucker is not afraid to make novel combinations of philosophy, psychology, math, computer science, quantum physics, science fiction, and personal anectdotes. This is one of the best books produced for handling notes well. Turning to the back of the book for a note is generally rewarded with insights or speculations related to the text. Only occasionally is a note simply a bibliographic or web reference.

The book itself is a gnarly computation as well as a gnarly program for gnarled minds. It should be required reading for everybody who things they have a grip on life, the universe, or anything. If I could give it more than 5 stars, I would.

I found Rudy Rucker on a road trip. Or at least, I found part of his lifebox. If you haven't yet read this book, you probably don't know that the lifebox is a fictional invention into which a person speaks, and eventually it gets to know him well enough to tell his stories, and perform more menial conversational duties. That was Wetware, the first of his books that I read. Since then, I've read everything of his I could get my hands on, and I anxiously awaited the arrival of my pre-ordered copy of The Lifebox, the Seashell, and the Soul.

I had a bad experience with submitting a snarky review about A New Kind of Science after I'd only read 200 pages, so I decided to actually read this whole book before trying to draw any conclusions. I believe that is something the crappy reviewer from Publishers Weekly just didn't do. My conclusions after the first read:

1. This is the most phenomenal, approachable, and thorough introduction (certainly leaves Fredkin and Wolfram in the dust for approachability) to cellular automata and computation that I've ever met.
2. This book, true to its title, has soul. It's wacky, interesting, fun, deep, and self-critical of the so-called "Universal Automatist" philosophy.
3. The illustrations, stories, personal anecdotes, and tables (yes, he loves his tables) are what makes the book work- it would have been possible to write this book (and probably to read it) without them all, but it would have been less fun, less interesting, and less illuminating.
4. Rucker obviously spent a tremendous amount of time in actual experimentation- doing it himself. He articulates a better "feel" for the field than anything else I've read.

I'm sending this book to my dad and my brothers for Christmas. I got them all A New Kind of Science year before last, but none of them got past the first chapter. I can't wait to hear what they think of this one!

As a disiple of the Stephen Wolfram's universal automaton paradigm this book does a good job of looking at the different areas of science to see how they work with such a view. Taking on issues like free-will, quantum mechanics, and psychology he attempts to demonstrate there compatibility with his thesis that the world consists of computations. I recommend this book to anyone how has interests in determinism, aritificial intelligence, and/or Wolframs "new science" as this book has something of value to offer on all this areas.