Rating: 
Summary: this 'new kind of science' is old indeed
Review: THE FIRST PART OF THE TRAGEDY"I have, alas, studied philosophy,
Jurisprudence and medicine, too,
And, worst of all, theology
With keen endeavor, through and through --
And here I am, for all my lore,
The wretched fool I was before.
Called Master of Arts, and Doctor to boot,
For ten years almost I confute
And up and down, wherever it goes,
I drag my students by the nose --
And see that for all our [new kind of] science and art
We can know nothing. It burns my heart." -Quoted from Goethe's FAUST
(maybe Wolfram should add this tome to his sprawling library)
Rating:
Summary: New title: A re-hashed sort-of-kind-of-a science
Review: It took me less than 5 minutes in a bookstore to understand the main points of this book, and the author's never-ending hubris. Let me save you the time: Cellular automata (or a very simple procedural rules) can generate complex patterns that cannot necessarily be determined to repeat themselves. Oh... and the author thinks this "discovery" should be regarded with the same as one might consider those of Galileo, or Newton (though I prefer Hooke), or Darwin, or Einstein. Well, he may have spent 10 years of his life devoted to it... but the evidence in the book alone is unconvincing.
Rating:
Summary: Long joke with no punchline!!!!
Review: I have long had an interest in Artificial Life and
Artificial intelligence...things that deal with the
very kinds of cellular automata he talks of. However,
while CAs have been useful for some very simple
models and simulations, I had yet to see how they could
be applied to real world problems. I hoped this book
would change that. It didn't. First, its way, way waaaay too long. For the simplicity
of the ideas he is talking about, 300 or fewer pages
would have sufficed. He spends almost as much time
on self-glorification as on actual content. And,
to make it worse, I was able to skip whole chapters
because he was just reiterating(in almost as many words)
what I had read in previous books.At the end of the book
(i couldn't bear to read the notes...they're worse
than reading a math book) you end up wondering where
this whole paradigm shift is. He doesn't make a convincing
case for the "world-as-programs" idea. Also, he seems to make a fatal error that many other
self-important theorists have done in the past...
try to explain basically EVERYTHING with this theory.
While I admit it has some potential for simulations,
we will never get the insight that Einstien and
Heisenberg got, by using computer simulations...we
need math, as much as Wolfram tries to say it's incorrect,
and everything should be programs. Overall, this book presents nothing you couldn't
get by looking at its contents and doing some
Google searches. (I should know, because I have
been reading about CAs, Artifical Life, AI etc
for over a year) I learned NOTHING new here,
its all a rehash of other stuff. Save your money
and or buy another book on CAs (a shorter one);
you'll learn the same thing.
Rating:
Summary: Don't waste even a minute of your life in this; pure[garbage]
Review: Many have written very good and technical opinions on NKS, so I am not going to elaborate on that. One must give credit to Stephen Wolfram in one thing: he is a master of expressing in 1000 words what can be said in 20. Actually an essay of about 2 % the size of the massive NKS would be sufficient to get the point through. The big question is really what is the point Mr. Wolfram is trying to make us believe? That just with simple rules we can explain life and answer all the questions of existence and universal mystery? I don't think so... It is an insult to scientists around the world. But let us assume that Wolfram is the enlightened genius to be only recognised by future generations (who knows he might soon claim that he has come with an answer to the Riemann Hypothesis; or that he was actually the one that was first in resolving the Fermat 350+ years of study, ...whatever), he lacks the humbleness and humility to express his views. And this is what future generations will remember, in nicely digitally conserved undestructible record. Don't buy this it lacks style and has serious technical flaws.
Rating: 
Summary: Assault on the establishment and a fresh perspective
Review: Wofram has the guts to spell it out in simple terms that any kid could understand: we, and the contents of the entire universe, are nothing but enormous collections of trivially interacting particles. His blunt and clear presentation style is a full faced assault on the arrogance of all the religious pseudo-scientist who still think that God created Man, or that the universe is created by a magical force beyond our current understanding. The book's insights into perception, information theory, evolution for example are highly thought-provoking and original, and are likely to anger people who have their egos wrapped up in stale old unclear ideas. He has a big ego, and an repetetive writing style...so what?
To the pervious reviewers: get over it. The clear presentation style is a nice change.
Rating: 
Summary: Little content, poor style
Review: Although this book purports to be a fundamental revision of science and scientific methodology as we know it, it is little more than a study in discrete systems. To begin, I am somewhat impressed by Wolfram's seemingly omniscious ability to relate his findings to many real world phenomena and to continually provide concrete examples, and the evidence gathered from his exhaustive work will probably help to solidly ground many important ideas in 20th century theoretical science. Nevertheless, his book is burdened by his rote, repetitive style, heavily inflated ego, and repeated attempts to take credit for already-established ideas. Wolfram's "discovery" that a system with simple rules can give rise to complex behavior was around even before the Mandelbrot was popularized in the 1970s. His comments on perception, analysis, and computation are part theoretical musing of little significance, part slight extension of the Church-Turing Thesis first formulated in the 1930s and the age-old P vs NP problem in computer science. His continual lambasting of established and successful scientific and mathematical methodologies does little to help his grasps for the fame of being the next Isaac Newton: "...the new kind of science that I have spent so much effort building has seemed an ever more central and critical direction for future intellectual development." (22) The only reason many of Wolfram's results are just now being set in stone is that only recently have computers become powerful enough to make these investigations possible; on page 762 Wolfram notes, "With 3 states and 2 colors there are a total of 2,985,984 possible [Turing] machines [representing] about 33,000 distinct functions..." The reason I give this book 2 stars rather than 1 is that it still has recreational interest; as someone interested in recreational mathematics and computer science, I did enjoy the investigations into discrete systems and the tentative real-world connections Wolfram draws. That is not enough, though, to make this a work of the true, lasting significance the author claims, or to justify its sheer mass.
Rating:
Summary: Simple, but difficult. Volumes more probably coming.
Review: The basic premise of the work is that simple rules inherent to matter are what generate both the complexity and the common structures found throughout the universe. Simple idea, right? On a primitive level, most people can grasp this idea. But what many non-scientists--as well as many scientists--will not / do not want to grasp is that this "simple" idea is not just a repetition of something said before. True, Wolfram is not the inventor of automata (small programs that behave according to a set of rules), and true, other researchers have dabbled in this line of research. The seminal work comes from Zuse, and Fredkin has proposed similar ideas. But Wolfram took the initiative to run an exhaustive investigation into this field on a number of topics. Most of the older literature is either "too" mathematical to be accessible to the layperson, or comes to purely theoretical conclusions which have little bearing on the direction of science. The difference between Wolfram's work and those before him is that 1) Wolfram avoids as much as possible purely mathematical descriptions of his findings, and 2) Wolfram investigates and even solves some popular scientific/logical/philosophical problems. He leaves the heavy reading in the notes of the appendix (and also in his other collected papers). Readers unfamiliar with the relevant literature can first reason about the qualitative claims of Wolfram, and then later investigate the more difficult theory behind them. Readers well-versed in the literature of complexity, network theory, universal machines, and so forth, can review the appendix while reading through the text. Even so, in some parts the appendix is a little weak, so if you are looking for more information, you may want to run some searches on the Web using the terms you see there. A lot of the criticism of this book so far has been rooted in the idea that Wolfram's work is somehow not authentic. I disagree with that claim. Wolfram may have a large ego, but he did do the dirty work to get this research done. He should have noted where his work is confirmed by others, though, and also stated more explicitly what inferences were strictly his and what others were from other scientists. Furthermore, as a service to the reader he should have provided more extensive linkage to other work for people interested in related fields. Hopefully we will see that in the next edition. I think that if a reader is patient and actually reads the entire text and notes, reflecting upon what Wolfram is writing, s/he will come to see the usefulness of this new (comparatively speaking) method for scientists. And, maybe in a grander sense, the patient reader will come to some deeper understanding of time, space, energy, and truth. Critics who think this work is old hat are probably just as egotistical as they claim Wolfram to be. As Fredkin has pointed out, Wolfram is qualified to say what he says (run a Google on Fredkin Hawking Wolfram). I recall only a few books that have challenged me intellectually like this one. I think the reason for this book being such a struggle is not its complexity, but rather its simplicity. When a writer produces something on the level of Smith's Wealth of Nations--something elegant, encompassing, and yet empirically (maybe too much so) sound--my response is to cringe at the possibility that so few ideas can have so many consequences. But then I usually fight with the concepts until I am exhausted, and ultimately agree with the writer. In the case of Smith, we have found that some of his arguments fall apart under the right conditions, but the general premises still live. Similarly, there will probably be chinks in the armor of Wolfram's work, but his ideas will still probably prevail (particularly the Principle of Computational Equivalence) because of their general ability to describe so many phenomena. I do disagree with Wolfram on some points, but not about his central arguments. I think in some cases, Wolfram actually underestimates the power of his scientific framework. Whereas scientists probably see Wolfram as a cowboy (academics often seem to revere citations and politics over the search for fundamental truth), I see not someone disrespectful of the establishment, but an inventor humbled by his own findings. The writing style in this book is informal. I don't mind the writing style, but the overuse of certain phrases in the book is slightly annoying. English buffs, prepare for a fair amount of passive voice. I will recommend this book to anyone, but I also will recommend a few tips for those who take on this thick book. 1) Unlike the press, question the ideas in the book. 2) Unlike the pseudoscientists, analyze the ideas, but don't criticize just for the sake of being cynical or because or because you think you know more than Wolfram. 3) Try applying the ideas on your own set of problems. The simplicity of Wolfram's modeling technique is extremely powerful, even for someone with a limited background in science. 3) Don't go crazy. While Wolfram's work goes a long way to describe everything, the brain is apparently finite, and it will not completely understand the universe. So, even though we conceptually understand that some small set of rules may govern the universe, that doesn't necessarily mean that we can find a theory of everything, although some people have tried. All in all, I was impressed with the book, and it has renewed my interest in physics. That alone has made the purchase well worth the money. In the long run, the book is worth its price because science will probably change as a result of Wolfram. It is wise, I think, to understand Wolfram before everyone jumps on the bandwagon.
Rating:
Summary: A major disappointment
Review: In a nutshell the book's main thesis claims that complex behavior can arise in a system for which each component obeys a simple rule. Unfortunately this thesis embodies nothing new to mathematics and computer science. Any student who takes an introductory "Theory of Computation" course learns this within the first few weeks, whether it be in studying Russell and Whitehead's logical foundations for arithmetic, Thue's string-rewriting systems, Stephen Kleene's generalized recursive functions, or Alan Turing's computational models. For some reason the author seems to believe that the cellular-automaton instantiation of this principle represents new science and merits a 1000+ page book that, outside of some notes at the end of the book, does not provide concrete references or historical perspective. Recursion is at the heart of cellular automata, and is something that is taught to every freshman or sophomore computer-science major. Recursion represents a very powerful idea, since seemingly complex systems can arise by two usually simple definitions: i) the initial conditions of the system and ii) the recursive step which describes the next state of the system in terms of the system's current state. The most common example is the Fibonacci sequence which has been known for over half a millenium: Initial Conditions: f_0 =0, f_1=1,
Recursive Step: f_n = f_n-1 + f_n-2 These two steps yield a sequence of numbers that possess a very rich set of properties still studied today by number theorists. Moreover, asymptotically speaking there is nothing theoretically computationally advantages about cellular automata, in that any computation performed by an m by n cellular-automaton grid can be tractably simulated by any general-purpose computer (otherwise the author wouldn't have been able to perform his experiements and show us those pretty pictures). However, it is true that cellular automata have proven useful in modeling some dynamical systems (see "Dynamical Systems and Cellular Automata" by Demongeot et al.) and, for these applications, that one would achieve a constant-factor computational speedup by using a programmable computer whose architecture followed that of cellular automata rather than a Von-Neumann-style machine, as it would eliminate the need for the Von Neumann machine to simulate the cellular network. But I read this book hoping not to be reintroduced to the ABCs of computing, but rather to possibly have some light shed on how engineers might be able to harness cellular automata for the sake of developing intelligent systems. The current state of the art in terms of distributed intelligence is in the area of artificial neural networks ( a special case of cellular networks) and using them for the sake of pattern recognition. However, success with neural networks has been limited to relatively small-scale networks, given the intractable task of training the network. The task becomes intractable because in the current paradigm it is required that the strength of every neural connection be computed by the engineer/scientist, in hopes of minimizing the average amount of error the network produces when performing the pattern-recognition task. This results in an unwieldy optimization problem in terms of thousands if not millions of variables. Thus success with neural networks has been limited to very well-defined tasks. It is thus an interesting question as to how to make this learning process scale to very-large scale networks. Unfortunately, this fundamental issue does not get addressed anywhere in the book, and surprisingly artificial-intelligence considerations are for the most part absent throughout the book. Finally although the author correctly argues that one cannot mathematically predict the future states of some cellular automata (unlike how one can use calculus to predict the position of the planets at any moment in time), it still may be possible, by making some simplifying assumptions about a network (e.g. by assuming that it is smooth and continuous instead of finite), to prove macroscopic properties that could provide insights into how to harness the complexity of a cellular network. In other words, just as cellular networks can be useful for modeling continuous smooth systems, so it may be that smooth continuous systems may prove useful in uderstanding how to engineer "intelligent" cellular networks. In fact this has been going on for years in the lifelong work of Walden Freeman (see Freeman's "Neurodynamics") who worked with dynamical-systems models in studying mesoscopic brain activity.
Rating:
Summary: A revolution it's not
Review: I'm not a mathematician, physicist, or computer scientist, so I'll leave the technical critiques to others. I am, however, a great fan of the scientific genre popularized by authors such as Stephen J. Gould, Lewis Thomas, and Oliver Sacks, to name but a few. As a writer, Wolfram compares poorly. He is remarkably boring for a self-proclaimed revolutionary. Moreover, I found no fundamental ideas in ANKS that James Gleick didn't describe far more eloquently in "Chaos: Making a New Science" back in 1987. For example, on p.105 of ANKS we learn that "even though their underlying rules are extremely simple, certain cellular automata can nevertheless produce behavior of great complexity .... the phenomenon of complexity is quite universal and quite independent of the details of particular systems." Wolfram repeats similar statements ad nauseum, often preceded by the phrase, "As I discovered..." But take a look at page 304 of "Chaos," where Gleick summarizes the fundamental principles of chaos theory as follows: "Simple systems give rise to complex behavior. Complex systems give rise to simple behavior. And most important, the laws of complexity hold universally, caring not at all for the details of a system's constituent atoms." Clearly Wolfram's "insights" are neither new nor original. His examples of computer-generated snowflakes, leaves, and other natural phenomenon can also be found in Gleick's book, as well as discussion of how computer modeling of complex systems is useful in diverse fields such as economics, climatology, and medicine. Admittedly, Gleick does cite Wolfram in a few footnotes, but only as one of a long list of people whose ideas contributed to the development of chaos theory. That said, ANKS contains many lovely images of computer automata. In fact I am thinking about turning some of them into sweater patterns. Knitters, quilters, and graphic artists will all find much to inspire them. Wolfram's hefty book will also be good for tasks such as pressing flowers, removing warps from damp photos, and flattening out watercolor paintings.
Rating: 
Summary: Everyoe takes sides!
Review: I can't really assign stars: Five would be as meaningful as one. It is hard to know what to say about the book, and many reviews have been negative, some extremely so. Yet for a science book, its amazon sales ranking is very good indeed. The book generated several scholarly reviews in major math and science journals, but the reviewers couldn't agree on what the book is about, so I will not try to characterize it is a one-paragraph review. Wolfram tries to identify a simple idea that unifies major trends in science. Many reviewers say that the idea is not original,--- that it is far from new, and that Wolfram doesn't achieve his goal. Judge for yourself. Parts of the book read well, and other parts tend to rant, making it hard to see what the gist of the argument is. The camps of readers and reviewers are very divided. I would say, 'Why take sides!' To me, the book and the many reviews are fun to read, and they generated a lot of discussion. More than what can be said about most math books. So that was reason enough for me to buy the book. Very few of my colleagues will admit to having bought it, even if asked. Some positive features of Wolfram's book. * Opinionated discussions of points of history of science. * It is rare to get a book from a scientist which is personal, but I don't see what is wrong with it. You don't have to agree with the author. Actually, I don't but I had fun reading several parts of the book. * The book is about ideas rather than formulas... --- To no one's surprise, the two Spotlight Reviews on amazon are flaming, devastating, and perhaps over the top...etc etc. The book itself has a lot more negative reviews than positives, not only on the amazon product page, and it seems to have inspired a cult of haters. I am neutral in this flaming war, but the book does have lots of weak points, I should say. No question about it. Its positives shouldn't get completely buried.
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