Artificial Life Books

1. P2 robot was developed over 10 years at a cost of $100 million. Its successor is Asimo with the ability to walk, run, turn, greet, and deliver a coffee tray.

2. The Marsokhod rover has six wheels on movable axles allow it to climb over rocks 1.5 times the height of its deeply ridged conically shaped wheels.

3. In the USA, Sandia National Laboratories has a hopper in a plastic shell the size of a grapefruit. Using a built-in compass and a gimbal mechanism with a moveable weight, it cal roll around to right itself after each jump. A small internal combustion engine with enough fuel for about 4,000 hops drives a piston into the ground, generating a leap three fee and six feet forward.

4. The Nagoya brachiator has 14 motors controlling a fully articulated body. A separate stereo-canera setup connected to a computer determines where the brachiator's arms are, updated 60 times per second. Using basic equations for swinging and knowledge of distance between handholds the Brachiators is able to swing between branches.

5. Alan DiPietro of iRobot has created robot gecko feet allowing the robot to walk up a wall. The German MAKRO Project of 1997-2000 developed a multisegment robot to inspect the interior of sewerage pipes. The snake-like robot could travel down the pipe autonomously and was seen as a cheaper and much more effective way of carrying out inspections. Shigeo Hirose built a simple snakebot with serpentine motion by placing wheels under each modular section. Snakebot II developed by Mark Yim incorporated some autonomous behavior.

6. David Barret, in 1995, built a robot tuna. Controlled by six servo motors each rated at 2 horsepower, it had force sensors at various locations along the path of its controlling tendons. Mitsubishi Heavy Industries built a robotic sea bream, in which the tail fin and two pectoral fins and controlled by desktop computer giving the robotic fish a top speed of 0.82 feet per second.

7. iRobot is interested in creating a legged robot that can scuttle along the bed of a river or lake, a robotic crab with possible uses for detecting mine detection.

8. Lucy by Steve Grand does have a lot of knowledge, but the designer of the robot claims it has the ability to mimic, "Many people still think of the brain as a passive receptor of information. I think of perception as a much more active process. As conscious beings, we don't live in a real-world-we live in a virtual world inside our heads. Most of the time this internal world is closely synchronized to the external world-our model matches reality, tracks it, and predicts it. When we dream or when we image things, we disconnect from the real world and let the model run on its own. Although the same mechanism are at work in both cases, the synchronization with reality is missing when we dream or think. The model is the crucial thing: perception is an active process, in which we use this model to predict, hypothesize about, and correct data fed in by our senses-filling in details when the data is incomplete and being surprised when reality fails to live up to the model."

9. Smelly, a University of Portsmouth robot has two tubes containing a smell sensor sensitive to alcohol. The sensor is connected to a bridge circuit and its resistance changes when an organic compound is absorbed by the sensor film, allowing the concentration to be measured. Hiroshi Kobayashi uses electric actuators beneath robot skin to create the appearance of facial expressions. The actuators are made from shape memory alloys, metals which are easily deformed when the current is flowing and returns to its original shape when the current stops.

10. Stirling Cricket uses ANN to control its movements and behave similarly to a female cricket seeking a mate. Female crickets home in on males by listening to their chirping song. Sound reaches a crickets eardrums-located on its forelegs-both directly and via internal tubes. When the robot hears a sound from its right a signal passes down to its motor via the right-hand neurons, at the same time inhibiting the passage of any signal from the left-hand neurons, and the robot cricket moves toward the source. Pine Labs have pioneered a method of sitting cultured neurons on multi-electrode substrates - 60 electrodes made of the transparent conductor indium-tin oxide on a glass substrate-allowing their electrical activity to be monitored. A gas-permeable membrane made of Teflon protects the cultured neurons and allows them to be kept alive for two years or more. Steven Potter has connected the neurons to an animat, a simulated mouse moving around a virtual maze in 3-D graphical environment. Electrical signals from the neurons are picked up by the electrodes and converted to movement commands.

11. Duke University connected electrodes to the brain of a monkey and recorded brain activity as the monkey reached for food and data from the actions were feed into a ANN. When the monkey reached for food, the ANN could predict its muscle movements and send the instructions to a robotic arm.

12. Fred and Ginger are two robots that can work together to carry out a task. Each robots that have square plates that can move forward or back, left or right.

13. Sandia National Laboratories have been developing swarm robots for find a source. Each robot continually informs others of its position and the strength of the signal it is receiving from the source. The streams of information allow each member to continually refine its search allow the robots to find the source four times faster than any published method.

14. Hiroaki Kitano established Robocup. The ultimate challenge was to develop an team of humanoid robots that could defeat the human world champions by 2050. The rules change each year as research groups get better at their tasks. The robots must recognize where they are on the field, whether they are in attack or defend mode, recognize other teammates, and execute complex trajectory projections of the ball.

15. The piezoelectric effect uses mechanical energy-pressure, to the polarized crystals-the bending results in an electrical current. Touch sensoring is going to be crucial allowing the robot to feel and prevent squashing items it picks up. The degree of skin material elasticity will determine the amount of electric charge.

16. Hiroshi Kobayashi work concentrates on robot facial expressions that can accurately mimic human expressions. The more real the robot looks the more human like its behavior is expected to be.

Research into artificial intelligence has been undergoing a roller coaster ride in the past four decades. Promises were made, but were never fulfilled as to the building of intelligent machines. Both the military and industry were interested in robotics, and industry got what it needed at the time, in the context of manufacturing, but these robots were by no means intelligent. Lately a new wave of optimism in artificial intelligence has appeared, and one will naturally wonder if this optimism is justified. Highly advanced intelligent machines have been predicted to arise in the next two decades, but it remains to be seen if the research in artificial intelligence will allow this to come to fruition.

This brief but insightful book is about the ongoing efforts to build intelligent robots. It gives though a healthy dose of skepticism, and that serves to remind the reader that a lot of hard work is ahead if these types of machines are to be built. The author emphasizes the viewpoint that basing intelligence on the human model as was done in the last thirty years has not resulted in advances in artificial intelligence. Therefore, the author looks to other more simple forms of life to obtain a model of intelligence. Indeed, in the book one finds robots based on snakes, monkeys, flies, cockroaches, grasshoppers, crabs, pikes, birds, orangutans, tortoises, lobsters, crickets, lampreys, dogs, and platypuses. It remains to be seen if this approach will lead to the rise of intelligent machines, but the book does give a highly interesting overview of what has been accomplished to date using this approach. The acceptance of robots and their practical use could perhaps be done best by introducing them as objects we are familiar with. Pet robots or robots that perform useful but restricted functions as already begun in the marketplace, with impressive results.

The author discusses some interesting work on just how to employ robots in the field so that they are able to function and obtain energy autonomously. Anyone who has owned a pet robot understands the aggravation of the frequent need to recharge batteries. The author gives the example of the "SlugBot", which captures real slugs, drops them into a methane-producing biomass generator, which produces electricity for the robot. The engineering difficulties of this approach are enormous of course, and the author is careful to point this out. Farmers though, would appreciate the assistance of these slug-exterminator robots. Other strategies that deal with the "recharging" problem are discussed, such as the one of building "robot ecosystems".

The author also includes a very brief discussion on "robot cars", pointing out that autonomous cars are already a reality. The legal environment though is the only real impediment to their being put into production, as the author points out. This and human factors, such as the trust that an individual must feel in permitting the car to deliver him safely to the destination, will play a major role in the acceptance of robot cars, and robots in general. Humans need to know that the robots are smart enough, and adept enough physically, to assist them in tasks that might bring them physical harm.

Robot toys in the form of "baby bots" are also discussed in the book: the "Robota doll", which was designed to react to touch and handling and to the presence of a human. The author discusses the negative reaction of child development experts to robot dolls, the claim being that children may perhaps be confused about whether the doll is really alive. She raises the question as to whether the money spent on robot doll research would best be spent on child playgroups. Her question is an interesting one, and the answer to it will determine the economic plausibility of developing robots. If a certain need can be met without robots, and at a substantially less cost, there will be no incentive to bring robots to the marketplace, in the area in question. Researchers and business people are going to have to scale down the cost for intelligent robots if they are to become normal additions to the human community.

No book about robots could be complete without a discussion of nanotechnology, and the author does this in the context of the physics. The accelerations and momenta of nanobots is not a problem that researchers need to be concerned with, contrary to the case of large robots. The author also discusses the possibility of using DNA as a "chemical glue" to assemble molecule-size nanobots. This brings in to the picture the use of genetic engineering to assist in the manufacture of these nanobots, a prospect that is utterly fascinating.

The material is presented clearly and comprehensively from the unique perspective of the SOM originator himself. The inclusion of exhaustive references is particularly useful for the prospective researcher, but, at the risk of sounding ungrateful, I'm curious as to why paper titles were not included in the citations? Overall though, a very good reference.

This is a wonderfully written, and excellent book. It assumes only minimal background knowledge but imparts a great deal of insight. I love the way that the author describes this area and the connections with deep and beautiful mathematics.

The book presents an interesting premise that humans will evolve from purely biological to biological/technological and ultimately to technological beings. Whether or not Kurzweil has gotten the time frame right is the question. If he is right, humans are only 20 to 30 years from this singularity. A most thought provoking read.

A well-written and optimistic view of humanity's future. If even 1/100th of what Ray Kurzweil predicts comes occurs (which seems likely given his record) - then we are in for a very exciting century indeed.

It's a potentially important book as many other reviewers have pointed out but his stream of consciousness writing style gets aggravating. If you can skim, you win. If you read, you bleed. There aren't many things he says fewer than four times. But some of those things have come true, some will and some of the amazing ones may. He has an impressive track record. Now if he'd just add discipline to his writing.

Ray Kurzweil is an exceedingly intelligent and perceptive individual. His scientific insight into the future is fascinating and frightening. I am listing this as a "must read" to all of my top students.

Over 600 pages with 100 pages of "notes"!. Lots of rambling commentary. Not worth the money. Watch Nova.

When you are the inspiration for a Jurassic Park character (or at least I think he was), then you immediately capture my attention. I'll buy your book, even if its subject matter is generally outside my interests.

Stuart Kauffman seems to have been at least partially the inspiration for the interesting chaotician character "Ian Malcolm" in Jurassic Park, and I thought his real life ideas would be as interesting as his fictional incarnation's ranting on chaos theory.

Not quite. At Home in the Universe sounds a lot more interesting than it was. It's plodding and full of jargon. And for a bonus, atheist polemic is included at no added charge! Yay! *Cough*

Kauffman also inserts random drivel about nature being sacred despite the falsehood of religion. Not only is that absurd on its face (sacred means set aside for religious veneration), but what the hell does it have to do with his ideas about complexity and emerging order? Spare me the atheo-preaching, please.

Actually the books is an outcome of scientific experiments in a computer lab. Differently from other reviewers, I want to notice that the facts of chaos exist in every where such as in Nature or in A Company.
Writer shows that everything in the world can be reduced to a series of chemical reactions. Chemical reactions can generate a complex system such as life from dead. He argues also the equilibrium of life and dead from the view of the number of kinds of molecules and the number of kinds of outcome from these molecules create or which are already in the system.
He also like many chaos theorist says that small changes in the system make big changes in the whole. (Explaining evalution). By some evidents and using probability, he shows that life on earth is the expected.
The books most important view is explaning everything as chemical reactions. And I believe this is the right thing...At Home in the Universe: The Search for the Laws of Self-Organization and Complexity

In this book, Stuart Koaffman opens new doors to us. Through the theory of the chaos, proportions fractals and their networks boulinas, give an interesting speculation us on the origin of the life, the complex systems and the societies. It is hour to be on the awares and to try to focus to us in new horizons. This book took to him of the hand by these new horizons. It is hour to know our house in the universe...

Stuart Kaufman's At Home in the Universe is a lay redaction his scientific hypotheses from his Origins of Order, a rich, fascinating, sophisticated, and complementary set of hypotheses added to Darwin's theories of evolution. For the moment, at least, they are the promising fruit of speculative or theoretical biological hypotheses (with physics, chemistry, geology, paleontology, mathematics, game theory, and economics thrown in), but they go a long way to filling in many of the gaps that strict Darwinists seem content to ignore. And some of his hypotheses, he readily admits, are heretical.

One of the obvious problems, if not primary one, that Kaufman sets to answer, Is how can natural selection work, culling the fittest to survive, without something to act on? In other words, natural selection operates on the already existent (i.e., regressive engineering), not in the formation of the entity itself. Another problem is that 4 billion years, long as that is, is still not sufficient time for natural selection to have acted through a totally random, step-by-step process in determining today's survivors. Even 100 billion years would not be enough. Another problem is how could so many species have come into existence and failed to survive (99.9%), leaving a mere 100 million for the present, in the span of a mere 4 billion years (mathematically impossible on Darwin's theories alone).

The central theme of Kaufman's work is Self-organized Criticality, a scientific twist on the notion of irreducible complexity (from the Discovery Institute's lexicon, no less), where a minimal degree of inherent complexity in a subcritical-supercritical phase transition is what spontaneously orders the animate world and generates and sustains life in accord with other, as yet, unknown, but implicit laws. From the moment that a sufficiently critical diversity of molecules reached the ideal phase transition, life itself was "spontaneously generated" as inevitable, not by accident. Once life appeared, the acts of natural selection, adaptation, coevolution, evolution of coevolution, cellular, morphological, and physiological differentiation, ontogeny, niches, populations, stable cum-chaotic dynamics, etc., could operate, but in addition to forces beyond natural selection. And while speculative, apparently many scientists share Kaufman's intuitions, inferences, and insights.

But the "other" force or forces is not mystical, much less divine, even if they may be truly awesome. Rather, it is in the nature of the universe, and more particularly in our evolving earth, that these implicit laws work in tandem with Darwin's laws. At this point, these laws are posited from the empirical knowledge we do have, but have not yet demonstrated in the scientific manner to make them even hypotheses. But Kaufman's speculative biology is not a whimsical or arbitrary metaphysics, but logical inferences based on laws and facts already in place. Having done the easy work (thinking the notions of what these other general laws of nature must be like), now science must work in earnest to confirm or reject his speculative hypotheses.

The key word and concept throughout this humorous, heady, and exacting exposition is "complexity" and within the manifold complexities of lives, environments, and mutually intersecting dynamics is a spontaneous order that arises "for free" that in turn sustains stable and steady systems just at the subcritical-supercrticial phase transition (e.g., horizon, or "edge of chaos"). Another key word and concept is "dynamic." Steady-state and homeostasis are often thought of as a static plateau, but that is mistaken, as such states are actually in a fluctuating dynamic at the phase transition between equilibrium (death) and disequilibrium (disorder). Indeed, on many different levels, living organisms are born, dwell, and die precisely at this phase transition between the subcritical (stasis, moribund) and supercritical (chaotic, disordered) states. And the key thesis is that order ("for free") is embedded in the delicate balancing act precisely at this phase transition.

Kaufman extrapolates some of these implicit biological laws and applies them to human cultural and technological advancement. The "fit" is remarkably uncanny, helping us to understand some of the dynamics of technological improvements (and diminishing returns), innovation, extinction, and spontaneity of the economy. Perhaps the most salient features are the concepts of "dynamic" and "spontaneous."

Moreover, if an analogy can be drawn from the biosphere and ecology to the social and political realms, the overwhelming preponderance of biological evidence screams complexity, diversity, and interdependence of organisms and their environments, which arise spontaneously and reciprocally to each other, in a constant dynamic that is vibrant, active, and always on the threshold of "chaos," but retains some stability through change. It is only those social and political forms that are "adaptive" that are socially and politically the "fittest," and democracy and market economies are obviously the most adaptive mechanisms to adapt to changing human needs.

Frederick Hayek addressed himself to these very issues over 50 years ago, and called the market economy and democracies "spontaneous" associations, in contradistinction to "planned" economies and governments. The former "adapt" to changing environments and circumstances, while the latter lack flexibility, and thus do not easily yield to adaptive mechanisms. "Planned" economies attempt to calculate rationally human desires, motivations, and needs in either an abstract or a priori fashion, then calculate the mode of production, the degree, and whether to accommodate, as if some "Absolute Human Mind" could anticipate all contingencies and changes by a simple mathematical formula. The problem is that bureaucrats are notoriously theory-laden and too calculating to include, much less advance, diversity (think Medicare Part D for "planned" absurdity). In practice, socialisms impede innovation and stifle ingenuity. With no means of adaptation, there is no "fittest," much less any mechanism to adapt to the actual dynamics of the world.

Communism's planned economy is an extreme case of an irrational calculus asserting what the government will allow, applying the lowest-common denominator as a criterion of sufficiency. We all know of the U.S.S.R.'s food lines, limited products, forced housing, inferior merchandise, and minimal labor investment. But even weaker forms of the rational calculus, such as socialism, does not do much better. At least their democracies allow policies to change, even if it becomes years for government to adapt to the new exigencies. Even the most socialized societies have "capitalist" outlets, to provide some barometer of social wants and meeting them. Social insurance makes sense on many fronts, but social or state "planning" of economics has rotted state and worker. Kaufman's biological analogies explain why.

Postscript: Kaufman's book is a provocative, challenging, and fascinating (sometime heady) read. Even if all of his hypotheses in the abstract are found to be untrue, at least he captures the reader's imagination, and asks the questions that most of us non-dogmatic Darwinians have raised for some time. In a time when the "easy" and "orthodox" are all too convenient for slipping under the rug, Kaufman's questions (and suggested answers) go the the very nexus of the difficulties. His suggested answers are at once perhaps too simple, on the other hand, perhaps too complex. What is refreshing, above all, is that he's not afraid to ask, and even less fearful of suggesting solutions. Thank gawd for the Sante Fe Institute, where brave and curious minds still ask questions.

Stuart brings the science of complexity and complex adaptive systems to a broad range of topics from evolution to business to learning curves. The book is masterly written to allow you to skim over the formulas without lossing the excitement or to dig into the technology to understand its broad application.

This book was incredibly interesting - a topic that I certainly wouldn't have thought would be so. It is very well written - so much so that you think you are reading a novel at times. The stories of tracking down the families and descendants were particularly fascinating.

I think the title of the book (and the jacket picture) provide a good clue to how this book is written - serious yet fun. The reviewers that said the book lacked scientific fact or basis missed this point entirely.

Definitely one of my all time top non-fiction books.

I'm only half way through the book, but I'm not sure I can go on. I may return to it later. There is definitely thought provoking material here, but I was looking for more scientific "meat". Interesting that when Graham opened up to the sperm bank to any woman, it became a market driven company for the short time it was around and women began selecting for qualities other than Nobel type intelligence, qualities like "looks", happiness, athleticism. Darwin strikes again!!!

This is a great book, made even more relevant due to the pressing times we live. Given the current state of science, artificial births, and eugenics, this book provides some great background on one man's attempt to breed a society of geniuses. Science fiction you say? No, this is all true fact! The author did an excellent job of digging up all the information necessary for this book, which was no easy task given the ultimate secrecy in which the project was carried out under. This is definitely THE book on this topic, and I highly recommend it to anyone who is interested in science and where society may be headed in the near future if we are not careful!

As numerous readers have pointed out, this book is divided into several parts or themes. What I found to be the most interesting was the psychological and emotional part of the book--the identity of the donors, how these banks operate, and the psychological fallout for the kids and their families from anonymous donation, and then in some cases, disclosure. Basically, the banks were run like a joke, and a lot of the kids seem desperate to know their genetic (what they would call "real") dads--which is incredibly sad. Meanwhile, the fathers who (sometimes) raised them felt disconnected to the kids... Some of the donors were liars and very creepy guys who were going nowhere with their lives, but had some bizarre sense of narcissism that was satisfied by donation. This book is not fantastically written, and there are almost too many angles and things to think about in here, but overall, it is indeed a page turner and fascinating book that is well worth buying. Probably great for book clubs as well. There would be no end of opinions and items of discussion...

My husband is one of the children Mr. Plotz writes about in his book and in his articles on Slate. Unfortunately, Mr. Plotz is much more concerned with creating a sensational story than with fairly or accurately presenting the lives of the people involved in the Repository.

My husband's experience with Mr. Plotz demonstrated to us that Mr. Plotz's sole interest is in dramatizing and sensationalizing, even to the point of distortion, the experiences of those he interviews in order to benefit from their stories himself. Please read all his writings with a critical eye - he was neither "sensitive" to the "emotional consequences" of his journalism in our encounters with him, nor was he respectful of the youth and vulnerability of his subjects in our experience.

How does the development of "artificial" intelligence fit into biological evolution? George Dyson suggests that the fit is seamless. This profound investigation of the history of thinking machines and evolutionary theory is brilliant and engaging. It offers a far more palatable look at the human-machine future than the misanthropic vision of Ray Kurzweil in THE AGE OF SPIRITUAL MACHINES (Viking, 1999 ). (Palatability is no assurance of accuracy, of course, but it sure feels better going down.) Where Kurzweil sees machine intelligence as better than human and confidently predicts that we will upload ourselves and abandon our bodies, -- drawn initially by the superiority of cyber sex -- Dyson envisions a spreading macro intelligence that will involve humans in wholly new ways -- and suggests it is already emerging. He predicts that much like other life forms which share their eco-niche, we are apt to become symbionts with the machines, each doing what we do best and benefitting the other. But perhaps I have gotten ahead of myself here. Are there living, thinking, artificial minds on this planet already? Dyson asserts that it depends entirely on one's definition of "living," "thinking," "artificial," and "mind." There are self-replicating cybernetic entities evolving within computer networks. Self awareness is not yet evident, but it is not entirely clear that it doesn't exist. How will we know when and if it emerges? We don't have a clear definition of consciousness as it applies to our own condition, which makes consideration of the whole issue iffy at best. Tracing the history of evolutionary theory, following the startling course of mathematics in the past century, noting the lightning fast advances in silicon technology, and all underlain by a trenchant sense of human development, Dyson weaves a fascinating tale. He pulls in the Darwin family, Darwin's critics (then and now), oddball tinkerers, mainstream theorists, nuclear physicists, Turing, Godel, and von Neuman, and the science fiction of Olaf Stapledon in the telling. A highly rewarding if sometimes difficult read. Dyson ends with a quote from Thoreau, suggestive of his own open-ended view of our future: "In wildness is the preservation of the world." Exactly so.

EDVAC architecture by Von Newmann changed the world. Von Newmann chose to adopt the McCulloh-Pitts symbolism for diagramming logical structures of stored program codes. EDVAC had the ability to modify its own instructions similar too the theoretical Turing machine. EDVAC stored both data and instructions in mercury delay-line memory as binary and as in the Turing Universal Machine, long strings of bits represented numbers to be operated on and sequenced and potential dynamic structures of operations to be performed, such as bit shifting, multiplexing, Boolean logic, memory storage, and accumulation.

Von Newmann's next machine was called the IAS. The initial development of the IAS design was distributed to multiple locations. A central processor operating in parallel on multiple bits of a word of data at a time characterized IAS. ISA had a hierarchical memory range with random access to memory on limited media, and a distinction between software functionality and hardware functionality. "Science, as well as technology, will in the near future and in the far future turn from problems of intensity, substance, and energy to problems of structure, organization, information and control." Von Newmann was persuaded that the high-speed computer would change the nature of mathematical research. The IAS machine contained the world's first fully functional random-access memory, RAM. Disk storage was provide through 40 cylinders arranged in a bank of 20 with 1024 bits per cylinder; additionally, 40 Williams tubes and 2,600 vacuum tubes performed digital processing with a 75% up time. IAS included an arithmetic unit, accumulator, two shift registers, an adder, and a digit resolver. Floating point was considered but not implemented. IAS included 20 basic instructions and 44 order codes.

Human calculators provided the pattern of processing modeled in the computer. Human calculators demonstrated coordinated computing, sequencing, and analytical capability. Human calculators worked in parallel managed and coordinated processes deciphered WWI Germany encryption messages. The brainpower and segmented problem solving 10 X 15 power number combinations.

The human calculator model could be simulated in the Von Newman and Turing machine and the connection machine architectures and software. Neural Nets could be model in the Turing machine.

However, evolution algorithms will not be able too produce a thinking machine. Thinking is limited to the humans and divine beings. Behavior can be represented in Finite automata graphs, AFSMs, and mechanized behavior may appear logical but this does not suggest the machine can cross the sphere into human intelligence. The title of the book directly is a criticism against the evolutionary humanist. Turing grammer suggests discrete processes can be interactive described by a language. Computer automata can not evolve beyond discrete functions and the machine will be confined to the range of mathematical theorem proofs. Mathematical reasons does not encapsulate all human reasoning and such an acceptance of this conclusion would be uncreative, limiting, and lacking in vision of the potential for humans to feel love, joy, and acquire greater intelligence.

Von Newmann saw digital computers as mathematical tools, a general class of automata and did not imply they could think. Von Newman became more interested in the machine reproduction. "Every automa that can produce other automa will only be able to produce less complicated ones." Celluar Automa has yet to produce a computer brain that will function. CA algorithms surprisingly can model many patterns found in nature and physics. However, no CA has produced a grammer or graph that can be reproduced by the machine yielding an intelligence reasoning machine. Von Newmann hoped for CA salvation, "there is, however, a minimal level where this degenerative characteristic ceases to be universal. At this point automata which can reproduce themselves, or even construct higher entities are possible." Von Newmann's inspiration was not CA but VLSI. VLSI were being replicated from computer generated patterns by computer operated tool. FAB in the 20th century continued Von Newmann's aspiration and robotic automated factories suggested to a minimum degree the theory had value. Intelligence move counter to entropy and if one observes a machine producing other machines of higher construct characteristics than one would declare intelligence has been proven. In "Flesh and Machine" the Brooks suggests GA do have the ability to create simple behaviors such as locomotion, tactics, and architectural models but fail too create higher-level concepts. Abstracting and creative thought are outside the realm of the machine. Brooks suggests AI breakthrough is limted by a lack of quality software, missing laws of intelligence, slow machines, and entropy caused by a lack of young Einsteins willing to dedicate their brains to solving the AI problem.

Von Newman in his "Theory of Self replicating Automa" believed automa would grow more complicated from one generation to the next; no device would become the brain; high speed switching was millions of times faster than biological neurons but pales in comparison with the combinatorial ability of a billion neurons; and something as complicated as the brain could not be designed but had to be evolved. The idea that perfection could be reached by random arrangement of neurons seems doomed to fail. Von Newmann suggested growing a matrix of artifical neurons. These neurons should have the tendency towards self-organization among large number of interconnected secondary machines. Incomprehensible complex processes among the secondary machines could be observed by humans have the appearance of comprehensible behavior. Brooks simple behavior modeling through AFSM seems too synchronized within the realm of computer theory. Imitation verses enhancement feels like imitation is more discrete, definable, and programmable. Enhancement seems to be the result of complicated imitation and the AI is the failure to adequately define AFSMs too model observable behavior. AI evolution must be confined to the realm of the Turing machine grammar. To expect a machine to suddenly start thinking and its neurons to behavior like biological counterparts is a myth, a fable to consume brilliant minds into the dream that machines can think.

Though well written and informative, in the end DAM was a less than satisying read. Dyson marshals considerable data (and extensive and informative quotes) from the fields of history (of science and technology), the sciences (principally evolution and CS), and philosophy (as it has, historically, reflected on notions of mind and evolution). As an avid reader of history, with a deep interest in all of these subjects, I found the opening chapters of DAM quite interesting. That said, the history in DAM is not particularly deep. But Dyson writes well, and I appreciate his having shed light on several lesser known (and underappreciated) historical figures along the way.

Where DAM ultimately falters, in my view, is in its shallow futurism. I say "shallow" not because I don't think Dyson is highly imaginative. He is. And his predictions (to the extent he articulates them as such) may well be realized one day. However, though Dyson is skilfull in establishing the historical groundwork for the development of computer and communications technology as they exist today, he is far less skilfull in tracing even a speculative chain of developments from the present state of the art to the global/artificial intelligence he envisions as a possible (perhaps inevitable) future development. In fairness, every futurist has hit and will continue to hit this wall until the future comes knocking. But Dyson purports to do so.

In the final analysis, though Dyson does an admirable (and entertaining) job of accounting for the rise of computers, and the increasing complexity of computer networks, his discussion of artificial intelligence has more the ring of a leap of faith. It's a fascinating idea (though hardly original to Dyson), and certainly a possibility, but one whose potential trajectory (from idea to realization) is barely even attempted in DAM. DAM would have profited from a little more hard science, and a little less soft speculation.

Several have criticized Dyson's philosophical and historical treatise "Darwin Among the Machines" for not articulating exactly how a global intelligence might emerge from today's synthetic biological and computational networks. But as Dyson says in the preface, the past is where we find answers, and the future merely a fog of questions "to which the answers are up to us." In the next 200 pages, Dyson explores the history of an idea: that man will someday create a form of artificial life, with intelligence that may match or exceed our own.

It may astound some readers to know that these ideas date much farther back than Alan Turing's "Turing Test," or Vannevar Bush's influential essay "As We May Think." Consider the following quote from Thomas Hobbes (1651): "Nature is by the Art of man, as in many other things, so in this also imitated, that it can make an Artificial Animal." Or consider this excerpt from Samuel Butler's 1859 essay, which serves as Dyson's main theoretical foundation: "As the vegetable kingdom was slowly developed from the mineral, and as in like manner the animal supervened upon the vegetable, so now in these last few ages an entirely new kindgom has sprung up ... It appears to us that we are ourselves creating our own successors."

Careful to acknowledge his predecessors, Dyson profiles the lives of some of the most prescient Enlightenment- and modern-era thinkers in captivating detail. In so doing, he traces the evolution of the "Artificial Animal" from its earliest incorporeal appearances - as merely an idea - to its current computational incarnation in neural networks. But Dyson doesn't stop there.

In fact, he goes on to argue that the global telecommunications network (primarily the internet) may provide the appropriate architecture for a kind of global, distributed intelligence to evolve. Here Dyson borrows from Leibniz, who noted that the "soul" may be "born when the machine is organized to receive it, as organ-pipes are adjusted to receive the general wind."

To further support this claim, Dyson draws parallels between the development of increasingly efficient machines and the processes of biological evolution. In fact, this is one of the most interesting parts of the book, in part because the language in which Dyson details the principles of evolution might be considered dangerous today, in the midst of the raging Intelligent Design debate. For example, Dyson suggests that evolution itself may embody a kind of intelligence, though we frequently perceive it as merely a shallow process, highly dependent on chance and randomness.

As Dyson points out, this perception gets to a fundamental semantic confusion surrounding "intelligence," a phenomenon well known to AI researchers in which problems once thought to require intelligence are then seen as trivial after an algorithm is designed to solve them. As Dyson points out, intelligence may simply be a word we use to describe behavior that corresponds to our view of how humans behave. Not believing in "'the existence of an intelligence behind the achievements in biological evolution may prove to be one of the most spectacular examples of the kind of misunderstandings which may arise before two alien forms of intelligence become aware of one another.' Likewise, to conclude from the failure of individual machines to act intelligently that machines are not intelligent may represent a spectacular misunderstanding of the nature of intelligence among machines."

Ultimately, whether you agree with Dyson's perspective is besides the point. This is not a scientific book; many of the ideas are purely philosophical, and the logic used to support Dyson's assertions frequently rests on historical anecdote and analogy. These should not be considered weaknesses, however. The real, lasting value of "Darwin Among the Machines" is Dysons's imaginative and graceful writing, his impeccable historical research, and the conceptual ease with which he integrates ideas from ballistics, biology, hydrodynamics, set theory, Cybernetics, and uncountably more esoteric subjects.

Though I won't dispute that many of these exciting ideas are far-fetched, Dyson has found powerful allies for his assertions, from Hobbes and Leibniz to Goedel and Von Neumann. So if you find yourself believing - or simply wanting to believe - in these groundbreaking ideas, then you're in fine company.

I bought this book in the hope of reading some intelligent speculations by the author about evolution, machines, and AI, which is what the title suggested I would find. However, it turned out to be a history of the evolution of computers with old speculations from the computer pioneers concerning the evolution of computers injected along the way. To be fair, the author does have an overarching thesis that he tries to weave into the historical narrative whenever some past speculation seems to lend it some support. It is that the World Wide Web - that well known network of millions of computers - may some day, at a certain critical size and running who knows what software (certainly not the author) will become intelligent in some way (also not specified by the author). Come to think of it, I think the author has used the historical angle of the book - the similar speculations of the computer pioneers of the past - as a device to lend credence to his thesis - a kind of proof by consensus. I remain unconvinced, however. His arguments (where there were any; it was hard to tell his arguments from narrative) were very weak and unconvincing. To his credit, the author did a tremendous job of scholarship for the historical side of the book. However, he left the speculative side undeveloped (at the most weakly developed) and, therefore, the book was unappetizing to me.

I have to agree with all of johnnied7 criticisms. This book is pitched at a level too advanced for an introduction. It also reads and is structured like a research paper. Not recommended.

I have an engineering degree, and I found this to be a little tough to follow for two reasons:

1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.

2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.

When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!

This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.

There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.

To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.

First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.

Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.

The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.

This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.

About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.

This book gives a very deep insight in the problems of the creation of systems with some intelligence.

I seldom pan books, but this one is little more than an egotistic rehash of simplified versions of AI/AL theories of the past 3 decades. The only new data are the author's opinions. If you are looking for anything new and/or in depth, look elsewhere.

There are some interesting analogies and examples of perceived or emergent behaviour but overall many of the arguments do not hold up to scrutiny. If you are looking for a book written in the form of opinions, as opposed to hard facts, then this book will be more to your liking. Certainly something I would borrow from a friend/library rather than paying for it to sit on my shelf - not something I will go back to in future to read again or for reference. It was a disappointment as I was led to it from Richard Dawkins' excellent "The God Delusion".

I must say I enjoyed the book completely. While I don't agree with the author on all points - the book is incredibly though provoking. I was so glad to see such a book written that after I read the book (in 3 nights - short book) I bought three copies for co-workers who I wanted to read it. Steve Grand is now quite famous (or should I say even more famous) after writing this book. He has challenged traditional thought on the subject of AI and in a way made converts out of many.

However, he spends 10 chapters saying "you are all wrong - this is the way to do it" - then doesn't follow up on doing it himself but rather takes enough shortcuts to make the work suspect - but to his credit he does say he's doing going just that :) All in all - if you're a software engineer or software developer I highly recommended the book.

Grand's book isn't that at all, although it tries to be. He starts out with his definition of life, and builds (slowly, oh so slowly you could cry) up to the point where the rubber hits the road, so to speak. At that point, he imply handwaves the entire process of programming a-life.

So in short, there are books out there that do a better job of explaining the application of neural networks. There are better books on the philosophy of consciousness and life. There are better books on game design.

So you don't need this one, because Grand only tells you what you already know, if you're at all interested in the subject, and doesn't add enough best-practice techniques to make his way of building a-life better than any other. It's as though he was so worried about giving away his secrets that he redacted all of the information in the book that would have made it more than 'Here's my philosophy.'

The only thing saving the book from a one-star review is that the first 10 chapters do light a fire in your belly to find out more. But this book isn't going to provide that 'more' that you want to know.

Interesting how Ms. Hayles does not mention the transhuman or transhumanism, Max More and his seminal essay "Becoming Posthuman" written several years before Ms. Hayles book was published. Anyone using the book in their course work might want to think about this.

This book is worth the effort. Or maybe all the effort you'll put into this triggers a cognitive dissonance reaction: I just spent 4 hours reading one chapter, so it must have been good. Right? Right?

This book is good, if only for her obvious reverence for the cyberpunk grandaddy PKD (Phil K Dick if you don't know already). Whether or not you accept her premise that we are already "posthuman" she considers her subject matter in a most interesting and relevent way, bringing in fiction that relates to the subject, as well as the history of computing and cybernetics (with some fun little anecdotes about the one and only Norbert Weiner). If you're a geek or into future-minded philosophy, pick this one up. She makes some convincing arguments, it just takes a good long while to decipher what those arguments actually are.

This is probably one of the hardest books I have ever read--with no background in either philosophy or cybernetics, much of what Hayles discusses is just plain incomprehensible. I also found it difficult to accept the idea of humans already being "post-human." If you are interested in deep philosophical writings on technology and the human condition, with links to literature, read this. If you don't really care about the post-human, skip it.

Yes, this is 22nd Century thinking today. I was fortunate enough to meet the author at a LA FUTURISTS SOCIETY meeting where she was a guest speaker. She looks ordinary-- like a college professor-type, speaks clearly but her writing is the extraordinary talent. She combines humanism and science to see how virtual bodies and informatics are influencing how we live, work and love. One of those books that yearns for you to write in the margins and put your notes in the back. Pages and pages of notes on my copy. No one will share this copy, don't even ask!!!! Not an easy read but well worth the journey. I love to read books in hours or days but this one took weeks (in between other reading) and it was well worth every minute, hour, day spent. Perfect book for this summer when the MACHINES ARE TAKING OVER on our screens at movies and television. The crossover from cybernetics to literature is what is so fascinating. I can't begin to summarize all that I learned and all the questions that it brought up for me to seek out more info. Belongs on every science and literature teacher's shelf. One of the books they should require for every engineer and techie at the beginning of their careers. Make way for the future!!!!!

This is an important, impressive, and infuriating book that should be read by all those interested in the posthuman movement, the possibility of a cyborg future, and the nature of cyberspace. I agree with other reviewers that it is a penetrating analysis of the cultural revolution taking place in information and what it means for human (and posthuman) society. It is important as a powerful statement of the post-modern concern with embodiment and what that might portend for the future of humanity. It is impressive as a wide-ranging analysis of the inter-linkages of technology, culture, and the human body. It is infuriating because of the jargon-filled text and convoluted nature of the writing. That last criticism is one that is generic for post-modern works such as this, and certainly not a specific criticism of this book.

UCLA professor of English N. Katherine Hayles makes the case that the body (or lack thereof) is central to this posthuman future. She notes that the body is lost in the information age, as disembodied voices/knowledge/data came to dominate thinking about a posthuman evolutionary stage. She also explores the development of the concept of the cyborg, and what the merger of humans and machines might eventually come to mean. She undertakes the analysis through a series of case studies. One of the best of them is her chapter on the science fiction of Philip K. Dick, whose novel "Do Androids Dream of Electric Sheep?" was made into the classic feature film "Blade Runner." His obsession with artificial life, and by extension "real" life, consumed much of Dick's writing and has much to say about the essence of the posthuman.

The most challenging and interesting part of this book is Hayles argument that Homo sapiens as a species are endangered in ways we have never conceptualized. Hayles notes that the rise of artificial life will lead to the next stage of the evolution of life on Earth. "If the name of the game is processing information," she writes, "it is only a matter of time until intelligent machines replace us as our evolutionary heirs. Whether we decide to fight them or join them by becoming computers ourselves, the days of the human race are numbered" (p. 243). The author does not view this with serious trepidation. As her last sentence in the book states: "Although some current versions of the posthuman point toward the anti-human and the apocalyptic, we can craft others that will be conducive to the long-range survival of humans and of the other life-forms, biological and artificial, with whom we share the planet and ourselves" (p. 291).

I think Hayles would agree with the Borg's slogan, "resistance is futile," but not with the dystopian concept of the human future it offers.

As my background is mainly in the brain sciences, I was most interested in what Penrose had to say about consciousness and the brain in this book, so I'll concentrate mostly on the chapter that had to do with that. This is not to say I didn't enjoy the other chapters, just that I'm not as qualified to critique those as I am the one on the brain. There has been a lot of speculation in recent years about such things as computability and the brain, quantum consciousness, and so on, and I was interested to find out what Penrose might have to say about that.

One of Penrose's major ideas in this chapter is his demonstration that consciousness, although perhaps mathematical, isn't computable, in the sense that you could program a computer to simulate it. Penrose uses the example of geometric tilings or polyominos that are deterministic in their coverage of the Euclidean plane, but that aren't computable, to show this. Since, as Penrose points out, there are plenty of mathematical concepts that aren't computable and that can't be done on a computer, but that the human mind can understand, Penrose concludes that there is something beyond computability in both pure mathematics and the human brain.

This is interesting, and Penrose might be right about that. However, I must point out that while consciousness itself may not be computable (and I'm not really prepared to conclude this for sure at this point, because of what I'm about to say), nevertheless, many aspects of the brain's functioning have been shown to be computable, so I'd like to discuss that briefly.

For example, sensory neurophysiology has been shown to be both quite mathematical and computational as a result of the work of a pioneering mathematician by the name of David Marr 25 years ago, whose ideas revolutionized neurobiology almost overnight, after which the field was never the same. Marr examined a number of different fundamental sensory mechanisms, and showed, for the first time, that the way in which the visual system was processing light information was consistent with the operation of certain sophisticated spatial-frequency filtering transforms that are well-known in many engineering applications. To mention just a few of his important ideas, Marr's demonstrations that retinal receptive-field geometry could be derived by Fourier transformation of spatial-frequency sensitivity data, that edges and contours could be detected by finding zero crossings in the light gradient by taking the Laplacian or second directional derivative, that excitatory and inhibitory receptive fields could be constructed from "DOG" functions (the difference of two Gaussians), and that the visual system used a two-dimensional convolution integral with a Gaussian prefilter as an operator for bandwidth optimization on the retinal light distribution, were more powerful than anything that had been seen up to that time.

It was as if vision research suddenly acquired its own Newtonian Principia Mathematica, or perhaps General Relativity Theory, in terms of the new explanatory power Marr's theories provided. Basically, in one fell swoop sensory neurobiology also became an area of theoretical physics rather than purely biology, giving the area a rigor and elegance never before seen--an amazing achievement for a young man who died so prematurely from leukemia at the age of 36.

The main point of all this is that all of these mechanisms are both mathematical and computable, although the way in which they're done in the brain is probably more like how a computer would use numerical analysis to solve a differential equation, rather than using the original equations in a purely analytical way themselves. Since Marr's time, there has been further progress in this area, such as the great Bela Julesz's demonstrations that the visual system can extract and compute binocular disparity cues point-by-point for depth information from abstract, non-representational pictures or textures such as random-dot stereograms, the extension of Marr's ideas about monochromatic edge detection into color edge detection, the mathematical bases of non-linear visual field distortions present in optical illusions, and many other areas.

Furthermore, in the last few years, the nature of consciousness itself has been shown to be composed of many different separate mechanisms in the brain that are being coordinated in time in order for consciousness to occur. It simply isn't one process or central program that runs in the brain, nor is there a "master" brain center that one can point to where it can be said that consciousness resides. I'm sure the progress of this research will also have implications for ideas about the nature and computability of consciousness.

So overall, a fascinating and enjoyable discussion about the brain and consciousness by Penrose, even if I don't completely accept one of his major ideas about it for the reasons that I discuss above.

I see others have praised this book richly and a couple others poorly. One reviewer said one needed to be a mathematician and a physicist to understand the book. It would certainly help, but Penrose describes enough about the function of the math concepts he invokes so that I can follow him (and even in maths one has to have studied things like those density matrices to really understand in depth). I do understand about computability and problems which have been proven non-computable, and I think he is possibly right, that 'mind', because it shares/crosses the quantum/macro world boundaries, cannot be easily mathematically described without finding a 'bridge' between those worlds. I found his description/hypothesis about the microtubules fascinating (and again I am not a biologist but I am not disturbed that he didn't try to explain or prove his hunch regarding the possible functioning of these structures).
I appreciated the book for the wonderfully clear style as well as the content.

Let me first say something about Roger Penrose. One notices how certain other mathematicians and mathematical physicists speak of him. He is not only admired and respected; it seems that he is positively enjoyed! This may be a bit surprising when one notices that Penrose is something of a thorn in the side of several popular ideas in contemporary physics (and psychology). Cosmic inflation theories and ideas regarding the fundamental nature of quantum uncertainty find a formidable and articulate critic in the Oxford mathematician. Of the somewhat less popular, but ever fanciful "many-worlds" interpretation of quantum superpositioning, Penrose says "[the 'many-worlds' view] is not a very economical description of the Universe but I think things are rather worse than that for the many-worlds description. It is not just its lack of economy that worries me. The main problem is that it does not really solve the problem." He brings the same mental rapier to what he has called "the missing science" of mind and to the idea of computational / artificial intelligence. It is the problem of superpositioning described by Schrodinger and the decoherence caused by quantum measurement that prompt Penrose's search for an 'objective reduction' (OR) of quantum state vectors, the key ingredient in a "revolutionary" physical theory that remains a mystery. He speculates that this physical mystery may be related to the mystery of consciousness. He is unconvincing in this regard, but his ideas and arguments are quite interesting.
Well, let me now take this a bit further. Penrose also seems to terribly irk certain others! In particular he really raises the hackles of proponents of strong AI and the Dawkins/Dennett camp of 'consciousness-is-merely-mechanism' dogmatists. His views are much closer to those of perhaps most mathematicians and philosophers and stand on a deeper logical footing than do the doctrines that the human mind is mere biology. Let me say that I agree with Penrose in that the 'simple biology' view is never going to win this argument for reasons that can be demonstrated by the application of mathematical logic. To say that Penrose "doesn't understand biology" is to miss the point. The author freely admits, "there is a good deal of speculation in many of these ideas". Of course there is; science is largely -- we might even say wholly -- speculation. A more perceptive analysis would suggest that those committed to a rigid materialistic aesthetic don't understand (don't want to understand) the mathematics. Those who summarily dismiss Penrose do so unwisely. Given his contributions to mathematics (e.g., Penrose tiling, computability, mathematical logic) and his stature within the mathematics community, and given that the history of mathematics is essentially written by mathematicians, Roger Penrose may come to be considered the greatest mathematician of his generation. Given his work on black holes and space-time geometry (he recognizes the apparent "flatness" of the universe but suggests a more elegant geometry to describe that flatness), he may be one of his day's greatest physicists as well. Should his hunch ("OR") one day prove "true", his stature would approach that of a Newton or Einstein. The point being that any scientist who avoids or ignores Penrose's views, or is inclined to dismiss them by erroneously characterizing them, does so, as I say, unwisely.
Chapters 4, 5, and 6 are challenges to Penrose from A. Shimony, N. Cartwright, and S. Hawking, respectively. Apart from Shimony's discussion of A. N. Whitehead's views, its not on a par with the author's discourses; Cartwright suggests that nature may be a mess of "patchwork" laws (her view itself seems a horrible mess), and Hawking is disappointingly flippant. Penrose certainly meets these challenges.
I must say that the "controversy" over Penrose's Platonism is nothing less than nonsensical. Hawking complains "basically, he's a Platonist," as though calling him an offensive name and thereby granting the reader cause to disregard Penrose's arguments. That's unfortunate. Most of history's great minds have been Platonists; even Aristotle*, so often cited as the philosophical godfather of reductionism, was arguably a Platonist. Augustine, Kepler, Descartes, Pascal, Newton, Leibniz, Kant, Linnaeus, Einstein*, Schrödinger, Gödel, Whitehead -- the list of Platonists is long and impressive. As Penrose has said, "... it is my direct personal impression that the considerable majority of working mathematicians are at least 'weak' Platonists." Yet it seems as if some who call themselves "positivists" feel a calling to be science's mind-police. I suggest that this should be the real controversy... So-called positivists would do well to honesty consider Gödel's observation that the idea that mind/mentality is simply material is nothing more than the "prejudice of our time."
There is a rather child-like glee in the way Penrose sees and uses mathematics. His investigations and speculations are those of an extremely astute mind having fun! In his aggressive curiosity, his boldness, his clear-eyed honesty about the frailties of human thought and the limits of science, it seems to me that Penrose is something of a treasure and an inspiration. As he candidly states, "... the world-view that present-day physicists tend to present may well be grossly overstated as to its closeness to completion, or even to its correctness!" This volume presents a concise look at the Penrose ideas/arguments and even if nothing much ever comes of these arguments, they present a shining example of the kind of creative thinking that moves science into new frontiers.
*(footnote: While recognizing that it can easily be argued that Aristotle and Einstein were not "strong" Platonists, it seems obvious to me that they were each Platonists in some fundamental ways. I consider them to have been "weak" Platonists.)

Penrose concisely manages to give us an overview about 3 somehow interconnected fields, the mathematically described large-scale world, the deterministic quantum microcosm and the recently emergent mind science. His major aspiration is to see the new generation of scientists erecting a bridge between the quantum world and the always controversial substance of conscience.

Having in his mind (in a neo-platonic way) the idealistic nature of mathematics that apply to the physical world as a well-justified model, he firstly presents some themes from cosmology and abstract mathematics (e.g. hyperbolic, Riemann geometry), and why, in his opinion, Guth's inflationary universe theory, has weak points (see also Penrose's book- Difficulties with inflationary cosmology) In chapter 2 ,quantum physics related, he gives us interesting examples (the paradox& puzzles reference shows his great sense of humor) and explain us how wavefunction's reduction can assist us to deal with the probabilistic nature of events in this level.
In the most interesting third one, he is concerned to lay an in-depth foundation between quantum procedures through neurons, so as to explain his main belief - brain function (that creates conscience) can't be simulated through A.I. Even though I tend to prefer J.Searle opinion (presented in his book Mind,Brain & Science) Penrose's points are adequately justified, thus leaving an open window for Free Will.

In the next three chapters certain Penrose's point's are opposed from Shimony (physician, philosopher) Nancy Cartwright(logician, philosopher) and the renowned Steven Hawking.
Shimony in a formalistic language, but slightly excessive for the common reader, finally makes a conjecture about a hyperselection law, in order to avoid quantum dualism, while Mrs Cartwright sets a contronversy against the usefulness of a perception that sets Physics the only explanatory science for mind theory and not for example Biology.(which for Penrose is reduced to Physics)
Hawking denies an indispensable and direct correlation between quantum gravity and the yet inextricable conscience and in chapter 7 Penrose responds to all so as to end this dialectically fair and fruitful discussion.

Overall this was worth my time, not only for this subject's great interest but because Penrose explains his thesis, clearly and distinctly.The uprising need for 'popular' science is reflected and adequately satisfied through this lucid book which succinctly presents a contemporary overview in a 'hot' scientific field.

Even non-expert readers (no special background in maths or physics is needed) will be able to follow and admire the ongoing revolution of scientific thought.Given it was written in'97 I'm looking forward and will benevolently embrace another similar work of a splendid thinker such as Penrose

Turned off by the strong AI type point of view of consciousness, yet looking for a scientific explanation, I have repeatedly turned to Penrose's work hoping he would have enlightening ideas. At first, he seems to be on the right track, but when he starts making conclusions, things go awry.
As a biology student, I can say that his understanding of biology seems mediocre at best. And physics may be even worse - in fact his skepticism about the "flatness" of the universe has recently been rendered bascially obsolete.
I feel that the unified brain quantum undulation camp, if you will (penrose, zohar) paint themselves into a frightful corner. For instance, penrose never explains why his microtubule ideas would apply to the brain in particular...we've got oodles of them in every cell in our body! Basically, these ideas try to strike out against the strong AI poing of view, but actually create a new version of it! It's not the neuron construction, they say, instead it's a mechanism even more arbitrarily linked to the brain!
Penrose seems to be a great mathematician...and should stick to that. Still searching for explanations...