Machines Books

The National Gallery of Canada has outdone itself in every way. Kudos to Cazort, Kornell, and Roberts for putting together an astounding book about anatomy in art, a book that is easily comprehensible and not overly bearing. The Drawings and prints reproduced in the book are amazing themselves. The book traces the development of anatomy through the ages. Tough to find a comparable book.

"Ingenium: Five Machines That Changed the World" by Mark Denny is another book in the genre of popular science books about familiar objects.

Denny covers the bow and arrow, waterwheels and windmills, counterpoise siege engines, the pendulum clock anchor escapement and the centrifugal governor.

This might seem an odd selection of machines, given the title of the book, but Denny describes the broader relevance of these machines and their underlying physics to society.

Denny covers the history and development of each machine and follow this with a more detailed discussion of important principles. He starts with a simple model of the physics of each machine to tease out important variables and to learn about the factors influencing efficiency and performance. This allows him to refine the model and make it more realistic.

Such a technique is very useful and should be a part of the toolkit of all engineering and technology students, not to mention their teachers. Modelling a device is an essential first step to understanding its underlying principles.

Denny then uses the insights gained from his mathematical models to illustrate how each machine developed empirically over many years as artisans and craftsmen tinkered with traditional designs to improve them - without the use of mathematics or physics. Such empirical developments led to very efficient designs - often close to optimal when checked with all the mathematical and computer tools available to modern engineers.

Key equations are not derived in the text, but are simply quoted. References to the full derivations (and much else) are given at the end of each Chapter. That is fine for the general reader who may be put off by equations and physics, but the absence of derivations (even in outline) might frustrate mathematical readers.

Website references are given so that readers can see some of the machines (eg clock escapements) in action.

Text boxes are used to give short biographical details of key individuals involved in the development of the machines discussed.

The chapter dealing with clock escapements is particularly good. There is a simple diagram that shows exactly how escapements work. Sufficient, but very simple, equations are presented so that readers can see what factors are important in escapement design and understand some counter-intuitive conclusions (eg a small amount of friction is critical to the stability of pendulum clocks).

The book is a good blend of history, biography and physics. One can enjoy it by reading the non-mathematical text alone. Readers with more physics knowledge will enjoy the additional mathematical challenges.

I liked this book. It is a cut above many of the books in this genre. Many of these books are fairly trivial and padded with marginal material and illustrations to bulk out the text. Thankfully, this book does not suffer from such faults. Definitely worth the money.

In this most fascinating book, the author presents a brief history as well as the basic scientific and engineering principles behind five devices (machines) that have been important in human history. Each chapter ends with a set of references for those interested in pursuing the history, the mathematics and the finer technical points more deeply. The writing style is very friendly, witty and often tongue-in-cheek; it is also very clear and authoritative. The author has endeavored to aim the book at as broad a readership as possible and I believe that he has succeeded admirably, i.e., mathematical formulas are included for clarity and to please the scientifically curious but they are not derived, keeping the math-phobic in mind. So, this book should be accessible to everyone, but will likely be most appreciated by science and engineering buffs. Personally, I found this book very pleasant to read as well as very instructive, but I think that more mathematics, perhaps included in appendices, would have made the book even more fun for those, like me, with a scientific background.

On a long car trip, who could make better traveling companions than the brilliant Inspector Morse and his able assistant Sgt. Lewis? Acorn Audio has faithfully transferred four original episodes of the "Mystery!" television series to audio cassette, with the actual dialogue and musical score intact. Each episode is recorded on two cassettes with a total running time of around two hours. The episodes include "The Ghost in the Machine", "The Infernal Serpent", "Masonic Mysteries", and "Deceived by Flight".

"The Ghost in the Machine" involves Morse and Lewis in the investigation of an art theft which soon becomes a search into the circumstances of the death of a member of the British nobility. "The Infernal Serpent" begins with what appears to be the murder of an Oxford don during a simple mugging incident. By the time the case is concluded, the secrets of the college and its master are exposed. "Deceived by Flight" involves Sgt. Lewis going undercover as a member of a cricket team to solve the murder of one of its players. Finally, in "Masonic Mysteries" Inspector Morse undergoes a series of "trials by fire and water" inspired by the opera "The Magic Flute". Finding who is causing this to happen and why exposes Morse and Lewis to the mind of a twisted genius bent on revenge. The sound quality of the cassettes is excellent and voice over narration is provided to provide the listener with the appropriate visual effects. This is especially critical in "Masonic Mysteries" where much of the story revolves around the actions of the characters.

For fans of the Morse mysteries, listening to these stories on tape is an audio treat. For those who have not experienced the characters on television, here is a golden opportunity to become acquainted with them through the actors that brought them to life.

There is one small critcism of the tape: there are long periods of silence at the end of each side of the cassettes. This was apparently done to spread the episodes out evenly over two tapes. This is minor flaw, however, in an otherwise excellent endeavour. I look forward to additional episodes being made available in this format.

I am allergic to Intarsia. I go out of my way to avoid it. This being said, I knew I needed help and this is the book that did it! Ms. Stuever wrote this book with more detail and clarity than I thought possible with intarsia. She addresses all those odd little occurances that happen with intarsia, such as uneven tension which can happen every other row. The sampler she designed is well thought out to give the knitter all the possiblie scenarios one can come upon while knitting in intarsia. AND, she knit hers in cotton--the least forgiving fiber of all--and it is perfection. If you are unhappy with your results when knitting intarsia, this is the book for you! Read it, make the sampler, and then continue on in confidence!

This book introduces the concepts of infinite refinability, and finite refinability and splits the world apart into those two categories. Nearly everything falls into the category of infinitely refinable, except for digital computers that are by definition finitely refinable. Then it examines the consequences of that, and what it means for artificial intelligence.

The argument is made that intelligence is infinitely refinable, computers are not, therefore computers do not even posses the ability to have intelligence.

Or to put it more concisely, a digital machine at any point in time can be represented by an integer--it's Godel number. (An extremely large, but finite integer.) Since intelligence is not an integer, no digital machine at no point in time can be intelligent.

Then this analysis is applied to the Turing Test, and Searle's example of translating Chinese to Spanish languages, and explains how computers will not accomplish it. (They will attain any arbitrary level set by finite requirements.)

And it analyses different myths about AI. Is playing chess intelligence? Is anthropomorphizing machines intelligence in those machines? Is there some critical-mass of programming that can be achieved that will somehow magically make the machine intelligent beyond that? That's what this book is all about.

This book is extremely well written and laid out. I like the easy explanations and diagrams, as well as the more technical boxes for those who wish to delve deeper. This is a great primer for AI. It should be required reading for those interested in learning about AI, to whet the appetite.

The title of this book is a reflection of both the deep skepticism regarding the existence of intelligent machines and the exaggerations of their mental prowess. The skepticism usually comes from the philosophical community, and ironically, from many researchers in artificial intelligence (AI). The exaggerations find their origin predominantly in Hollywood, with their portrayals of killer robots and machines run amok. But they arise also, and again ironically, from many AI researchers. This usually occurs when new and promising approaches are attempted, and the initial excitement causes some investigators to impute qualities to their machines that are difficult for interested observers to accept sometimes. After a rather sustained period of enthusiasm, one usually finds a period of reflection on just what is being done. After the research is analyzed and understood, it is then usually designated as being a "program" or some other label equally as banal. Skepticism regarding its "intelligence" grows accordingly.

This roller coaster ride of confidence and skepticism is the result of the lack of an effective tool that would discriminate between intelligent and non-intelligent machines. The AI community is certainly aware of this, and this book can be viewed as part of the search tree for this discriminator. Based on only two of the articles that were studied by this reviewer, it is a work that should be perused by anyone interested in the history of artificial intelligence, but especially for those who are working diligently to find a workable definition of machine intelligence. It is fair to say that the book reflects the attitudes of those in the control theory community, which in fact might be its biggest virtue since that field stays close to what can actually be implemented in real machines in the field.

The article entitled "What Makes A Machine Intelligent" by C.W. de Silva, the editor of the book, holds that it is the ability to find approximate solutions to problems that is the characteristic sign of intelligence. Referring to approximation as a "soft" concept, intelligent machines the author says are part of the field of "soft computing", and after a short review of this field he presents a general framework for assessing the intelligence of a machine. Most importantly, he believes that intelligent machines are prevalent in the world today, and find application in the household, electronics, transportation, and manufacturing. Interestingly, the author puts quotation marks around the word `brain' when writing that there are many products that have a "brain." He is thus displaying reluctance (or caution) in claiming that there are machines that can "think". Why not say that machines have a "brain" if they indeed exhibit intelligence? Can a machine exhibit intelligence without a "brain"?

But it is the knowledge base of a machine that enables machine intelligence the author argues, in that manipulation of this knowledge base can result in effective decision-making and inferences. And also crucial to the author's view of intelligence is that it is non-reductionist: at the "atomic" level the intelligence of the machine cannot be detected, much as at the neuronal level one does not observe intelligence in humans. Therefore, he argues, it is the external behavior or characteristics that determines to a large degree whether the machine is to be designated as intelligent. He lists several characteristics that if exhibited by machines would be a sign of their intelligence. One of these includes pattern recognition, and via the use of connectionist networks, this has become a reality. Another is inference from incomplete information, and this has been realized using inductive logic programming. Still another is inference from qualitative or approximate information and this has been achieved with various approaches with qualitative reasoning "software. And machines can now exhibit a fair degree of commonsense, the Cyc project being a prime example of the integration of commonsense into machines. However, there are no machines as of yet that exhibit curiosity and that engage in creative invention, and the author is careful to note this in his article. The construction of machines that exhibit these characteristics is one of the major challenges of twenty-first century AI, but it is the opinion of this reviewer that it will be accomplished within the next two decades.

The general schematic that the author gives for an intelligent machine is interesting in regards to its domain specificity. The author argues that efficiency drives this specificity, and that such intelligent systems are to be favored over general purpose ones. If one is emphasizing applications, it is difficult to argue with this point. For example, if one were interested in a system that would intelligently manage a wide area network, then one would not want this system to engage in chess playing or musical composition, even if it were capable of doing so. Resource allocation and context thus governs to a large extent what kind of intelligent machine is to be deployed. General-purpose intelligent machines however have been the topic of much discussion of late, due to the insistence by many AI researchers that only such a capability should really be designated as intelligent.

One could designate the machine as discussed by the author as a triplet (M, C, K), where M is the `structural system', i.e. the physical hardware and devices, K is the knowledge base, and C is the "brain" that consists of various reasoning strategies acting on K. There is no reason a priori to insist that C and K are tied to an explicit location in the machine. They may indeed be distributed throughout the machine, and so systems such as the Internet could qualify as candidates for intelligent machines. He gives an explicit example of such a machine, namely one that is able to perform intelligent sensor fusion.

A very different and highly interesting outlook on machine intelligence is given in the article "Intelligent Machine to Modify or Adapt Human Behavior" by D.W. Repperger. The author's view on intelligence is unique in that he asserts that if the overall interaction between a human and machine is improved, over and above what can be accomplished when there is no interaction, one can say that the machine expresses intelligence. The machine has thus assisted the human in some way, and caused her to increase her knowledge base or abilities, or in fact induced an adaptation to her behavior. The author gives two examples illustrating his definition of machine intelligence, and interestingly, these examples illustrate the idea that a machine can be viewed as intelligent in one time frame and not in another. The machine must add "value" to the machine/human interaction, and a machine that was able to do this two decades ago may not be able to do it today. The utility of the human/machine interaction is therefore time-dependent and either may increase in time, if the machine evolves its knowledge base or the sophistication of its reasoning strategies, or decrease with time, this happening when the machine is unable to keep up with ever-increasing knowledge or whose reasoning strategies prove impotent when coping with contemporary problems.

The author then outlines a possible taxonomy for machine intelligence, requiring first that any measure of machine intelligence must have a corresponding analogy in humans or animals. This requirement is somewhat restrictive, since highly advanced machines might formulate new languages or methodologies to communicate among themselves, which may have no analog in human or animal communication. But it would be difficult to recognize when this did happen however, at least from the standpoint of humans, and so the author's requirement does carry practical weight. Another requirement is that when a human is given a certain task, it is better able to complete that task when interacting with the machine in question. Also, a machine may express intelligence for one task but not another. For example, a machine could manage a network quite superbly but fail miserably at chess. Fourthly, intelligence measures must always be relative, not absolute, i.e. whether a machine is intelligent depends on the context and historical epoch in which it is embedded. A machine could be unintelligent a few years ago but be intelligent today, due to its ability to evolve and meet new challenges, or conversely, due to disruptions in human societies that require them to use machines that they used to regard as practically useless. Lastly, the author believes that a quantitative measure of machine intelligence should be related to information theory, such as channel capacity. As an example of an intelligent machine, the author discusses a system that detects tracking error for jet pilots.

As a researcher in the design-manufacturing integration field, I found this book pretty helpful to learn about past research. This book is written by many experts. Though the progress of research in the design and manufacturing`s field is pretty fast, I think this book will survive as a good reference book for students and researchers.

Kearns is an impressive researcher, precise and succinct. The material on this book follows a tradition of careful proofs of fundamental issues in learning. I wouldn't think this is material of practical use; for that kind of material I'd recommend the new edition of Duda. Rather, Kearns is one of a team of researchers pushing the frontier of proving what is learnable and what is not, why some representations are good for learning and which are not, the dimensionality of the target problem (related to overfitting) working with prinpled definitions of what it is meant to learn borrowed from computational complexity theory.

This book is a very clear exposition on how tu use formal techniques like Z. It is both rigorous and clear. First the mathematical theory is introduced in a rigorous way and then it is explained how these techniques are applied in specifying formal models. The examples are also very good : small and clear, but containing enough information to learn the stuff..