Artificial Intelligence Books

This volume combines Sladek's acclaimed novel Roderick with its sequel Roderick at Random. The first book is a darkly comic fantasy about a learning machine named Roderick, and his unusual upbringing. Wrested from his creators at too early an age, Roderick finds himself habitually used and abused by everyone he encounters, but still manages to take it all in stride. The tone of the story makes it stand out among a genre full of artificial intelligences, but the most endearing facet of the book is Roderick himself, who unlike many fictional robots, is very childlike in his limited knowledge-base. Like a mechanical Forrest Gump, Roderick does what he's told because he doesn't have any better ideas, and doesn't know enough to pass moral judgments on those who are giving the orders. The author slyly uses this device to poke ironic fun at many of the sillier aspects of 20th Century America, allowing us to see the effect our culture's messages have on one who accepts everything at face value. At the same time, Sladek clearly has little compassion for human foibles; the people Roderick encounters often represent the worst humanity has to offer, although most of them are very familiar types nonetheless. Even Ma and Pa Wood, who care for Roderick more than anyone, don't seem to really understand him. The story might have been a lot funnier if the reader didn't feel so bad for poor Roderick. As it is, he remains a tragicomic figure, and the novel is slightly less successful for this duality.

The second novel, Roderick at Random, has less to offer in the way of interesting ideas, and is neither as funny nor as melancholy. Ultimately, it's more adventures without any real focus - a typical sequel. For those who just can't get enough of Roderick, this should be a decent value. For those who felt that the original novel was a touch overlong to begin with, perhaps the Complete Roderick is unnecessary.

John T. Sladek made his reputation in the SF community writing clever satires and parodies with the emphasis on AI and Robotics. "Roderick" is the life story of one such robot, and like Sladek's other masterful robot tale "Tik Tok", it involves a huge cast of characters and all kinds of twists and turns.

What sets this apart is the tone. Sladek has stepped away from the black comedy and created a melancholy epic. This is his middle-age novel. Darker and more personal than most of his other work ("Bugs" being the exception), "Roderick" is about growing up, disillusionment, and self-realisation. It's still funny, but it's hard to tell who you're laughing at.

I've read it twice, and each time I was sorry when it ended.

This is a classic for anyone interested in machine learning, data mining or predictive statistics. Though it is beginning to age, it covers essential aspects of empirical modeling still not covered by many more recent titles (!). A subsequent effort by one of the authors, "Predictive Data Mining" is a bit more current though shorter on the fundamentals.

This book gives a good cohesive introduction to the basic algorithms from Statistics, Machine Learning and Pattern Recognition research. These include Nearest Neighbor, Decision Trees, Bayesian Networks, and Neural Networks.

The main value of the book however is its coverage of techniques that 1) estimate a model's accuracy, and 2) select a 'good' model. This book offers the reader a solid foundation to what we are trying to achieve: to get at the objective truth.

Nardi has edited a provocative compendium of theoretical expositions and methodological examples for the application of activity theory in the design of collaborative computing systems. In many ways, this book was ahead of its time. It's importance to the emerging disciplines of interaction design and design based research should not be underestimated.

One of my favorite books.

This is a mind blowing collection of papers that would interest any enthusiast of educational frameworks/theories. It clearly states the implications of activity theory to instructional design/architecture, knowledge management and all that fun stuff. The first couple of papers, by Nardi, Kuutiti and others, are particularly provocative.

The nature and origins of creativity is of great interest in the field of human psychology, and research on creativity has occupied the time of many individuals. This book, written by an individual well recognized in the field, gives a fascinating account of her ideas on creativity, and has been a major influence for those attempting to realize creativity in non-human machines. Throughout its pages, the author attempts not only to define creativity, but also to give criteria for distinguishing new ideas that are creative from simply new ideas. The most important goal of the book though is to frame the nature of creativity in a computational paradigm.

Philosophers, writers, artists, musicians, and others in the humanities will perhaps frown on the author's project to use computational concepts to assist in the understanding of human creativity. Many of those who work in these professions will point to "intuition" as being the source of creative ideas and works, regardless of their vague understanding of this term. The author demands, rightfully, a clarification of the notion of intuition, but she does not seek to do this herself in the book, but instead looks to the computational paradigm to settle the issue of human and machine creativity. She argues at length throughout the book that not only can this paradigm assist in the understanding of human creativity, but that non-human machines can appear to be creative and can also recognize creativity. The question as to whether non-human machines really are creative is also addressed towards the end of book.

Having a computational model of creativity would allow the design of machines that could produce novel scientific theories, musical compositions, architectural designs, and works of art. It would also give those working in the cognitive sciences greater insight into the understanding of the mechanisms involved in human creative thinking, such as the ability to make analogies, formulate complicated mathematical ideas, or classify patterns. In addition, having a generic computational model of creativity would give insight on how to create novel ideas in a manner that is independent of the domain or context in which one is working. Machines could thus be built that would be able to generate creative ideas in many different domains. For example, a machine that generates new mathematical ideas would also be able to create interesting works of art. This would be a major advance in machine intelligence if such machines could be built.

In the book, the author introduces two notions of creativity that she feels is necessary for her study. One of these is called 'P-creativity', and is a personal and psychological notion. The other is called 'H-creativity' and is a historical notion. Given an idea that arises in a particular person, this idea would be called P-creative if the person could not have had it before. It is irrelevant how many other people already have the same idea. An idea is called H-creative if it is P-creative and no one in human history has ever had it before. As can be seen from the definition, it would be difficult to check whether an idea is really H-creative, since it would require quite an extensive statistical survey. But such a survey would be unnecessary, for the author contends that there is no theory that can explain solely H-creative ideas, but that since H-creative ideas are also P-creative, an explanation of P-creative ideas would also apply to H-creative ideas.

According to the author, the ability to distinguish first-time novelty from genuine originality involves an understanding of what generative processes were involved in the production of the idea. A "novel" idea is one that can be described or produced by the same set of generative rules as other familiar ideas, whereas a genuinely original idea is one that cannot. Therefore, the designation of ideas as "genuinely creative" is always done relative to a specific generative system.

Conceptual spaces, in the author's view, are styles of thinking, and creativity must be thought of in terms of the mapping, exploration, and transformation of conceptual spaces. Conceptual spaces are used to organize and give structure to a particular domain of interest, such as checkers game or a Bach concerto. The structure of a conceptual space can be mapped by forming mental representations of it, and these maps can be use to explore them and to change them if necessary. As one example of this, the author quotes the investigations of tonal harmony in post-Renaissance Western music. The dropping of the home-key constraint in tonal music transformed the conceptual space of tonal music to create the conceptual space of atonal music. The deliberate dropping of a constraint is one method by which conceptual spaces can be generated. Another method is to negate a constraint. The author illustrates this method with a discussion of the discovery of the benzene ring due to Friederich Kekule.

A highly interesting and totally unexpected discussion in the book involves the connection of unpredictability with creativity. Since H-creative ideas are ones that have not appeared before in human history, they are unpredicted. The author emphasizes though that this does not mean that H-creative ideas are unpredictable. The role of serendipity, chance, and coincidences in creative thinking are discussed in terms of the computational paradigm. The author concludes from this discussion that creativity cannot be due to chance alone, but that chance with judgment can. The author also discusses three different notions of randomness, with the intent of shedding light on the belief that determinism is incompatible with creativity. These three notions are labeled 'absolute', 'explanatory', and 'relative'. The first is total absence of any order or structure within the domain; the second the lack of any explanation or cause, and the third lack of any order relevant to some specific consideration. Human creativity is relatively unpredictable the author asserts, and it is easy to believe this considering our folklore understanding of it. The computational paradigm of the author has assisted our understanding of it, but more research is needed.

The idea of seeking to better understand human creativity through comparing it to certain kinds of computer operation , I must admit, seemed to me , at the outset , less than exciting. This is not to say that the Boden approach of exploring conceptual spaces in the mind, transforming them through computational concepts drawn from artificial intelligence, and through this revealing certain insights into human creativity- is totally barren or meaningless. I myself do not really feel I have the qualification to say, whether some of these insights might be useful or not.

What I can say , is that this kind of analysis seems to me , not really relevant to understanding the kinds of literary creation I know a certain amount about. Consider the following lines of poetry, or for that matter any lines of poetry. " Shall I compare thee to a summer day/ Thou art more lovely and more temperature/ Rough winds do shake the darling buds of May/ And Summer's lease hath all too short a date/ ......

The language of poetry cannot really be translated into mathematical language, whether it is gobbyldegook or not. Nor I would argue can the language of any kind of real "Literature"

The kind of explanations given for Creativity in Literary work here seem to me to be ' reductions' to a 'lower level'. The complexities and ambiguities of literary language, the vast hinting and suggesting which move much poetry it seems to me, are wholly out of keeping with a clear mathematical language , analogue or digital.

To put this another way, this book does not give me 'the feeling' that the kinds of creativity I appreciate are even touched upon by its concepts.

If you're looking for a book that uses a non-technical approach to address really difficult issues, this is one you must read. Anyone that is starting a new project or contemplating a major update should look at these lessons carefully.

I read this book and believe it is very useful in providing a review of the strategies and application of Electronic Commerce and the Internet phenomen into the modern business environment.

It also analyses the benefits of Intranets and Extranets. There are some very good tables and diagrams which summarise information neatly. Definitely worth reading for any business person or student of business.

Although (necessarily) brief, this is a good overview of data mining techniques that can be applied to bioinformatics. Some of the text could have benefited from a bit more review by an editor, but the book is highly readable and usable. As there are few such books as far as I know, Mitra and Acharya are to be commended for doing a nice job.

This book is a distinct addition to the field of data mining in its coverage of topics and ideas.
The good point about this book is that it explains the central concepts in great details with the required rigour and also puts in examples to make them understandable. Its not a typical Theorem , Lemma book , but also do not shy away from the mathematical complexities.
There is adequate coverage of all the standard topics and additionally there are interesting sections on Rough sets and Fuzzy sets, which are generally not available everywhere.
The most important point about this book is that it gives a thorough coverage of Multimedia data and web data and biological data, which is very dificult to get in one place in any book.
A researcher or anyone with adequate background in Mathematics will find this book very helpful in their research.
References are also pretty much exhaustive.
I would have liked to see some more coverage on datawarehouse and complexity analysis of the algorithms mentioned, also some language level implementation examples at least in some appendix to the book. Also a paperback edition is needed as the price is quite high.

This is the best way to invest in markets in the present! Using the neural technology to teach your PC investing by you. You only need to be a good teacher. This book will help you for sure...

This book contains 33 articles some of which discuss using Neural Network models to predict the direction of the Stock Market on a short term basis (see table of contents on hardcover version). For those of you who are targeting long term results exclusively, you may want to look elsewhere. What I liked about the book, is that the articles are very specific, and they follow the format of A. Here is my idea for a model, B. This is how I tested it, and C. Here are my results, with plots. Some of the book is easy reading while other articles are more difficult and require some knowledge of calculus and probability. Most of the articles assume some knowedge of statistics. The writers and editors are well known in their field. Some are professors from business schools, others are from investment comapanies, and some are have an engineering or physics background. No lightweights from what I can tell.

If you are serious about modeling the market using your computer (and making money on your investments) then this book may help you do that. It is a serious book for academically oriented individuals. It has none of the fluff usually contained in consumer targeted books (aka - get rich tomorrow with my new stock picking plan...). One thing I didn't like about it is the smaller print on some of the articles, but this was not a big factor in my decision to buy it. When I saw this book it only took me a few minutes to decide that I was not leaving the store without it.

This book is a must read for anyone who is serious about the future of Robotics and how these biomechanical devices will interact with us.

The book is well structured, and includes many photos and diagrams. I found it useful because I work exactly on the same topic. However, most of its content can be found elsewhere (in published articles). The accompanying CD-ROM includes very interesting videos, although I think they can also be found elsewhere. In summay, I definitely recommend this book, but only if you are really interested in the topic and/or in Kismet, and you want to know as many details as you can.

First, this 'edition' listed in Amazon as 2005 seems to be identical to the edition I have listed in Lib of Congress as 1998-
The Table of Contents and Index are identical down to the page numbers.

Nonetheless it is a vary theoretical analysis of potential models which relate to DNA computing as Formal Language Theory models and the proofs of various capabilities of these languages which might be implementable with DNA techniques. This is not for the faint of heart or lay reader, ie not a mathematical/computing theorist.

It is an excellent start if this is your perspective.

Chapters 1 & 2 make for interesting and informative reading. The rest of the book reads like a calculus book and is obviously meant only for the most technically literate: you should know (and like) Chomsky grammars and want to learn about their (mathematical) relationship with DNA bases.

Topics include: Sticker Systems, Watson-Crick Finite Automata, Splicing Systems, Universality by Finite H Systems, and Splicing Circular Strings

This is the book that that spawned the PBS series "The Machine that Changed the World" airing in America around 1992. It was a five part series that closely matched this book, on which it was based. With this book you will gain an appreciation for the history of the modern computer and get an excellent historical perspective. The 5 part TV series with episodes taken from chapters of this book were titled:

1) Great Brains
2) Inventing the Future
3) The Paperback Computer
3) The Thinking Machine
4) The World At Your Fingertips

The book was written before the the invention of the internet and the world wide web, but you can see it coming. Industry giants such as the Alan Kay, Doug Lenat, Steve Jobs, Steve Wozniak, Alan Touring, Robert Noyce, Charles Babbage, George Boole, Jon Von Neumann and many others are all mentioned and their contributions are put into a modern (for the day) perspective.

I would recommend this book to any student of computer science or computers as a hobby. Though the book is well over 15 years old, it has a timeless quality that takes you back to the beginning of the computer age and leads you gently into the modern era.

The Dream Machine provides a plethora of information to the reader. It gives specific detials of the evolution of the computer. Including: many people, companies (IBM, Remington Rand), the languages of programming, the personal computer and more...