The Artificial Intelligence Handbook: Business Applications Review

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The Artificial Intelligence Handbook: Business Applications ReviewThe authors write for an educated person who wants a good idea of how artificial intelligence is currently applied in industry. In part, this might be because you are aware that AI has been studied intensively in academia for decades. But you are interested in practical and successful deployments.
In this sense, the book is quite optimistic. It explains the mainstays of AI. Along the way, we meet such things as the Turing Test to see if a machine can mimic a human. Also, we see MIT's Eliza. Famed since the 70s, despite its primitive text interface.
For current applications, the authors give a good survey of the field. From manufacturing case studies to finance. Like discerning good credit risks. The book also discusses neural nets, which are sometimes seen as a different and competing approach to AI.The Artificial Intelligence Handbook: Business Applications OverviewThe purpose of this book is to help business professionals understand artificial intelligence software and how to make practical use of it. The authors provide a complete overview of expert systems and neural networks. Applications in a variety of discipline are included such as: banking, insurance, investments, accounting, law, marketing, and manufacturing.

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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) Review

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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) ReviewWhat strikes me each time I open this book is Mr Kecman's sense of pedagogy: it is a lesson in the matter. Not only his book delivers the - sometimes complex - techniques in a highly readable manner, but the concepts behind each of the main tools (SVM, NN & FL) he chose to highlight are always brilliantly put in context. One comes out of the reading with more than a set of equations but rather with a clearer picture of the field.
Mr Kecman is - without a doubt - a great teacher.
This effort to deliver a clear message is furthermore underlined through the numerous original figures: if you are like me and feel that a (good) picture speaks more than a thousand words, you will sure appreciate the way the illustrations complement the text and truly help the understanding.
I have read several other books on the subject but if I had to chose one for teaching purposes, this would be the one. I you want to build a better understanding of the field, get this book: it will pay on the long term.Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) OverviewThis textbook provides a thorough introduction to the field of learningfrom experimental data and soft computing. Support vector machines (SVM) and neuralnetworks (NN) are the mathematical structures, or models, that underlie learning,while fuzzy logic systems (FLS) enable us to embed structured human knowledge intoworkable algorithms. The book assumes that it is not only useful, but necessary, totreat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory andalgorithms are illustrated by practical examples, as well as by problem sets andsimulated experiments. This approach enables the reader to develop SVM, NN, and FLSin addition to understanding them. The book also presents three case studies: onNN-based control, financial time series analysis, and computer graphics. A solutionsmanual and all of the MATLAB programs needed for the simulated experiments areavailable.

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Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. First Edition (Advanced Information and Knowledge Processing) Review

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Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. First Edition (Advanced Information and Knowledge Processing) ReviewThe word `ontology' is usually associated with philosophical speculation on the reality of things, and if one checks the literature on philosophy one will find a diverse number of opinions on this reality. Engineers and scientists typically view philosophical musings on any topic as being impractical, and indulging oneself in these musings will cause one to lose sight of the topic or problem at hand. Rather than simplify the problem and make it understandable, philosophy tends in most cases to complicate it by endless debate on definitions and the use of sophisticated rhetoric that seems to have no bearing on the problem at hand. The conceptual spaces generated by these debates can become gigantic and therefore unwieldy, thus making the problem appear more complex than it actually is.
In the information age however, ontology has become a word that has taken on enormous practical significance. Business and scientific research are both areas that have increasingly relied on information technology not only to organize information but also to analyze data and make accurate predictions. In addition, financial constraints have forced many businesses to automate most of their internal processes, and this automation has brought about its own unique challenges. This push to automation usually involves being able to differentiate one thing from another, or one collection of data from another, or one concept from another. Thus one needs to think about questions of ontology, and this (very practical) need has brought about the rise of the field of `ontological engineering', which is the topic of this book.
The authors have given a good general overview of the different approaches to the creation of ontologies. There are many of them, some of which seem "natural", while others seem more esoteric. The reader though will obtain an objective discussion of the ontologies that the authors chose to include in the book. Discussions of the ones that are not included can readily be found on the Internet.
Given the plethora of ontologies that have been invented, it would be of interest to the ontological engineer to find common ground between them. The re-use of a particular ontology may be stymied by the different ontological commitments it is adhering to or it's actual content. In order to use it, it must therefore be "re-engineered". The authors discuss this prospect in the book, and define `ontological re-engineering' as the process where a conceptual model of an implemented ontology is transformed into one that is more suitable. The code in which the ontology is written is first reverse engineered, and then the conceptual model is reorganized into the new one. The new conceptual model is then implemented.
Also discussed in the book, and of enormous practical interest, is the automation of the ontology building process. Called `ontology learning' by the authors, they discuss a few of the ways in which this could take place. One of these methods concerns ontology learning using a `corpus of texts', and involves being able to distinguish between the `linguistic' and `conceptual' levels. Knowledge at the linguistic level is described in linguistic terms, while at the conceptual level in terms of concepts and the relations between them. Ontology learning is thus dependent on how the linguistic structures are exemplified in the conceptual level. Relations at the conceptual level for example could be extracted from sequences of words in the text that conform to a certain pattern. Another method comes from data mining and involves the use of association rules to find relations between concepts. The authors discuss two well-known methods for ontology learning from texts. Both of these methods are interesting in that they can apparently learn in contexts or environments that are not domain-specific. Being able to learn over different domains is very important from the standpoint of the artificial intelligence community and these methods are a step in that direction. The processes of `alignment', `merging', and `cooperative construction' of ontologies that are discussed in the book are also of great interest in artificial intelligence, since they too will be of assistance in the attempt to design a machine that can reason over multiple domains.
The ontologies that are actually built are of course not unique. This results in a kind of semantic or cognitive relativism between the environments that might be built on different ontologies, even in the same domain. Merging and alignment both address this relativism, along with other techniques that are discussed in the book. The selection of the actual language that is used to create an ontology is also somewhat arbitrary. The authors devote a fair amount of space in the book to the different languages that have been used to build ontologies. Through an elementary example, they discuss eleven different languages, namely KIF, Ontolingua, LOOM, OCML, Flogic, SHOE, XOL, RDF(S), OIL, DAML+OIL, and OWL. The choice of a language is dictated by what one is seeking in terms of `expressiveness' and what kind of reasoning patterns are to be deployed when using the ontology. The authors point to a tradeoff between the expressive power of the language and the reasoning patterns that are attached to the language. The expressiveness of a language is directly proportional to the complexity of the reasoning patterns that are used.
Ontological engineering as it presently exists is still carried out by a human engineer. To create an ontology every time from scratch would be tedious, and so it is no surprise that tools were invented to make ontology creation more straightforward. Some of these tools are discussed in the book, such as KAON, OilEd, Ontolingua, OntoSaurus, Protege-2000, WebODE, and WebOnto, along with assessments as to their utility. The discussion is helpful for newcomers to ontological engineering who need guidance as to what direction to take. The automation of ontology building would of course be a major advance. To accomplish this however would require that the machine be able to simultaneously and recursively construct the knowledge base and reason over it effectively. This is a formidable challenge indeed.Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. First Edition (Advanced Information and Knowledge Processing) OverviewOntologies provide a common vocabulary of an area and define, with different levels of formality, the meaning of the terms and the relationships between them. Ontological engineering refers to the set of activities concerning the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. During the last decade, increasing attention has been focused on ontologies. Ontologies are now widely used in knowledge engineering, artificial intelligence and computer science; in applications related to areas such as knowledge management, natural language processing, e-commerce, intelligent information integration, bio-informatics, education; and in new emerging fields like the semantic web. The book presents the major issues of ontological engineering and describes the most outstanding ontologies currently available. It covers the practical aspects of selecting and applying methodologies, languages, and tools for building ontologies. "Ontological Engineering" will be of great value to students and researchers, and to developers who want to integrate ontologies in their information systems.

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Practical Risk Assessment for Project Management Review

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Practical Risk Assessment for Project Management ReviewThe ideal audience for this book is relatively narrow, consisting of readers who meet the following two criteria: (1) new to risk management techniques, and (2) using Palisade @Risk. In fact, this book comes close to being a tutorial on using @Risk, which is a popular commercial product that works within Microsoft Excel and project management programs.
If you are new to advanced techniques in project risk management you'll like the way the author succinctly covers all of the key elements of project risk assessment, project finance forecasting, and simulation and modeling. Considering the complexity of the subject area, and the fact that both probability and simulation techniques are covered, the author does a remarkable job of conveying the wide range of topics in a scant 134 pages. I especially liked the generous use of graphs and examples, and the way each topic was broken down into easy-to-grasp facts and steps.
However, even without @Risk you can learn much about risk assessment from this book, including a refresher on probability distributions, how to perform an assessment using manual techniques, and modeling and simulation with an emphasis on Monte Carlo simulation.
The only problem I have with this book is that it's out of date with respect to @Risk, which has evolved into a much more capable tool since this book was first published 7 years ago. Since one of the this book's strengths is the way it teaches how to apply @Risk to real world project risk assessment, the fact that it's out of date with respect to the software version diminishes its value.Practical Risk Assessment for Project Management OverviewIn the estimating, planning and management of any project, large or small, an understanding of the impact of risk is critical. This book explains how the growing number of people choosing to or forced to organise their work as projects can make realistic assessments of the uncertainty affecting costs, timescale and revenue, before commitments are made. A clear analysis of the role of uncertainty is combined in this concise and practical handbook with simple, cost-effective techniques for measuring and modelling the overall risk to a project's budget and schedule. There is advice and help here for the whole project team, including project managers; bid managers; project sales professionals; planners; estimators; managers running a project-based business; and consultants and auditors advising a projects business. Drawn from the author's extensive experience on projects ranging in scale from a few man-months to hundreds of man-years, the book will beelevant to anyone involved in a project-based business. Examples are presented as simple models, built in spreadsheets using the @Risk software package. No more than basic knowledge of Lotus 1-2-3?? or Excel?? will be required by the reader.

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AI Game Programming Wisdom 4 (AI Game Programming Wisdom (W/CD)) Review

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AI Game Programming Wisdom 4 (AI Game Programming Wisdom (W/CD)) ReviewThis is a great reference if you're in need of architectural or conceptual advice regarding AIs. It's not "learn AI in 24 hours" type of book, the reader is assumed to posses substantial knowledge of programming, as implementations aren't usually explained - this is a good thing, because it means there's more pure knowledge inside. (There's a CD with implementations and some sources.)AI Game Programming Wisdom 4 (AI Game Programming Wisdom (W/CD)) OverviewWelcome to the latest volume of AI Game Programming Wisdom! AI Game Programming Wisdom 4 includes a collection of more than 50 new articles featuring cutting-edge techniques, algorithms, and architectures written by industry professionals for use in commercial game development. Organized into 7 sections, this comprehensive volume explores every important aspect of AI programming to help you develop and expand your own personal AI toolbox. You'll find ready-to-use ideas, algorithms, and code in all key AI areas including general wisdom, scripting and dialogue, movement and pathfinding, architecture, tactics and planning, genre specific, and learning and adaptation. New to this volume are articles on recent advances in realistic agent, squad, and vehicle movement, as well as dynamically changing terrain, as exemplified in such popular games as Company of Heroes.You'll also find information on planning as a key game architecture, as well as important new advances in learning algorithms and player modeling. AI Game Programming Wisdom 4 features coverage of multiprocessor architectures, Bayesian networks, planning architectures, conversational AI, reinforcement learning, and player modeling.These valuable and innovative insights and issues offer the possibility of new game AI experiences and will undoubtedly contribute to taking the games of tomorrow to the next level.

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Connections: Patterns of Discovery Review

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Connections: Patterns of Discovery ReviewI found this book to be entertaining, informative, and insightful. It covers a great deal of information technology, history, and draws relationships between many different aspects of inventing across various fields of computer science. By identifying patterns in innovations, it shows how major trends developed for information processing over the past half century. In addition, it describes how some of these trends might progress over the next decade.
The book follows the connections of information flow to highlight search technology. It follows the connections of microchips in building computers, networks, and small wireless devices. Then it connects all these inventions through ubiquitous computing and a ubiquitous Web.
Like the TV-Series hosted by science historian Jake Burke, the book links ideas, innovations, and inventors to build stories about trends and interrelationships. The Information Age's innovations from vacuum tubes to transistors, from computers to networking, and simple programming to intelligent software are presented and followed by forecasts for their continued development.Connections: Patterns of Discovery Overview"In their fascinating analysis of the recent history of information technology, H. Peter Alesso and Craig F. Smith reveal the patterns in discovery and innovation that have brought us to the present tipping point. . . .
A generation from now, every individual will have personally tailored access to the whole of knowledge . . . the sooner we all begin to think about how we got here, and where we're going, the better. This exciting book is an essential
first step."—From the Foreword by James Burke
Many people envision scientists as dispassionate characters who slavishly repeat experiments until "eureka"—something unexpected happens. Actually, there is a great deal more to the story of scientific discovery, but seeing "the big picture" is not easy. Connections: Patterns of Discovery uses the primary tools of forecasting and three archetypal patterns of discovery—Serendipity, Proof of Principle, and 1% Inspiration and 99% Perspiration—to discern relationships of past developments and synthesize a cohesive and compelling vision for the future. It challenges readers to think of the consequences of extrapolating trends, such as Moore's Law, to either reach real machine intelligence or retrench in the face of physical limitations. From this perspective,the book draws "the big picture" for the Information Revolution's innovations in chips, devices, software, and networks.
With a Foreword by James Burke and bursting with fascinating detail throughout, Connections: Patterns of Discovery is a must-read for computer scientists, technologists, programmers, hardware and software developers, students, and anyone with an interest in tech-savvy topics.

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Thinking on the Web: Berners-Lee, Gödel and Turing Review

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Thinking on the Web: Berners-Lee, Gödel and Turing ReviewAlthough the target audience for this book is most likely comprised of computer science students, those well versed in computer science, IT type professionals and anyone with a vested interest in remaining on the leading edge of Web capabilities, it is my opinion that even a casual reader will benefit from reading this book. Because this book makes one aware of the current Web limitations and describes how it could be significantly more than what it is today and then launch us into the real Information Revolution. Yes, according to the authors we have not yet experienced the full Information Revolution.
This book makes you think about thinking or at least the thinking process as it relates to instilling the Web with enough artificial intelligence (AI) to make it capable of thinking. I learned from this book that the Web, as it is currently structured, it not really very intelligent at all and there are many enhancements that have to be made to bring the Web to its full potential. Those who are in any way interested in the Web achieving its full potential will be well served by reading this book.
The authors take on a sizable task and do an excellent job of interweaving the philosophical with the technical aspects of AI as a driver and/or incremental part of enabling the Web to "think". The authors start from the beginning and bring us up to the current status of web thinking. The beginning here is literally from Aristotle and along the way they spend considerable time laying a foundation that includes the significant contributions of Berners-Lee, Gödel, and Turing. After the first part of the book establishes the foundation, the second part of the book becomes very technical (as you would expect) focusing on Web ontology and logic and a lot more to address the complex superstructure that will be required to establish thinking on the Web.
One aspect of this book that I found refreshing and I believe unique for a technical book are the interludes at the end of each chapter. These interludes are a running interaction/dialogue between two computer science students as they debate/discuss the feasibility of using AI applications, etc. to make the Web capable of thinking. These interludes are refreshing to read and give a real life perspective of how daunting the task is to make thinking on the Web possible. And, indeed will we all ever agree on what thinking on the Web really means and if it is ever fully achieved? My opinion after reading this book is that there will probably not ever be a unanimous agreement. Of course, you will have to judge for yourself.
I gave this book five stars because I really learned a lot, and some of what I learned was more than I bargained for, a real surprise. The authors did a thorough job, and the book stimulates a lot of thinking about something we take for granted --- and that is thinking. Enjoy the book and when you read it, expect to be challenged.
Thinking on the Web: Berners-Lee, Gödel and Turing Overview
Provides valuable insight into the progress and direction of development of the World Wide Web and its likely future applications in science and business.
Reviews the prospects for the Web to develop intelligent services (such as online businesses, games, purchases, new search capabilities, and accessibility to trustworthy information).
Offers a view for thinking about thinking on the Web.
The companion website offers access to supplemental text, specialized information, additional examples, demos, tools reference material, and advanced applications.


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A Semantic Web Primer (Cooperative Information Systems) Review

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A Semantic Web Primer (Cooperative Information Systems) ReviewReaders will need a basic understanding of formal logic in order to get the most from this book. Also realize that some material, such as the discussion and presentation of monotonic and non-monotonic rules are still hotly contested in the semantic web community.
This book starts out with an excellent introduction in Chapter 1, titled "The Semantic Web Vision". It next begins building towards the basic elements of a semantic web by starting in familiar territory - structured web documents in XML. Many readers will be intimately familiar with this material, but I recommend reading it because the authors lay a solid foundation for subsequent chapters here.
The components and concepts of the topic are then covered in chapters devoted to:
- Describing Web Resources in RDF, which includes basic ideas, XML-based syntax, schema, and querying.
- Web Ontology Language (OWL), which introduces the OWL language, examples and future extensions. Appendix A contains Abstract OWL syntax, which augments this chapter.
- Logic and Inference, covers monotonic and non-monotonic rules, syntax, rule mark-up in XML and examples. This chapter will require an understanding of formal logic, and I also recommend additional research on the web regarding the debate about using non-monotonic rules, which has highly vocal proponents and detractors.
- Applications, a chapter of case studies from real companies, including Audi, and material on how semantic web concepts can be applied to E-learning, web services and other scenarios.
- Ontology Engineering (ontology is synonymous with taxonomy) using manual and semi-automatic methods. There is also an excellent discussion about reuse.
The web site that supports this book is rich in content that will not only augment the book, but greatly expand it. Each chapter has an associated page on the site containing PowerPoint presentations, PDF documents, and other material. The site also has a section for errata, problems and quizs if you are basing a course on this book, and additional links to resources related to the material in the book.A Semantic Web Primer (Cooperative Information Systems) OverviewThe development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its use. A Semantic Web Primer provides an introduction and guide to this emerging field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for self-study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own. It includes exercises, project descriptions, and annotated references to relevant online materials. A Semantic Web Primer is the only available book on the Semantic Web to include a systematic treatment of the different languages (XML, RDF, OWL, and rules) and technologies (explicit metadata, ontologies, and logic and inference) that are central to Semantic Web development. The book also examines such crucial related topics as ontology engineering and application scenarios.After an introductory chapter, topics covered in succeeding chapters include XML and related technologies that support semantic interoperability; RDF and RDF Schema, the standard data model for machine-processable semantics; and OWL, the W3C-approved standard for a Web ontology language more extensive than RDF Schema; rules, both monotonic and nonmonotonic, in the framework of the Semantic Web; selected application domains and how the Semantic Web would benefit them; the development of ontology-based systems; and current debates on key issues and predictions for the future.

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Nerds on Wall Street: Math, Machines and Wired Markets Review

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Nerds on Wall Street: Math, Machines and Wired Markets ReviewWith all due respect to the previous Amazon reviewers, it's hard to believe they both (a) read this book and (b) have any familiarity with Wall Street technology. The book is a collection of articles written for technology magazines from the mid-80s to the mid-90s. Even within an article entire paragraphs are repeated, and the same idea in more or less the same words can often be found a dozen times or more in the book. This is interspersed with apparently random cut-and-pastes from the Internet and lots of tiny black-and-white pictures which the author tells you are only meaningful with color and animation. You get the feeling the author cleaned out his desk, and decided to make some money from the stuff he didn't want anymore.
There is some useful information in here, and the author does know a lot about automated equity trading before the advances of the late 90s. The trouble is it's not presented in coherent sequence and the technical level is too uneven. For example, it is asserted five separate times that garbage collection is a problem for LISP, without any background material. Anyone who knows what garbage collection means in this context, or has worked with LISP, already knows this and will get annoyed at even the second repetition. Anyone without that background will find the repeated explanations meaningless. There is nowhere near enough technical information for nerds who want to understand Wall Street (or the Wall Street of 20 years ago) or Wall Streeters who want to understand nerds, but there is far too much unexplained jargon for non-technical readers.
Another complaint is the author makes significant errors when he steps beyond his expertise, which is often. For example, he claims if you have 1,000 statistical results significant at the 5% level, 50 of them will be false. The correct statement is if you test 1,000 rules with no predictive value, you expect 50 of them to show significance at the 5% level. The number of your significant results that are false depends whether you start with rules that are mostly useful, or mostly random. This is the key insight to the concept of data mining, the author's misunderstanding makes his chapter on the subject misleading.
Another error is the claim that futures markets were developed to allow farmers to lock in prices. This is false historically (no farmers were involved in the creation of futures markets, farmers have never been big participants and have often tried to have them shut down, when farmers do transact it is much more often to double up their bets by buying the crop they grow than it is to hedge) and anyone who believes it misunderstands the economic function of futures. That's dangerous if you also have a computer that can send trades to financial exchanges. Professionally, the author stuck to equities so it didn't matter to him, but it could matter to his readers if they rely on his account.
There is one up-to-date section at the end, which the author admits was tacked on to make the book more relevant, even though he knows nothing about the topic. His angry rant about the current financial crisis appears to be constructed from reading the first paragraphs of other people's rants. He relies almost exclusively on quotes from politicians, senior regulators and bank CEOs, who all agree it was the nerds' fault. He condemns "complex and opaque" techniques in strong language and great lengths. This from a guy who built black-box trading systems. While it's true there can be a long path between a mortgage dollar a borrower sends in (or, more to the point, doesn't send in) and the end investor, and there can be matches from phantom securities along the way, all of this is done by clear rules which are disclosed. You don't really know what a black box program will do until you turn it on, and its workings are never made public. I'm not defending synthetic CDO-squareds, I'm just pointing out opinions on complexity should come from people who know the field. A non-programmer might look at 1,000 lines of computer code and say it is hopelessly complex and opaque, when a programmer finds it a clear and elegant solution. When disaster strikes, everyone will agree it was the computer's fault.
Then he's "mad as hell" at the irresponsibility of Wall Streeters. Again, without arguing the point, this is a guy who loves the Cold War doctrine of mutually assured destruction, and worked on military projects involving weapons of mass destruction for, in his own words, "the guys in the five-sided nuthouse." The worst financial idea in history does not compare in irresponsibility to supporting the capability to destroy all life on earth, at the direction of people you believe to be insane. In my opinion, the system the author supported and still supports had something like a 10% chance of killing me and everyone else (and still might do it), with absolutely no moral or other human justification. And it was done by people, like the author, who were avenging no personal tragedy, were not hungry or trapped or desperate, who had no great spiritual rationale; just irresponsible nerds with toys.
Finally, the coverage is entirely based on projects the author happened to work on and write about at the time, so a few areas are overcovered and many other areas are ignored. With a good editor to remove the redundancies and sections the author is not qualified to discuss, to order the material and to insist on background explanations, links and transitions, this might be a pretty good account. Until that happens, I suggest you avoid this book.Nerds on Wall Street: Math, Machines and Wired Markets Overview

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The Intelligent Universe: AI, ET, and the Emerging Mind of the Cosmos Review

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The Intelligent Universe: AI, ET, and the Emerging Mind of the Cosmos ReviewHaving read Gardner's earlier work Biocosm: The New Scientific Theory of Evolution: Intelligent Life Is the Architect of the Universe, I was prepared for "The Intelligent Universe" to be the work of a visionary thinker who is not afraid to speculate about the cosmological principles underlying our universe. I cherish the work of authors who are not afraid to think big. Gardner does not disappoint in the grandeur of his vision. If you think there is nothing new under the sun, I encourage you to read "The Intelligent Universe". One can't help but find enlightening material in the book. Oliver Wendell Holmes said "Man's mind, once stretched by a new idea, never regains its original dimensions." This sentiment certainly applies to my reading of Gardner's book.
Gardner's book is, however, not without flaws. One criticism I won't make of the book is that the ideas are hopelessly speculative. The book employs the kind of theoretical flights of fancy that John Horgan dismisses as "ironic science" in his book The End of Science (Helix Books), a book which says that science is asymptotically approaching a point at which there won't be any major new scientific theories, not because of science's failures but because science has been so successful. I mention Horgan because the scope of Gardner's vision encourages me to believe that we haven't even begun to exhaust our potential to develop breathtaking scientific theories of the cosmos. I came away from The Intelligent Universe with an excitement about the power of large-scale thinking about the universe.
Having said that, while Gardner presents an original "story", weaving together the work of numerous cosmologists and other scientists, perhaps paradoxically, the book often reads as a rehashing of the ideas of numerous big-picture thinkers. Maybe both perspectives can be accurate: Gardner summarizes the work of many while putting together the pieces in a unique way. There is nothing wrong with synthesizing the views of one's peers. However, the book too often lapses into a series of synopses of the big ideas of other scientific thinkers, brilliant though these thinkers may be.
To give you an idea of Gardner's method I reproduce the train of thought found in a few early chapters. Gardner uses extended paragraph-long quotations to run through the following thinkers (not all of which Gardner ultimately endorses):
Fred Hoyle on the fine-tunedness of physical constants, Francis Crick on directed pansperma (the idea that extraterrestrials seeded the biosphere with the first life forms on Earth), Stephen Wolfram and Ed Fredkin on cellular automata principles underlying physics, Seth Lloyd on the cosmos as quantum computer, Erwin Schrodinger on quantum physics underlying life, John Wheeler's on the "participatory anthropic principle" (the idea that only with conscious life does the universe summon itself into being), John Koza on genetic programming, Roger Penrose on the quantum physical underpinnings of consciousness, (leading to a gloss on the implications of combining quantum computing and genetic programming). Then Gardner begins the next chapter with Mark Bedau on artificial life, with an interlude about the perils of nanotechnology run amok, alluding to Michael Crichton's techno-thriller Prey. After that, we move on to topic of the technological singularity, where Ray Kurzweil plays a prominent role, both for his vision of smarter-than-human artificial intelligence and his optimism about the prospects for immortality. In the same chapter Gardner describes how Vernor Vinge forsees the arrival of super-human intelligence as more likely to result from intelligence amplification (at least at first) than from artificial intelligence.
Many of the later chapters work in a similar fashion, cycling through the big ideas of major thinkers. If a book is going to run through thinkers as this one does I guess what I would wish for is a book with the kind of comprehensiveness of The Anthropic Cosmological Principle (Oxford Paperbacks). Instead, too often there is only a superficial treatment of one thinker before we move on to another superficial treatment of the big idea of the next thinker. Such a technique is particularly unsatisfying for someone who is already familiar with many of the thinkers presented. I would relish a deeper engagement with the work of many of the thinkers treated. So one problem that I have with the book is simply that it is not in-depth enough. The body of the text takes up 196 pages, with an additional 46 pages comprising reprints of three articles from the International Journal of Astrobiology and Complexity magazine (2 articles).
I don't mean to dismiss Gardner's writing style. The value of his approach was demonstrated to me by his discussion of Beatriz Gato-Rivera's proposed solution to the Fermi Paradox. I had never heard of Gato-Rivera but Gardner's treatment of her position in The Intelligent Universe provided a nice jumping off point to her work. It is easy to see how the book could function as a window into a lot of other scientific topics. I was wondering how Gardner would reconcile the Fermi Paradox with Gardner's view that the universe is "hard-wired" to produce intelligent life, and Gato-Rivera's work figures prominently in his proposed resolution to the conundrum, although, characteristically, there is no attempt made to contradict this hypothesis or to pronounce on the merits of any alternative explanations.
"The Intelligent Universe" ultimately attempts to answer what Brian Greene has called the biggest of the big questions: Why is the universe life-friendly? Gardner, bold and original thinker that he is, thinks he knows the answer. His solution is the Selfish-Biocosm Hypothesis. The central claim of his Selfish-Biocosm Hypothesis is "that the ongoing process of biological and technological emergence, governed by still largely unknown laws of complexity, could function as a von Neumann controller, and that a cosmologically extended biosphere could serve as a von Neumann duplicating machine in a conjectured process of cosmological replication." In other words, the universe comes to life and then reproduces itself through the creation of other universes. This comes right out of Gardner's first book Biocosm. In this picture, human beings (or other intelligent life forms) might be thought of as the mitochondria of the cells that make up the universe as organism. The Intelligent Universe can be seen as the exploration of this basic storyline, and this includes dealing with the religious implications of the radically new perspective afforded by the Selfish-Biocosm Hypothesis. All in all, the story is well worth reading.The Intelligent Universe: AI, ET, and the Emerging Mind of the Cosmos Overview

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Appreciative Coaching: A Positive Process for Change (Jossey-Bass Business & Management) Review

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Appreciative Coaching: A Positive Process for Change (Jossey-Bass Business & Management) ReviewI am a huge fan of the appreciative inquiry process. Appreciative Coaching takes you through the step by step process of bringing someone through the processes of identifying their dream and bringing it to reality. They even speak about letting go of the relationship when the process is complete. I can not think of a better book I have read where you end and say I know what the theory and reason is, I know how to do it, and have resources given to start the process.
This is a must read!Appreciative Coaching: A Positive Process for Change (Jossey-Bass Business & Management) OverviewAppreciative Coaching describes an approach to coaching that is rooted in Appreciative Inquiry. At its core the Appreciative Coaching method shows individuals how to tap into (or rediscover) their own sense of wonder and excitement about their present life and future possibilities. Rather than focusing on individuals in limited or problem-oriented ways, Appreciate Coaching guides clients through four stages—Discovery, Dream, Design, and Destiny—that inspire them to an appreciative and empowering view of themselves and their future.

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Artificial Intelligence: Foundations of Computational Agents Review

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Artificial Intelligence: Foundations of Computational Agents ReviewThe textbook Artificial Intelligence: Foundations of Computational Agents is a general introduction to AI that is aimed at survey-style courses for upper year undergraduates and graduate students and is also suitable for self-study for those with a general computer science or mathematical background. Quite simply stated, the book is excellent. The writing, use of examples, topic coverage, level of detail, exercises at the end of each chapter, and supplementary online code and materials, are all, in my view, outstanding. I highly recommend the book for adoption in AI courses and for self-study. Remarkably and generously, while the book can be purchased in hard copy form, the entire textbook is also available online for free (which has proved very popular with my students).
Artificial Intelligence: Foundations of Computational Agents OverviewRecent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture. The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications.Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers, and independent learners. AI is a rapidly developing field: this book encapsulates the latest results without being exhaustive and encyclopedic. It teaches the main principles and tools that will allow readers to explore and learn on their own.The text is supported by an online learning environment, artint.info, so that students can experiment with the main AI algorithms plus problems, animations, lecture slides, and a knowledge representation system for experimentation and problem solving.

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Views into the Chinese Room: New Essays on Searle and Artificial Intelligence Review

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Views into the Chinese Room: New Essays on Searle and Artificial Intelligence ReviewThe Chinese Room Argument (CRA) has nothing to do with the speed of computers or any future developments in artifical intelligence (at least as understood as following from Turing). The CRA is a purely formal argument intended to refute the claim that computers (defined as Turing machines) can think, or can understand, or are minds solely by virtue of their formal description. (This claim is the essence of "computationalism," after Turing's original formulation.) The CRA is that: 1) Syntax is not semantics. 2) The implemented synatactical or formal program of a computer is not sufficient to generate semantics. 3) Minds have semantics. 4) Therefore, computers (so defined) are not minds/cannot think/do not understand because they are not sufficient to generate semantics.
For example, the concepts we employ to think and the words we use to speak have meanings. But there is nothing in computationalism as syntax that has any meaning whatsoever. Whatever meaning an implemented formal program has results from its being programmed or interpreted by us. Syntax (e.g., a computer program) has no causal powers. Whatever causal powers computers have (e.g., to fly airplanes) results from our programming and our assigning interpretations to the electrical charge insides a chip, not from the program in itself.
The chapters in Views Into the Chinese Room attack different aspects of the CRA. But they address it as an argument that stands or falls on the truth of the premises and the validity of the inference, not on engineering questions such as the speed of computers, which are irrelevant. Searle believes that there are, in fact, thinking machines -- we human beings are biological machines that think. And he believes that there also could be artificially made machines that think. The CRA is meant to show only that an implemented computer program by itself cannot generate mental content or semantic content.
For a clear explanation of the CRA, see chapter 15 of this book, by Stevan Harnad, the editor of The Behavioral and Brain Sciences, where Searle's original paper appeared twenty years ago. Do not rely on reviewers who do not understand the argument in the first place.Views into the Chinese Room: New Essays on Searle and Artificial Intelligence OverviewThe most famous challenge to computational cognitive science and artificial intelligence is the philosopher John Searle's "Chinese Room" argument. Searle argued that, although machines can be devised to respond to input with the same output as would a mind, machines--unlike minds--lack understanding of the symbols they process. 19 essays by leading scientists and philosophers assess, renew, and respond to his challenge.

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Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence) Review

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Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence) ReviewThis book is a great introduction to genetic programming and should be a model for textbook authors in other fields. Knowing little about genetic programming to begin with, this book guides the reader through the various topics and problems associated with genetic programming in a very logical and understandable way. Highly recommended! I wish more technical books were like this!Genetic Programming: An Introduction (The Morgan Kaufmann Series in Artificial Intelligence) Overview

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Visualizing the Semantic Web: XML-based Internet and Information Visualization Review

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Visualizing the Semantic Web: XML-based Internet and Information Visualization ReviewIf you are analysing various XML-encoded data, and am overwhelmed with the sheer mass of it all, you have probably wondered about displaying it. The problem is that there are an infinite number of ways to display data.
This book can only describe a small, finite number of display ideas. But it may well be worth your while to at least quickly thumb through the chapters. Various authors offer different takes on their data sets. The book also has some nice colour plates showing results.
In the book's title, you can ignore Semantic Web if you so choose. The key thing is supposedly that you have XML data. But it turns out that even this is not a necessary restriction. One way to read this book is to look for different data visualisation ideas. If you find one that is promising, you then have to reimplement it for your data structures.Visualizing the Semantic Web: XML-based Internet and Information Visualization OverviewThe Web is undergoing revolutionary changes - its second generation is emerging. The key player in the new generation is not HTML but XML (this is why it is also known as "the XML-based Web"). If the appearance of web pages is a major concern in the first generation, then the meaning (or semantics) of information on the Web is the focus of the second generation, which is why it is also called "the Semantic Web." The new edition of the pioneering monograph on Visualising the Semantic Web has undergone a number of changes in order to reflect recent research results, web standards, developments and trends. In this new edition, 2 chapters have been removed, 4 new chapters have been added and the 10 remaining chapters have been completely revised and updated.

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Foundations of Statistical Natural Language Processing Review

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Foundations of Statistical Natural Language Processing ReviewThis is the best book I've ever read on computational linguistics. It should be ideal for both linguists who want to learn about statistical language processing and those building language applications who want to learn about linguistics. This book isn't even published and it's now my most highly used reference book, joining gems such as Cormen, Leiserson and Rivest's algorithm book, Quirk et al.'s English Grammar, and Andrew Gelman's Bayesian statistics book (three excellent companions to this book, by the way).
The book is written more like a computer science or math book in that it starts absolutely from scratch, but moves quickly and assumes a sophisticated reader. The first one hundred or so pages provide background in probability, information theory and linguistics.
This book covers (almost) every current trend in NLP from a statistical perspective: syntactic tagging, sense disambiguation, parsing, information retrieval, lexical subcategorization, Hidden Markov Models, and probabilistic context-free grammars. It also covers machine translation and information retrieval in later chapters.
It covers all the statistical techniques used in NLP from Bayes' law through to maximum entropy modeling, clustering: nearest neighbors and decision trees, and much more.
What you won't find is information on applications to higher-level discourse and dialogue phenomena like pronoun resolution or speech act classification.Foundations of Statistical Natural Language Processing OverviewStatistical approaches to processing natural language text have becomedominant in recent years. This foundational text is the first comprehensiveintroduction to statistical natural language processing (NLP) to appear. The bookcontains all the theory and algorithms needed for building NLP tools. It providesbroad but rigorous coverage of mathematical and linguistic foundations, as well asdetailed discussion of statistical methods, allowing students and researchers toconstruct their own implementations. The book covers collocation finding, word sensedisambiguation, probabilistic parsing, information retrieval, and otherapplications.

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