Artificial Intelligence Books

Honestly, I did not read it. I look forward to have it. I gave the book five star according my evaluation to the Volume 1 and my respect to David B. Fogel.

Volume I provided the general theory of evolutionary computation. This second volume on the other hand aims at introducing the reader to more practical aspects of evolutionary computation.

While i found the first volume great, this second volume lacked the details that are required to provide an intuition of the working of advanced evolutionary techniques. I feel that "How to solve it" by Michalewicz and Fogel and "Genetic algorithms + data structures = evolution programs" by Michalewicz both provide this experience useful to implement evolutionary techniques, by not trying to trade-off pages for understandability.

I would not recommend this book because it tries to introduce advanced aspects that are too difficult to cover in a single chapter each. If you really want to understand the practice of evolutionary techniques, you need a good intuition of how the various operators and structures work on real problems, just reading a few pages will not do the job.

This book is different from other books in image processing, since it also handles speech processing and gives much information on programming in C++. The authors provide free source codes with this book on a ftp-site.

The first introduction is about software development and ways to work with more people on one source code. It gives much information on programming. Then information is given on the difference between C and C++. I liked this overview very much, since I am used to program in C and not in C++.

An overview is given of the different kind of image processing operations in a compact way. Some information is given on statistical and neural pattern recogniton.

This book is a good learning book, whereas it can also be used as a reference source. The contents of the book is organized in a proper way, so it is not difficult to find the right information.

The book covers most topics which is very seldom found any where else

In the past five decades, the field of artificial intelligence has made significant progress, some of which can be characterized as radical departures with the past, while some as steady progress built on preconceived ideas. In general, progress in any field of endeavor is recognized by the participants and by the observers thereof, but this has not been the case in artificial intelligence. With few exceptions anytime an advance is made in this field it is at first greeted with a great deal of enthusiasm, and the algorithms it deploys are viewed as "intelligent." After some time however (and this time is relatively short) the algorithms are "understood" and are then designated as mere "programs" that certainly cannot be considered as intelligent. The "advance" finds its place in history as "trivial", and certainly not to be given any further consideration as "intelligent". Consequently, intelligent machines are always considered to be just beyond the horizon, as a goal to be achieved when better technology and algorithms are available.

But again, progress has been made in artificial intelligence: there are intelligent machines and they are used quite extensively in business and industry. But these machines are limited if one judges them from the standpoint of what is possible using human intelligence. The algorithms, or reasoning patterns that they deploy, are limited to working in a specific domain, such as finance, radiology, or network engineering. Human intelligence on the contrary can function in many different domains: a good chess player can also be a good musician or a good architect. Of course one can easily place algorithms in a particular machine each one of which has expertise in a particular domain, but they cannot cross over from one domain to another without considerable alteration from the designer or specialist. And any change in one domain-specific algorithm or reasoning pattern will not effect the efficacy of another algorithm or reasoning pattern with expertise in a different domain. To make an analogy with what is often discussed in the field of cognitive science, the machines of today thus have "modularized" intelligence: the modules or "programs" or "software" are designed to "think" in a certain domain or perform tasks restricted to certain domains.

There are a few in the artificial intelligence community that believe that genuine machine intelligence must at least be domain-independent, along with exhibiting curiosity and an ability to adapt to radically new situations. Such intelligence, in analogy with the human case, must be general enough to deal with situations, challenges, and contexts that are not tied to one domain. This has been called 'artificial general intelligence' (AGI) and is the subject of this book. It is a collection of articles by some of the individuals who have been actively involved in AGI and are working hard to bring it to fruition. The challenges in doing this are enormous, due in part to the paucity in funding for such endeavors, but due mostly to the conceptual difficulties involved in constructing reasoning patterns that can operate in many different domains without the assistance of the human engineer/designer. Suffice it to say that the goals that are discussed in this book represent the most ambitious projects ever attempted in the history of technology.

To assess or monitor the progress in AGI requires that one have at least a working definition of intelligence, and in the article by Pei Wang entitled "The Logic of Intelligence" this requirement is articulated clearly, albeit in a more general context. Wang asks whether there is an "essence of intelligence" that distinguishes intelligent entities from non-intelligent ones. His question is an interesting one since answering it will be necessary if one, again, is to gauge the progress in AGI. If the boundary between non-intelligent and intelligent systems is ill-defined then making claims regarding the status of AGI would be unfounded. But the definition of intelligence must also be one that is fruitful in a practical sense, since if AGI is to be successful it must have wide application in business, industry, and education. Wang settles on a "working" definition of intelligence, which he regards as a definition that is realistic enough to allow researchers to work directly with it. Such a definition will be robust in the sense that it is simple, has a close proximity to the concept to be defined, and allows a certain degree of progress to be made. His working definition of intelligence can be categorized as an adaptive one, in that it asserts that an intelligent machine is one that can adapt to its environment while having only insufficient knowledge and resources. The machine is therefore able to take the initiative to change its knowledge base or reasoning patterns as it confronts novel situations in the environment. He is careful to note what an unintelligent machine would be like, namely one that has been designed with the explicit assumption that the problems it attempts to solve are exclusively those that it has the knowledge and resources for, i.e. such a machine would be "programmed" to tackle certain problems of interest to the user, and would be given only those snippets of knowledge or expertise deemed relevant by this user. If the user were to give an intelligent machine this same collection of problems, it may not be able to find the solution more efficiently than the unintelligent one (or even find the "correct" solution), as the time scales needed for adaptation may be too long relative to the time needed for the unintelligent machine to solve the problems. The author recognizes this possible degradation in performance when using an intelligent machine, and such an issue will be very important when decisions are being made to deploy intelligent machines in time-critical situations or in situations where human or animal health is at stake.

Wang calls his version of AGI the 'Non-Axiomatic Reasoning System' (NARS) which deploys 'experience-grounded semantics', the latter of which is too be distinguished from the 'model-theoretic' semantics that is used in ordinary computing machines and is the foundation of much of theoretical computer science. In NARS, truth is dependent on the amount of evidence that is available, as is the meaning essentially. Wang also discusses in detail the need for `categorical logic' for knowledge representation, again since the machine is expected to operate with insufficient knowledge and resources, where `evidence' plays the key role in deciding the truth of statements (and not mere assignments of `T' or `F'). The NARS system will arrive at a solution that is `reasonable,' i.e. an optimal solution based on the knowledge it has at the time. Mistakes of course can be made, and in fact should be made, since otherwise the machine cannot learn from experience (even though trial and error learning is within the author's boundaries of what he considers intelligent). Therefore, an intelligent machine of the NARS type will not be "fool proof and incapable of error" to quote a line from a popular Hollywood movie. It will however constantly update it its knowledge base, a feature that the author calls `self-revisable'. He does not really say if such a machine could exhibit curiosity, i.e. do the problems it attempts to solve have to be instigated by the user or does it take the initiative to explore new knowledge bases or domains? If so, then such a machine might cause problems in deployment, since it can wander in conceptual space and not focus on the problems it was put in place to solve. However he does allow for autonomous behavior and creativity in the machine, even to such a degree that it completely loses track of the input tasks, i.e. the input tasks become `alienated' to use his words. In this regard, a NARS machine is somewhat like a human philosopher, for it can explore large conceptual spaces on its own and possibly get lost in them. Or more positively, it can find new knowledge that it did not possess before and construct concepts novel to itself (i.e. express `local creativity').

There are many other interesting discussions throughout the book, with each author outlining his/her notion of what it means for a machine to be intelligent and various strategies for constructing intelligent machines. One of these, called the Novamente project has been widely discussed in online messaging and is probably the oldest attempt to bring about AGI of those discussed in the book (at least from the standpoint of its origins). Particularly interesting in the Novamente project is its connection with dynamical systems, specifically in the role of attractors. Even though they do not mention it, the property of `shadowing' in the theory of dynamical systems may be a fruitful one for them to consider, especially in their use of `terminal attractors'. The shadowing property, if possessed by the `mind' of Novamente, would guarantee that an arbitrary dynamic pattern may not be a true `concept map' (as the authors define concept map), but it would be an approximation to some concept map. The shadowing property would guarantee that the reasoning patterns would be domain-independent, since any concept map acting on a particular domain, could be represented or approximated by some reasoning pattern. This reviewer does not know if the shadowing property has been applied to artificial intelligence, or even to neural networks, but if the dynamical systems paradigm holds in the latter, it does seem like an idea that may hold some promise, however small, for the development of domain-independent artificial intelligence.

If you are interested in human-level artificial intelligence you probably should own this book. I liked reading the book and am glad I own it but there are criticisms. Most of the book is too qualitative. Even where prototype software has been deployed algorithms are not given, even in pseudocode. Too much of the book is speculation. I also think that too little attention has been paid to the control of complexity.

I had high hopes for this book as part of my Artificial Intelligence course. I've been unhappy with the edited volumes where I would find a small subset of papers that I wanted to use in a course. Copeland's treatment of the intersection between Philosophy of Mind and Artificial Intelligence seemed to be exactly what I was looking for in a text. Unfortunately, the AI content is extremely dated, making it nearly useless to my computer science majors. (If I were teaching in a Philosophy department that wouldn't matter as much.) The first half of the book is great as a historical perspective, but I'll be going back to the edited volumes next time I teach AI and want to cover the Philosophy of Mind questions.

This is a fascinating and lively book, which is almost incredible give that it is an introductory philosophy textbook. Copeland manages to write with both personality and balance. The combination of his style (which is clear and witty without being facetious)and the intrinsic interest of the subject of artificial intelligence had me hooked. I read it like a novel, never wanting to put it down. Copeland assumes no prior knowledge of computer science, psychology, or philosophy, so the book should be accesible to any intelligent reader, although a few parts can be hard going. Beginners are likely to struggle with the sections on the CYC project (in chapter 5) and the Church-Turing thesis (in chapter 10), but slow and careful reading should do the trick. Copeland does explain eveything you need to know in order to understand what he's saying, but some of his explanations are gentler than others.

Otherwise my only complaint is that Copeland raises some interesting questions without exploring them very far. His view on the prospect for artificial intelligence is that, given the purposes for which we use such concepts as thinking, it is quite possible that there will come a day when the only reasonable course is to say that machines can think. In other words, he thinks that computers cannot now think, but that one day they (or their descendents) might become sophisticated enough that we ought to change our use of the word 'think' so that it applies to machines as well as humans. But he says very little about the purposes of concepts like thinking. In particular, he ignores the idea that rationality (surely a related concept) has great moral significance of a kind that might well make some people highly reluctant to say of any machine that it really thinks. Since this is an introductory book I don't hold this against Copeland, but it would be nice if he would say something about this in the next edition, which I believe is due out soon.

I'm looking forward to it.

The book is good, covers a lot and is very clear.

The downside: there are some small errors and mistakes.For example, the authors define gamma: SxAxE -> 2^S as the transition function, where S is the state space, A is the set of actions, E is the set of events. Later they say that if there are no events to be considered from the outside world, then you could use E={} (empty set) -- Assumption A3, page 10. Although this is intuitively OK, it is mathematically flawed, because the cartesian product of anything with {} is {}.
Planning with MDPs and specially with POMDPs deserves more attention. In particular, the very short commentary on planning with POMDPs mentions that it is not possible to solve big POMDPs. This is not true anymore; there are very good heuristics for POMDP solving currently.
I think more theorems could have been presented and proved, and some advanced sections could be added to each chapter (some authors include a section with a star, for example)
I also don't like the way pseudo-code is presented, but that is a matter of taste.

It would also be nice if the examples in chapter 2 were fully specified. That helps a lot to understand how problems are represented.

On the good side, there are LOTS of examples for each definition, and there are exercises at the end of each chapter (more exercises would be nice, actually). I also like the discussion and historical remarks at the end of chapters.

This is certainly a very good book. Anyone interested in planning ought to have it (and people interested in AI will certainly benefit from it).

Automated Planning is a good book for those who get started out in the field of search and planning. It's a good overview of the topics that abound within the planning community.

The only downside of the book is its dealing with important topics like planning graphs and markov description process is cursory, and more detail would have been nice.

Can one take any theorem or conjecture in mathematics and translate it into the language of OTTER in order to either prove the theorem or verify or disprove the conjecture? Unfortunately, the answer is no, as the translation of most of the statements of mathematics into the clause form required by OTTER will take a lot more research to bring it into fruition. The success of such research would be signal a major breakthrough in automated reasoning or automated theorem proving, as one could engage machines in the generation of new mathematical results and the settling of outstanding conjectures.

A variant of OTTER, called EQP, resolved the "Robbins conjecture" in 1997, via the efforst of the mathematician/computer scientist William McCune. First studied in 1933 by Herbert Robbins, the conjecture asserts that every Robbins algebra is Boolean. The proof took 8 days to complete and made international headlines. The success of this proof motivated many to look more deeply into OTTER, and since it is in the public domain, anyone curious about it can obtain it and use it. This book gives a comprehensive and very understandable overview of OTTER, and can be read by anyone with a background in mathematical logic. Some knowledge of logic and functional programming will help too. In relation to OTTER, one goal worth pursuing is to find out to what extent various fields of mathematics can be translated into the (clausal) language of OTTER. Point-set, geometric, and algebraic topology come to mind, as well as algebraic geometry in the guise of schemes and functors of points. Some automated proofs have been found in topology and algebraic geometry. It remains to be seen whether most, if not all the concepts in these fields can be expressed in a language that will enable automated proofs to be given.

OTTER is an ancronym for Organized Techniques for Theorem Proving and Effective Research", and after a forward to the book by Larry Wos, the author gives an introduction to the language in chapter 1. His concern is with applied logic and not theoretical developments, so the presentation is informal, and therefore useful to those who are anxious to learn OTTER and apply it. Theoretical developments are not completely ignored though, and throughout the book one can see to what extent expressions can be regarded as clauses and then translated into the language of OTTER.

This book fills a major void in the existing literature on OTTER,
the automated reasoning program developed at Argonne Research by William McCune. The book seems unusual in that, on one hand, it provides numerous examples of OTTER input/output files, useful tips on operating OTTER, and a plethora of exercises which, if carried out, will lead to a rapid understanding of the program and how to use it. But on the other hand, the book also works as a formidable introduction to automated reasoning. Starting from basic concepts such as inference rules and unification, and working up to more advanced topics in equational reasoning, one can gain a fairly good introduction to the theory of automated reasoning. My only complaint involves the lack of a good appendix or glossary which lists and summarizes the numerous commands that can be fed to OTTER. Furthermore, many of them are not even indexed, which makes referencing them somewhat tedious. Other than this, I highly recommend the book. However, I would encourage the novice to first study a more user friendly logic programming environment, such as swi prolog, before attacking OTTER. For having some experience with prolog programming will allow the reader to compare and contrast the two automated-reasoning methodologies. As for OTTER itself, I consider it more useful than a prolog interpreter since it allows for the use of
strategies for finding the desired proof or computation. On the other hand, it is a living embodiment of the fact that there is much progress that still needs to made in developing useful and powerful tools for automating logic and mathematics.

This book is a direct descendant of the Hampdenshire Wonder, although in this case the enhanced intelligence is controlled and deliberate.

When a very young James Holden's parents are killed, he is left in a different situation to most children in this situation. His clever scientist olds had invented an education machine that leaves him at five with the intellectual development and education of someone in high school, and beyond.

Much as Tim does in Children of the Atom he realises he can support himself by writing, and makes a living doing so when eight years old. This is a profession where you do not need to be seen.

Eventually he needs an adult front for economic reasons, and he approaches his landlord.

However, he can't stay hidden forever, no matter how clever, and unscrupulous types, as well as the government and the judiciary with ambition decide to put his invention to use, while shielding him from harassment, spies, and other such annoyances.

Terrifically suspenseful and readable tale of a small boy artificially made super-intelligent by scientists. He escapes his keepers and uses his superior brain to evade authorities and get along in the adult controlled world. If you liked James Patterson's "When the Wind Blows" and sequels, you'll like this original, tightly written suspense novel even better.

Having been involved in the technology business for way too many years, I found Newquist's book to be a pretty accurate account of what went on in the AI world. It's not a book about technology or programming, so if you're looking for technical data, try a textbook. But if you want to know why artifical intelligence died a slow and ugly death, you'll have all your questions answered here.

The book, as its subtitle suggests, is about "genius, ego, and greed"--the personalites involved in AI. It's not about the importance of neural networks vs the relevance of expert systems. As for the "discot" review that says to take some of the information with a grain of salt, Newquist includes nearly 15 pages of footnotes to back up his research. That should be good enough for most readers.

All in all, I found this book to be an insightful observation and reflection on what AI could have been. I'd recommend it over books by AI participants like Raymond Kurzweil, who obviously have personal motivations to keep selling AI snake oil in their self-promoting books.

I found this book both interesting and limited. Having encountered AI technologies and personalities at various points in my career, it was interesting to get from the author a more comprehensive view of what was going at the personal and organizational levels during the period covered by the book. The author's view of these matters was culled primarily from his experience as editor of a newsletter called AI Trends during that period. I visualize the author writing this book by pulling from stacks of old newsletters, article clippings, corporate brochures, and notes from interviews and discussions he had as a journalist on the beat. I see this book's value mainly in that it summarizes a lot of information about AI people and organizations in one place, organizing it into thematic chapters.
The author inserts his own perspective throughout the book, with mixed results. He is attracted to the dirt, the scandal, the quirky personality, and this leads to some interesting reading, interesting in the way you might listen to the town gossip, in spite of yourself. I had to take his gossip with a grain of salt, because some of it was based on questionable interpretations of the author, but enough was substantiated to be interesting. For example, the rise and fall of AI companies is an interesting story that parallels that the recent dot com cycle, and the AI era has lessons to teach us about the business and management of technology. However the author's bias toward airing dirty laundry sometimes comes across as a sneering attitude, or at least over-dramatization, and some of the ugly pictures he paints seem ugly because of his paint, not the events he reports. For example, he presumes to classify management talent as "A-teamers" (capable) or "B-teamers" (less capable), then identifies hiring B-teamers as evidence of poor management in some companies.
The author clearly does not have a deep understanding of AI technology, and this limits his ability to achieve two things he tries to do in the book: (1) explain AI in laymen's terms, and (2) interpret the technical significance, shortfalls, and potential of AI technology. He is on target some of the time, and sometimes misleading, or even wrong. For example, as the author correctly points out, the publication of the book Perceptrons by Minsky and Papert was an intriguing chapter in AI, since it effectively shut off research in neural networks for a long time. However, his discussion of the essence of Perceptron's criticism of neural networks is misleading: he says it was that neural networks cannot ".. learn new things from past experience..", when actually the main criticism was that certain kinds of problems can never be solved by neural networks. His discussion of how researchers eventually countered Perceptron's arguments is also misleading: he cites Hopfields's showing that recurrent neural networks can do things the brain does (an important contribution), when the more relevant direct answer to the Perceptron dilema was the development of good training algorithms for multi-layer nets that could solve the "impossible" problems.
I enjoyed reading this rather long (488 pages) book. It moved along quickly, and it was interesting to find answers to a lot of "whatever happened to ..... ? " questions. The AI era covered by this book was filled with fascinating stories and people. I would have preferred a more penetrating and knowing analysis of AI technology itself, that would make it easier to separate good ideas from business blunders, circumstances from fundamental flaws. And I wanted to have less of a feeling that the author was just guessing at some of his insider insights.

Freedman's book is a useful introduction to artificial
intelligence suitable for the lay reader. More technical
introductions exist in the form of the many good textbooks
on AI.
Brainmaker is a fun read but I do have a few criticisms.
Freedman distinguishes "old time" "good old fashioned AI"
g.o.f.a.i. from what he dubs "nature AI." I believe that he
has simply cobbled together some ideas and that his "nature
AI" does not exist as a coherent project.
Freedman seems to think that gofai was not modeled after
nature. I do not agree. Newell studied how people reasoned,
Boole was building a logic of how people think, and Rosenblatt
had real neural nets in mind. It is also inaccurate to call
gofai a failure. Sure there are lots of things that people
can do that computers can't. But there is also a long and
growing list of what computers can do and people can not. AIs
are good a modus tollens, humans are not. AIs are good at
long chains of reasoning and with negated terms, humans are not.
Computers can handle spaces having many dimensions, humans find
it hard to handle 3. Computers are good at probability and
math, people are not. And the list goes on and on.
Freedman's "nature AI" does not exist as a real AI subfield.
He has simply grouped together a number of new ideas, some good,
some bad. He also spends too much time on biology. If
there is any evidence that intelligence requires "wetwear" such
evidence is not presented in the book.

If you've ever read 'I, Robot' by Issac Asimov (or anything like it), this book should be required reading for you. It provides a fascinating and candid view into the history, research, debates and a detailed summary of the current progress in the controversial field of A.I. (artificial intelligence). I thoroughly enjoyed this book, and have shared it's title with many who have also enjoyed it. As an author and sci-fi fanatic, I found it's forthright, non-technical manner both refreshing and easy to read (finished it in one night), and the information provided to be excellent food for thought. Not too technical, this book can easily be understood by just about anyone, and SHOULD be read by anyone who has ever enjoyed reading anything involving robots or computers of any kind (even the average Star Wars fan will like this one). 'Brainmakers' is a must-read which has earned a permanent place in my personal library (which I might add is fairly extensive and somewhat exclusive). If you can find a copy, it's worth whatever price you have to pay for it.

Really neat book. I have wanted to tinker with an underwater robot for some time. Indeed, who hasn't wanted to build a submarine or something like it since they saw their first submarine war movie.

This book presents the fundamental design challenges in very clear English. There are 2 vehicle designs presented with plenty of diagrams and hints to avoid common pitfalls. The techniques for waterproofing the motors and control system are very simple and are commonly available in any hardware store or even already in the household. Great book to get one started in underwater robotics. And to finally fulfill that childhood dream.

This is indeed a great book, but guys... $192 ! You can buy this elsewhere on the net for around $30 including delivery. Don't give in to gougers, it just isn't that rare a book !