Showing posts with label artificial intelligence. Show all posts
Showing posts with label artificial intelligence. Show all posts

Fuzzy Thinking: The New Science of Fuzzy Logic Review

Fuzzy Thinking: The New Science of Fuzzy Logic
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Fuzzy Thinking: The New Science of Fuzzy Logic ReviewBuddhist math? C'mon.
First, let me say that fuzzy logic and fuzzy arithmetic are great tools. They're valued parts of the 'soft logic' kit that includes probability, interval arithmetic, Bayesian and Markov networks, and lots of other good stuff. Fuzziness involves many of the formal techniques used in probability and elsewhere, and gives a useful, alternative view of the systems it addresses.
The basic fuzzy idea is that most descriptions involve shades of gray, that few systems really match the black/white, on/off, either/or duality of standard formal logic. That's fine, I can get along with that quite well.
My problem, though, is that Kosko presents the fuzzy world-view vs. the traditional or "scientific" in exactly the black and white terms that he rejects. He also argues that fuzziness describes the world more effectively than "scientific" terms, that the rules of arithmetic, probability, and calculus are just games. They are played for their internal consistency, not because differentiation or factorials occur in nature.
That's true, and as a heavy math user I know enough to distinguish my models from reality. Two facts remain, though. First, the models very often do describe reality in ways that can be checked easily enough: the bridge doesn't fall down and the TV receives its signal. Both happen because the bad old exact arithmetic has some kind of correspondence (no, I don't know what) to the real world, giving real ability to predict real results. Second, fuzzy logic and fuzzy arithmetic are themselves mathematical formalisms, games like all the others. Once you get past the gee-whiz stage, there is mathematical content as rigorous as in any other field of study. It's not either/or, it's very often a different way to interpret the same self-consistent games people have played for years. It adds interesting rules to the game.
The great thing is that you really can use the new interpretations and tools along with the old ones. Fuzziness doesn't demolish the old structures, it bolsters them and adds capacity.
And you can get all these benefits without shrink-wrapped, bite-sized pieces of Eastern philosophy.
//wiredweirdFuzzy Thinking: The New Science of Fuzzy Logic Overview

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Artificial Intelligence: A Modern Approach (2nd Edition) Review

Artificial Intelligence: A Modern Approach (2nd Edition)
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Artificial Intelligence: A Modern Approach (2nd Edition) ReviewI didn't think that the first edition of this book was as bad as some of the reviewers said, but the second edition is definitely a vast improvement. It's not just some obligatory 2nd edition that some authors release to say that they are staying actively published. The first edition was somewhat confusing in its explanations and the exercises were really blurry on what was being asked. All of that has now been resolved.
The book is a comprehensive and insightful introduction to artificial intelligence with an academic tone. It provides a unified view of the field organized around the rational decision making paradigm, which focuses on the selection of the "best" solution to a problem. The book's overall theme is that the purpose of AI is to solve problems via intelligent agents, and then goes about specifying the features such an agent or agents should have. Pseudocode is provided for all of the major AI algorithms. Being about the broadest book in terms of coverage of AI, you should therefore not expect it to be the deepest in coverage. However, each topic is covered to the extent that the reader should understand its essence. Sections one through six are absolutely wonderful, and comprise the "meat" of AI. Section seven is rather weak since it tries to cover both robotics and text processing in their own individual chapters, and entire books have a hard time covering this material. Section eight is different from the others, since it talks about the philosophy and future of AI.
Another plus for this book is that there is a great deal of extra material that deals with standard AI curriculum. For example, the chapters on logic not only include the typical introduction to propositional and first order logic together with the usual inference procedures, they also give many useful hints how to use first order logic to actually represent aspects of the real world such as measures, time, actions, mental objects, etc. These chapters also contain much information about how to implement efficient logical reasoners.
Finally, this second edition has an excellent website that can be found by going through the publisher's webpage for the book. This website contains four sample chapters, pseudocode, and actual code in Java, Python, and LISP.
I notice that Amazon shows the table of contents from the first edition, so I am showing what the actual table of contents is for the second edition for the purpose of completeness. Note that the book has been significantly reorganized.
I. ARTIFICIAL INTELLIGENCE.
1. Introduction.
2. Intelligent Agents.
II. PROBLEM-SOLVING.
3. Solving Problems by Searching.
4. Informed Search and Exploration.
5. Constraint Satisfaction Problems.
6. Adversarial Search.
III. KNOWLEDGE AND REASONING.
7. Logical Agents.
8. First-Order Logic.
9. Inference in First-Order Logic.
10. Knowledge Representation.
IV. PLANNING.
11. Planning.
12. Planning and Acting in the Real World.
V. UNCERTAIN KNOWLEDGE AND REASONING.
13. Uncertainty.
14. Probabilistic Reasoning Systems.
15. Probabilistic Reasoning Over Time.
16. Making Simple Decisions.
17. Making Complex Decisions.
VI. LEARNING.
18. Learning from Observations.
19. Knowledge in Learning.
20. Statistical Learning Methods.
21. Reinforcement Learning.
VII. COMMUNICATING, PERCEIVING, AND ACTING.
22. Agents that Communicate.
23. Text Processing in the Large.
24. Perception.
25. Robotics.
VIII. CONCLUSIONS.
26. Philosophical Foundations.
27. AI: Present and Future.Artificial Intelligence: A Modern Approach (2nd Edition) Overview

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Neural Networks for Pattern Recognition Review

Neural Networks for Pattern Recognition
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Neural Networks for Pattern Recognition ReviewThis book came out at about the same time as Ripley's, which has almost the same title, but in reverse. At the time, I liked Ripley's better, because it covered more things that were totally new to me. Then a friend said he had chosen Bishop for a course he was teaching, and I went back and reconsidered the two books. I soon found that my friend was right: Bishop's book is better laid out for a course in that it starts at the beginning (well, not quite the beginning--you do need to be fairly sophisticated mathematically) and works up, while Ripley's is more a collection of insights all at the same level; confusing to learn from. Bishop is able to cover both theoretical and practical aspects well. There certainly are topics that aren't covered, but the ones that are there fit together nicely, are accurate and up to date, and are easy to understand. It has migrated from my bookcase to my desk, where it now stays, and I reach for it often.
To the reviewer who said "I was looking forward to a detailed insight into neural networks in this book. Instead, almost every page is plastered up with sigma notation", that's like saying about a book on music theory "Instead, almost every page is plastered with black-and-white ovals (some with sticks on the edge)." Or to the reviewer who complains this book is limited to the mathematical side of neural nets, that's like complaining about a cookbook on beef being limited to the carnivore side. If you want a non-technical overview, you can get that elsewhere (e.g. Michael Arbib's Handbook of Brain Theory and Neural Networks or Andy Clark's Connectionism in Context or Fausett's Fundamentals of Neural Networks), but if you want understanding of the techniques, you have to understand the math. Otherwise, there's no beef.Neural Networks for Pattern Recognition Overview

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Common Lisp: A Gentle Introduction to Symbolic Computation Review

Common Lisp: A Gentle Introduction to Symbolic Computation
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Common Lisp: A Gentle Introduction to Symbolic Computation ReviewMy copy is worn out, and I sure could use a NEW one...! When I buy a book, I seldom know whether it will become a staple of my library or not. This has, over the years, become part of the most central core. I have worked in LISP off and on for years, and this book unfailingly gets me back on in a hurry. Not only is it the clearest tutorial on LISP I know (it's even a literate, pleasant read!), it has the only discussion of recursion I know that's clear and fun.Common Lisp: A Gentle Introduction to Symbolic Computation OverviewA highly accessible introduction to LISP, this is for inexperienced programmers and programmers new to LISP. A LISP "toolkit" in each chapter explains how to use Common LISP programming and debugging tools such as DESCRIBE, INSPECT, TRACE and STEP.

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Computers and Thought Review

Computers and Thought
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Computers and Thought ReviewThis is the 1995 reprint of the original 1960s book. It has a new preface by Feigenbaum with help from Feldman that gives the history of the book. This is the first computer book I ever bought (after McCraken's Fortran manual). I gave it away in about 1980 and have regretted ever since not still having it on my bookshelf.Computers and Thought OverviewComputers and Thought showcases the work of the scientists who not onlydefined the field of Artificial Intelligence, but who are responsible for havingdeveloped it into what it is today. Originally published in 1963, this collectionincludes twenty classic papers by such pioneers as A. M. Turing and Marvin Minskywho were behind the pivotal advances in artificially simulating human thoughtprocesses with computers.Among the now hard-to-find articles are reports of computerprograms that play chess and checkers, prove theorems in logic and geometry, solveproblems in calculus, balance assembly lines, recognize visual temporal patterns,and communicate in natural language. The reports of simulation of cognitiveprocesses include computer models of human behavior in logic problems, deciding oncommon stock portfolios, and carrying out social interaction. Models of verballearning behavior, predictive behavior in two-choice experiments, and conceptformation are also included.Articles by : Paul Armer. Carol Chomsky. Geoffrey P. E.Clarkson. Edward A. Feigenbaum. Julian Feldman. H. Gelernter. Bert F. Green, Jr.John T. Gullahorn. Jeanne E. Gullahorn. J. R. Hansen. Carl I. Hovland. Earl B. Hunt.Kenneth Laughery. Robert K. Lindsay. D. W. Loveland. Marvin Minsky. Ulric Neisser.Allen Newell. A. L. Samuel. Oliver G. Selfridge. J. C. Shaw. Herbert A. Simon. JamesR. Slagle. Fred M. Tonge. A. M. Turing. Leonard Uhr. Charles Vossler. Alice K.Wolf.

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Common LISP. The Language. Second Edition Review

Common LISP. The Language. Second Edition
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Common LISP. The Language. Second Edition Review"Common Lisp, The Language" (or CLTL) is an industrial-strength language reference for a somewhat esoteric computer language (in the view of most programmers today), so this tome is definitely not for the novice, nor for the faint of heart. However, if you are a true devotee of Common Lisp, then it is hard to imagine how you can escape this most sacred of texts. I own two dog-eared and heavily marked-up copies of the book, from which I have gotten my money's worth many times over. For years one or the other of these copies has been a permanent fixture on my desk, beside my keyboard. It is an invaluable reference for serious Common Lisp programmers.
However, as a previous reviewer pointed out, CLTL is strictly a reference, not a text. If you attempt to use it as an introductory text, you will hate both the book and the language, which will be your loss. To learn the language, I would recommend either "Lisp", by Winston and Horn, or "ANSI Common Lisp", by Paul Graham. After perhaps several years of serious Lisp programming, you will most likely find yourself studying the pages of CLTL, at which point you will appreciate what Guy Steele has succeeded in accomplishing in this slender volume of 1029 pages. Common Lisp is an enormous language, with over 800 built-in functions, many of which have complicated semantics and dozens of keywords that alter those semantics. Considering the daunting task of documenting this language, Steele deserves a medal. (In fact, the book has received various awards.)
Common Lisp was an integral part of several classes that I taught at Caltech for many years; I had students write compilers, interpreters, theorem provers, symbolic manipulators, numerical solvers, graph algorithms, etc. When you attack such a wide range of problems with a single language, you appreciate how rich Common Lisp is, and how well suited it is to all these tasks (yes, even numerical computation). But to get the most out of the language, it's necessary to tap into its more esoteric functions, which is where Steele's book is very handy.
I can think of few topics in the field of computer science that have as rich a history as the language Lisp. It's difficult to present a meaningful view of the language, especially in it's "Common" incarnation, without delving into some of that history. Steele does this exceedingly well in CLTL, although I can understand how it can be off-putting to some; it adds bulk to an already formidable tome, and at times seems to clutter up what ought to be a cut-and-dried presentation of syntax and semantics. However, unless you subscribe to the mystical view that Lisp was created by divine fiat (a theory that is gaining popularity), then you will inevitably have questions as to why things were done in one way and not another. The answers provide insight into language design (or at least the workings of the X3J13 committee), and at times a better mastery of Common Lisp. For those who do not care for such details, Steele sets the digressions off from the main body of the text, making them easy to skip. But I, for one, am happy that this information is recorded somewhere. (If nothing else, it keeps the creationists at bay.)
Like the mathematician Gilbert Strang, who manages to inject humor into the driest of mathematical journals, Steele has found ample opportunities to sneak bits of wordplay and irreverence into CLTL for comic relief. Not only does Steele enliven his program fragments with snippets of pop culture, as in
"(loop for turtle in teenage-mutant-ninja-turtles do..."
but all such references are assiduously listed in the index, which makes it a real hoot to glance through. Listed there are "Mozart, Wolfgang Amadeus", and "Michelangelo (artist)" as well as "Michelangelo (turtle)". We also find things like "goody two-shoes", "oranges, comparing apples with", "square peg in round hole", and numerous foods, including garbanzo beans, ice cream, orange flavor beef, pizza, and peppermint. Under "pasta" we find "see also macaroni". But my favorite index entry is "kludges", which directs us to pages 1 through 971; which is, of course, the entire body of the book, excluding index and appendices. Steele obviously decided to have a little fun, which is understandable considering how dry such books tend to be.
But, before you click this book into your shopping cart, you should realize that the complete text is available on-line, and for free. I'm not sure how Steele swung this with the publisher, but it's out there in the public domain. Finally, I should point out that there are a number of excellent free Common Lisp interpreters available for many different platforms. The best I have found is CLISP, which is maintained primarily by Bruno Haible through the GNU Project. It's reasonably complete and robust.
Happy hacking. May cons be with you.Common LISP. The Language. Second Edition OverviewThe defacto standard - a must-have for all LISP programmers.In this greatly expanded edition of the defacto standard, you'll learn about the nearly 200 changes already made since original publication - and find out about gray areas likely to be revised later. Written by the Vice- Chairman of X3J13 (the ANSIcommittee responsible for the standardization of Common Lisp) and co-developer of the language itself, the new edition contains the entire text of the first edition plus six completely new chapters. They cover: - CLOS, the Common Lisp Object System, with new features to support function overloading and object-oriented programming, plus complete technical specifications * Loops, a powerful control structure for multiple variables * Conditions, a generalization of the error signaling mechanism * Series and generators * Plus other subjects not part of the ANSI standards but of interest to professional programmers. Throughout, you'll find fresh examples, additional clarifications, warnings, and tips - all presented with the author's customary vigor and wit.

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Programming Collective Intelligence: Building Smart Web 2.0 Applications Review

Programming Collective Intelligence: Building Smart Web 2.0 Applications
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Programming Collective Intelligence: Building Smart Web 2.0 Applications ReviewThis book is probably best for those of you who have read the theory, but are not quite sure how to turn that theory into something useful. Or for those who simply hunger for a survey of how machine learning can be applied to the web, and need a non-mathematical introduction.
My area of strength happens to be neural networks (my MS thesis topic was in the subject), so I will focus on that. In a few pages of the book, the author describes how the most popular of all neural networks, backpropagation, can be used to map a set of search terms to a URL. One might do this, for example, to try and find the page best matching the search terms. Instead of doing what nearly all other authors will do, prove the math behind the backprop training algorithm, he instead mentions what it does, and goes on to present python code that implements the stated goal.
The upside of the approach is clear -- if you know the theory of neural networks, and are not sure how to apply it (or want to see an example of how it can be applied), then this book is great for that. His example of adaptively training a backprop net using only a subset of the nodes in the network was interesting, and I learned from it. Given all the reading I have done over the years on the subject, that was a bit of a surprise for me.
However, don't take this book as being the "end all, be all" for understanding neural networks and their applications. If you need that, you will want to augment this book with writings that cover some of the other network architectures (SOM, hopfield, etc) that are out there. The same goes for the other topics that it covers.
In the end, this book is a great introduction to what is available for those new to machine learning, and shows better than any other book how it applies to Web 2.0. Major strengths of this book are its broad coverage, and the practicality of its contents. It is a great book for those who are struggling with the theory, and/or those who need to see an example of how the theory can be applied in a concise, practical way.
To the author: I expect this book will get a second edition, as the premise behind the book is such a good one. If that happens, perhaps beef up the equations a bit in the appendix, and cite some references or a bibliography for those readers interested in some more in depth reading about the theory behind all these wonderful techniques. (The lack of a bibliography is why I gave it 4 stars out of 5, I really think that those who are new to the subject would benefit greatly from knowing what sits on your bookshelf.)Programming Collective Intelligence: Building Smart Web 2.0 Applications OverviewWant to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general--all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:
Collaborative filtering techniques that enable online retailers to recommend products or media
Methods of clustering to detect groups of similar items in a large dataset
Search engine features--crawlers, indexers, query engines, and the PageRank algorithm
Optimization algorithms that search millions of possible solutions to a problem and choose the best one
Bayesian filtering, used in spam filters for classifying documents based on word types and other features
Using decision trees not only to make predictions, but to model the way decisions are made
Predicting numerical values rather than classifications to build price models
Support vector machines to match people in online dating sites
Non-negative matrix factorization to find the independent features in adataset
Evolving intelligence for problem solving--how a computer develops its skill by improving its own code the more it plays a game
Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

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