- Hardcover: 416 pages
- Publisher: Cambridge University Press; 1 edition (April 28, 2008)
- Language: English
- ISBN-10: 0521878675
- ISBN-13: 978-0521878678
- Product Dimensions: 6.1 x 1.2 x 9.2 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
- Average Customer Review: 4 customer reviews
- Amazon Best Sellers Rank: #7,678,883 in Books (See Top 100 in Books)
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Artificial Dreams: The Quest for Non-Biological Intelligence Hardcover – April 28, 2008
The Amazon Book Review
Author interviews, book reviews, editors' picks, and more. Read it now
"I think all undergraduate physics majors will own a copy of this book within a year. It's that good."
--Professor Krsna Dev, Middlebury College
"Morin's writing is informal and inviting, and students will almost certainly respond easily to this style... [students] will fine the book accessible."
--J.R. Buriaga, Whitman College for Choice
"Artificial Dreams by Ekbia (information science and cognitive science, Indiana Univ.) is an interesting, entertaining book on how some dreams of artificial intelligence (AI) practitioners become valued contributions while others become only unrealizable projects. A major contribution of the book is a taxonomy and historical review of the different views of AI, which is covered in individual chapters. Highly Recommended."
--C. Tappert, Pace University, CHOICE
"...Artificial Dreams: The Quest for NonBiological Intelligence is written in a clear and accessible style that lay audiences and researchers outside of AI will enjoy reading; they will find the book very interesting in its breadth of coverage and, if they are curious about doing further reading, will find its extensive references very useful....Cognitive psychologists with interests in AI but who have not kept up with it are likely to find Ekbia's coverage and treatment very interesting..."
--Michael Palij, PsycCRITIQUES, March 11, 2009, Vol. 54, Release 10, Article 4
This book is a critique of Artificial Intelligence (AI) from the perspective of cognitive science - it seeks to examine what we have learned about human cognition from AI successes and failures. The book's goal is to separate those "AI dreams" that either have been or could be realized from those that are constructed through discourse and are unrealizable.
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Most of the book, including some gems in the footnotes at the back, hover around the point that we are somehow missing something in AI that would put us on the "right path", and that we are, at least, approaching this path slowly, perhaps without even realizing it. With a rich and colorful history behind AI, its future is unlikely to suffer from exactly the same mistakes despite the necessary evil or growing temptation faced by researchers to somewhat mislead industry-related benefactors into thinking they are financing something truly significant. I found myself generally sobering up to Ekbia's insights into AI and learning of happenings in the field that I was previously unaware of myself. Many books on AI will likely come off as highly technical and complicated (a lot of math is usually involved) but this one takes a "higher level" or philosophical approach which, I now think, should not be neglected even in undergraduate study of the field. One should, however, be careful not to give undue reverence to the idea of simply "being human" just because of the current shortcomings in AI. I am nevertheless certainly glad I made it a point to read the book while waiting for my viva voce.
The author uses this program as a paradigm for his main case against the reality of machine intelligence, viewing the program as an excellent example of the false imputation of intelligence to a machine. He gives many other examples throughout the book, all of them being quite familiar to those readers who follow the field of artificial intelligence (AI) or who are active participants in research thereof. As a whole the book is interesting, mostly due to the detail that the author brings to the history of AI and the discussions of some of the attempts to bring about machine intelligence.
However the author's case against AI is incredibly weak, being non-constructive in its strategy and actually being one of many critiques of AI that fall victim to what this reviewer has dubbed the "Michie-McCorduck-Boden effect." This effect, kind of an inverse of the Eliza effect, summarizes the peculiarities and crises of confidence that have dogged research in AI since its inception in the early 1950's. The following quotation from the writer Brian R. Gaines encapsulates it beautifully:
"From the earliest days of AI pioneers such as Donald Michie have noted that an intrinsic feature of the field is that problems are posed such that all those involved accept that any solution must involve `artificial intelligence' but, when the solution is developed and the basis for it is clear, the resultant technology is assimilated into standard information processing and no longer regarded as `intelligent' in any deep sense. When the magician shows you how the trick was done the `magic' vanishes. Much of what has been developed through AI research has diffused in this way into routine information technology: the Michie effect."
Other authors, AI historians, and researchers have made similar commentary as to the nature and progress in the field of AI. In particular the AI historian Pamela McCorduck and the cognitive scientist Margaret Boden have discussed this phenomenon at length. It could thus be referred to as the Michie-McCorduck-Boden effect, and it has huge consequences for general acceptance of machine intelligence, especially for specific views of whether or not a machine is exhibiting intelligence.
The Michie-McCorduck-Boden effect can be considered an inverse "Eliza" effect (as the author describes the latter) in that those who fall under its spell are quick to impute non-intelligence to machines as soon as they uncover "the method behind the magic." The author does this several times in this book: in his criticism of connectionism, the Cyc project, and Deep Blue. Once he discovers the processes or algorithms that each of these "programs" uses, he points out their shortcomings in semantics and a notion of "meaning" that he never really explains to the reader. But he still wants to describe human cognition as "intelligent", which he refers to as a "complex, multifaceted, and multilevel phenomenon."
But is human cognition the way he describes it? And once it is "unraveled" as he puts it, will it in turn be delegated to a trivial collection of processes in the same way as chess programs and natural language processors (e.g. Cyc) have been? If historical trends are to be followed with respect to the science of human cognition as they were in research in AI, there is every reason to believe that once the "method behind the magic" of human cognition is discovered it will trivialized in just the way that machine processes are. Will the notion of "intelligence" then fade from scientific discourse, both in machines and humans? Maybe.
The book is thus full of examples of projects that fall short if judged from true intelligence or "meaningful" knowledge as the author discusses it (and he does so with the admission that the dividing line between information and knowledge is too "fuzzy"). But what is so deeply troubling is not the vagueness in which the author addresses these issues, but rather the insistence that the AI projects such as Cyc and case-based reasoning must be complete or all-encompassing before we can regard them as intelligent. If Cyc gets bogged down in a question-answer session for a particular domain it must rejected the author seems to argue. He forgets that even human experts in particular domains, like physics for example, typically make mistakes in their scientific narratives, and we certainly don't want to reject their expertise outright because of a few blunders on their part. A reasonable outlook on the projects discussed in the book would consist of estimating the risk that the user takes on when using machines that deploy Cyc, case-based reasoning, or connectionism. Such machines will not be "fool-proof and incapable of error" and their solutions to problems or answers to questions may seem foolish or incomplete at times. But such is the nature of intelligence, and insisting otherwise puts unreasonable expectations on machines (or humans for that matter).
This reviewer therefore disagrees strongly with the author's conclusions, but does agree that one must emphasize both the practical applications of AI as well as the theories and formal constructions behind these applications. One must also step beyond the media and advertising hype, the overindulgences of Hollywood movies and rapid-fire press releases, and give an honest and objective assessment of the status of AI as it exists at the present time. Therefore "futurism", unjustified optimism, and wishful thinking should be carefully guarded against. On the other hand great care should be taken to distinguish skepticism from cynicism, and there should be no hesitation from expressing emotion when contemplating genuine discoveries in machine intelligence. We must not be guarded in our enthusiasm in this regard.
The field of artificial intelligence is a healthy one, and delivers practical technology, with its influence rapidly increasing in the twenty-first century. For better or worse, and in spite of the tremendous social changes that AI might cause, from a rigorous and careful study of the evidence, we can turn Hollywood on its head and use one of its movie titles with pleasure: we can say with confidence that we are entering a world of the silicon geniuses; a world of the avatars. We are witnessing the rise of the machines.