- Hardcover: 408 pages
- Publisher: Prometheus Books (May 30, 2007)
- Language: English
- ISBN-10: 1591025117
- ISBN-13: 978-1591025115
- Product Dimensions: 6.2 x 1.1 x 9.3 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
- Average Customer Review: 4.2 out of 5 stars See all reviews (13 customer reviews)
- Amazon Best Sellers Rank: #1,421,427 in Books (See Top 100 in Books)
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Beyond AI: Creating the Conscience of the Machine Hardcover – May 1, 2007
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"Taking us on an eloquent journey through an astonishingly diverse intellectual terrain, J. Storrs Hall’s Beyond AI articulates an optimistic view – in both capability and impact – of the future of AI. This is a must read for anyone interested in the future of the human-machine civilization."
RAY KURZWEIL, AI scientist, inventor
Author of The Singularity Is Near
"An entertaining and very thought-provoking ramble through the wilds of AI."
ERIC S. RAYMOND
"Hall argues that our future superintelligent friends in the mechanical kingdom may develop superior moral instincts. I'm almost convinced. I learned a lot from reading this book. You will too."
ROBERT A. FREITAS JR.
Author of "The Legal Rights of Robots"
and Kinematic Self-Replicating Machines
About the Author
J. Storrs Hall, PhD (Laporte, PA), the founding chief scientist of Nanorex, Inc., is a research fellow for the Institute for Molecular Manufacturing and the author of Nanofuture, the "Nanotechnologies" section for The Macmillan Encyclopedia of Energy, and numerous scientific articles. He has designed technology for NASA and was a computer systems architect at the Laboratory for Computer Science Research at Rutgers University from 1985 to 1997.
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Top Customer Reviews
The last five chapters that are surprisingly good, and should shame most professional philosophers whose writings by comparison are a waste of time.
His chapter on consciousness, qualia, and related issues is more concise and persuasive than anything else I've read on these subjects. It's unlikely to change the opinions of people who have already thought about these subjects, but it's an excellent place for people who are unfamiliar with them to start.
His discussions of ethics using game theory and evolutionary pressures is an excellent way to frame ethical discussions.
My biggest disappointment was that he starts to recognize a possibly important risk of AI when he says "disparities among the abilities of AIs ... could negate the evolutionary pressure to reciprocal altruism", but then seems to dismiss that thoughtlessly ("The notion of one single AI taking off and obtaining hegemony over the whole world by its own efforts is ludicrous").
He probably has semi-plausible grounds for dismissing some of the scenarios of this nature that have been proposed (e.g. the speed at which some people imagine an AI would take off is improbable). But if AIs with sufficiently general purpose intelligence enhance their intelligence at disparate rates for long enough, the results would render most of the book's discussion of ethics irrelevant. The time it took humans to accumulate knowledge didn't give Neanderthals much opportunity to adapt. Would the result have been different if Neanderthals had learned to trade with humans? The answer is not obvious, and probably depends on Neanderthal learning abilities in ways that I don't know how to analyze.
Also, his arguments for optimism aren't quite as strong as he thinks. His point that career criminals are generally of low intelligence is reassuring if the number of criminals is all that matters. But when the harm done by one relatively smart criminal can be very large (e.g. Mao), it's hard to say that the number of criminals is all that matters.
Here's a nice quote from Mencken which this book quotes part of:
Moral certainty is always a sign of cultural inferiority. The more uncivilized the man, the surer he is that he knows precisely what is right and what is wrong. All human progress, even in morals, has been the work of men who have doubted the current moral values, not of men who have whooped them up and tried to enforce them. The truly civilized man is always skeptical and tolerant, in this field as in all others. His culture is based on 'I am not too sure.'
Another interesting tidbit is the anecdote that H.G. Wells predicted in 1907 that flying machines would be built. In spite of knowing a lot about attempts to build them, he wasn't aware that the Wright brothers had succeeded in 1903.
If an AI started running in 2003 that has accumulated the knowledge of a 4-year old human and has the ability to continue learning at human or faster speeds, would we have noticed? Or would the reports we see about it sound too much like the reports of failed AIs for us to pay attention?
This book gives a realistic appraisal of progress in artificial intelligence and sheds considerable light on these questions. It is careful to distinguish between fact and fiction, between what has been accomplished and what has not, and it does so without falling into the trap of extreme skepticism, the latter of which seems to happen to so many who are deeply involved in AI research. Indeed, after an initial period of extreme confidence in research results, and a designation as "intelligent", the confidence wanes until it is eventually viewed as a "trivial" discovery or merely a "program." There are many indications from the historical accounts of AI research that this pattern is repeated often.
This author though takes the general reader through this history and also takes a view of future developments in artificial intelligence, discussing at various places in the book the possibility of a technological "singularity" sometime in the next fifty years. Readers who are curious about the status of machine intelligence will find an understandable overview in this book, but it can still be of interest to those, such as this reviewer, who are working "in the trenches" of applied artificial intelligence and are interested in the opinions of researchers affiliated with the academy. The author does not delve deeply into the technologies, algorithms, and mathematics for this type of reader, but there are some new ideas within the covers that definitely make the book worth reading.
Machine intelligence has advanced, the author argues, and he gives many examples. Robots for example, can currently navigate with the same adeptness as a three-year old child, which is astounding considering what was possible just ten years ago. Readers who own and develop AIBO robot dogs will understand this claim, as their navigation abilities are impressive. If one thinks qualitatively, then one can project with a fair degree of confidence that robots will be able to interact with the environment with the same adeptness as an adult human within the next two decades. This prediction would however be difficult to put on a quantitative foundation, one must arrive at a measurable definition of robot-environment interaction. The lack of quantitative measures of progress has plagued the AI community since its inception in the early 1950's, especially the lack of a general, measurable definition of intelligence. In the opinion of this reviewer, the field of cybernetics and control theory, its generalization, has formulated the best quantitative notion of intelligence to date. Cybernetics is discussed in some detail in this book (along with its "death"). The author seems to believe though that it is information theory that promises the best measurable definition of intelligence. He discusses some reasons for this view, but does not elaborate with any detail, except brief commentary on how it can be used to measure, by using the concept of entropy, the predictive power of theories.
Particularly interesting in the book is the discussion of the "ELIZA effect" that refers to the program invented by Joseph Weizenbaum in the early 1960's that was designed to converse with a human subject, with the intent of fooling the subject into believing that the program understood what she was saying. The author scoffs at any imputation of understanding by ELIZA, and uses the "ELIZA effect" to describe any effort or claim by AI researchers that their work is a significant advance, and not just a bag of tricks that can easily mislead. But there is a serious problem with the author's use of the "ELIZA effect", in that significant advances may indeed have been made, but then after they are studied and understood they are then viewed as insignificant, and the discoverers are then labeled as falling prey to the ELIZA effect. As an example of how this scenario might be played out, consider an English professor who retired ten years before the advent of sophisticated spell checkers and real-time English grammar. She then comes out of retirement and decides to write a novel, and discovers this spelling/grammar checker, marveling at its abilities and definitely convinced that it displays intelligence. But sometime thereafter an AI expert reveals to her how it actually works and she then begins to accept it as merely a software program, no different really than some of the crude writing software she used years earlier.
But the author's belief in the ELIZA effect does not mean that he does not believe that intelligent machines (or "software") have not been achieved. However, this intelligence has only been able to operate in specific domains. As an example, he discusses the SHRDLU system invented by Terry Winograd, which was able to converse about a tabletop on which were placed a set of children's blocks. The author believes that SHRDLU was able to achieve genuine understanding, albeit in a very specific domain: the blocks world. This domain-specificity has been the hallmark of all of the commercial successes of artificial intelligence, since business are primarily concerned with automating tasks in very specific domains, such as managing and analyzing networks, collecting and interpreting information from competitors, or finding profitable financial opportunities by sifting through mountains of data. Machines that are able to think in many different domains may not be useful in this regard. A machine that is able to troubleshoot a network would be useful to a network manager, but if it had expertise in chess playing and decided to do this instead, this would raise the ire of the network manager.
For this reviewer, the most interesting part of the book was the discussion on `autogenous systems' because of its novelty and because it is related to the efforts to build machines that possess general intelligence. The author defines such a system as one that is able to extend itself arbitrarily, and thus go beyond preconceived limits. An autogenous machine will therefore be able to confront new and innovative situations or problems without excessive fiddling by the designer. Its cognitive structure, as one might call it, can build concepts and engage in learning on-the-fly without external intervention. At the present time, such systems are the holy grail of AI and there are concentrated efforts to build them. If they are built, and this reviewer is confident that this will be the case, will they fall into the usual pattern of first being viewed as major breakthroughs, and then latter as merely "programs?" If history is a guide this will happen, but such machines will be the tour de force of the twenty-first century, possibly bringing about a "singularity" as the author discusses, but also serving as an example of what can be accomplished with that low-voltage mass of biological matter called the human brain, which is the most impressive machine, and maybe indeed a universal one, that has yet arisen.
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