Marvin Minsky, along with a small group of scientists, coined the term, "artificial intelligence." He also wrote Society of Mind, a tour de force that paved the way for thinking about getting machines to think. The Emotion Machine, his more recent work, presents ideas that shake up the field of psychology. His suggestions about the ways in which emotions "cascade" as a series of logical events in response to life circumstances offer a picture that is both physiologically and cognitively rational. Beyond suggesting what machines can accomplish, Minsky's suggestions are highly innovative regarding the field of psychology.These topics, in addition to many others spanning varied fields, make the book amazing.

Progress in the design and creation of intelligent machines has been steady for the last four decades and at times has exhibited sharp peaks in both advances and applications. This progress has gone relatively unnoticed, or has been trivialized by the very individuals who have been responsible for it. The field of artificial intelligence has been peculiar in that regard: every advance is hailed as major at the time of its inception, but after a very short time it is delegated to the archives as being "trivial" or "not truly intelligent." It is unknown why this pattern always occurs, but it might be due to the willingness of researchers to engage in philosophical debate on the nature of mind and the possibility, or impossibility, of thinking machines. By indulging in such debates, researchers waste precious time that is better used dealing with the actual building of these machines or the development of algorithms or reasoning patterns by which these machines can solve problems of both theoretical and practical interest. Also, philosophical musings on artificial intelligence, due to the huge conceptual spaces in which they wander aimlessly, are usually of no help in pointing to the right direction for researchers to follow. What researchers need is a "director" or "set of directors" that are familiar with the subject matter, have both applied and theoretical experience in the field of artificial intelligence, and that eschew philosophical armchair speculation in favor of realistic dialog about the nature and functioning of intelligent machines.

The author of this book has been one of these "directors" throughout his professional career, and even though some of his writings have a speculative air about them, many others have been very useful as guidance to those working in the trenches of artificial intelligence. One can point to the author's writings as both inspiration and as a source of perspiration, the latter arising because of the difficulty in bringing some of his ideas to fruition. It would be incorrect to state that the author's ideas have played a predominant role in the field of artificial intelligence, but his influence has been real, if sometimes even in the negative, such as his commentary on the role of perceptrons.

There are intelligent machines today, and they have wide application in business and finance, but their intelligence is restricted (but highly effective) to certain domains of applicability. There are machines for example that can play superb chess and backgammon, being competitive with the best human players in this regard, but these machines, and the reasoning patterns they use in chess and backgammon cannot without major modification indulge themselves in performing financial prediction or proving difficult theorems in mathematics. The building of intelligent machines that can think in multiple domains is at present one of the most difficult outstanding problems in artificial intelligence. Some progress is being made, but it has been stymied again by overindulgence in philosophical speculation and rancorous debates on the nature of mind and whether or not machines can have true emotions.

Humans can of course think in multiple domains. Indeed, a good human chess player can also be a good mathematician or a good chef. The ability to think in multiple domains has been christened as "commonsense" by many psychologists and professional educators, and those skeptical of the possibility of machine intelligence. It is thought by many that in order for a machine to be considered as truly intelligent, or even indeed to possess any intelligence at all, it must possess "commonsense", in spite of the vague manner in which this concept is frequently presented in both the popular and scientific literature.

The nature of "commonsense" is explored in an atypical manner in this book, and in this regard the author again shows his ability to think outside of the box and phrase issues in a new light. This is not to say that advice on how to implement these ideas in real machines is included in the book, as it is not. But the ideas do seem plausible as well as practical, particularly the concept of a "panalogy", which is the author's contraction of the two words "parallel analogy". A panalogy allows a machine (human or otherwise) to give multiple meanings to an object, event, or situation, and thus be able to discern whether a particular interpretation of an event is inappropriate. A machine good in the game of chess could possibly then give multiple interpretations to its moves, some of which may happen to be similar to the interpretations given to a musical composition for example. The machine could thus use its expertise in chess to write musical compositions, and therefore be able to think in multiple domains. On the other hand, the machine may realize that there are no such analogies between chess and musical composition, and thus refrain from attempting to gain expertise in the latter. Another role for pananalogies, which may be a fruitful one, is that they can be used to measure to what degree interpretations are "entangled" with each other. Intepretations, which are the results of thinking, algorithmic processing, or reasoning patterns as it were, could be entangled in the sense that they always refer to objects, events, or situations in multiple domains. A panalogy, being a collection of interpretations in one domain, could be entangled with another in a different domain. The machine could thus switch between these with great ease, and thus be effective in both domains. It remains of course to construct explicit examples of panalogies that can be implemented in a real machine. The author does not direct the reader on how to do this, unfortunately.

The author also discusses a few other topics that have been hotly debated in artificial intelligence, throughout its five-decade long history, namely the possibility of a conscious machine or one that displays (and feels!) genuine emotions. The nature of consciousness, even in the human case, is poorly understood, so any discussion of its implementation in machines must wait further clarification and elucidation. Contemporary research in neuroscience is giving assistance in this regard. The author though takes another view of consciousness, which departs from the "folk psychology" that this concept is typically embedded in. His view of consciousness is more process-oriented, in that consciousness is the result of more than twenty processes going on in the human brain. An entire chapter is spent elaborating on this view, which is highly interesting to read but of course needs to be connected with what is known in cognitive neuroscience.

It remains to be seen whether the ideas in this book can be implemented in real machines. If the author's views on emotions, commonsense, and consciousness are correct, as detailed throughout the book, it seems more plausible that machines will arise in the next few years that have these characteristics. If not, then perhaps machine intelligence should be viewed as something that is completely different from the human case. The fact that hundreds of tasks are now being done by machines that used to be thought of as the sole province of humans says a lot about the degree to which machine intelligence has progressed. Whenever the first machines are constructed to operate and reason in many in different domains, it seems likely that they will have their own ideas about how to direct further progress. Their understanding of ideas and issues may perhaps be very different than what humans is, and they may in fact serve as directors for further human advancement in different fields and contexts, much like the author has done throughout a major portion of his life.

I've read this excellent book twice. Each time I've read "The Emotion Machine" I've gain a clear insight into how the human brain works and how we can design truly intelligent machines. I can also see clearer the future of designing transhuman and posthuman minds. The best high-level AI book yet.

It was hard yet fascinating reading. Very terse and illuminating book on the subject. There are few ready solutions inside for those who seeks understanding of our brain's inner workings. However it is a bookful of tools and strategic directions on where it might be fruitful (or more preciously,fruitlress) to search. A brilliant scientist who has spent his entire life building various kinds of minds shares his insights.

Marvin Minsky, a pioneer of Artificial Intelligence research, conveys a clear understanding of how the brain recognizes and interprets the world around us. He first shows how tasks that may seem simple are actually difficult for a computer to perform because they require an understanding of many details and facts about the situation. Then, he explains how the brain must be organized in order to rapidly categorize sensory input, compare with memories of prior experiences, and select methods or actions to accomplish the desired result.

This book discusses many interesting ideas about human minds in general and provides a plausible framework for understanding our own thought processes.

Anyone working on cognitive systems will want

this book in their library. In reviewing THE EMOTION

MACHINE there are two lines of criticism that seem

important. Firstly, with the behaviorists I would

argue that introspection is both frequently inaccurate

and unscientific. Secondly, and more significantly,

most of Minsky's theories have not been developed to

the level of detail needed in order to formulate

actual algorithms. (To be fair there is Riecken's

"M system" (in SOFTWARE AGENTS, J. M. Bradshaw, Ed.,

MIT Press, 1997) and Singh's thesis (EM-ONE, PhD

thesis, MIT, June 2005) which are at least a start in

that direction.)

On the positive side I am in general agreement

with Minsky that thought can be decomposed into

subroutines like:

remembering (search)

generalization

comparison

explanation

deduction

organization

induction

classification

concept formation

image manipulation

feature detection

analogy

compression

simulation

value assessment

My list appears in Asa H: A hierarchical architecture

for software agents (Transactions of the Kansas

Academy of Science, vol. 109, No. 3/4, 2006). Minsky

calls these "ways to think" and a partial list

appears on pages 226-228 of THE EMOTION MACHINE.

My own Asa H software uses exactly these mechanisms

but my architecture is not nearly as complex as

what Minsky is looking for.

Interesting read on a theoretical framework for Artificial Intelligence and Emotions. Expansive theory is quite invigorating. Good look into Psychology and epistemology of the mind.

I bought this book thinking it would have something to do with endowing AI systems with feelings, which seemed like it could be fascinating read. What I found however was a book that did its level best to drain anything as interesting as feelings out of the text and instead used a method of slicing the operation of the human mind down into trivially simple processes, or what the author calls resources, such as IF ..DO rules that specify what do in response to some stimulus, Selectors which choose among possible response, Critics which are built to recognize certain conditions. There are also instinctive reactions such as hunger, fear, and anger which operate at a lower level. The author's contention is that "suitcase words" like emotion, conscience, consciousness are hard to understand and explain because we pack too many different ideas into them, therefor we need dissect them into simpler single-purpose processes, the combinations and interactions of which we can then describe more intelligently and fruitfully. Applying this approach to the hard problem of consciousness he writes that "the sensation of seeing something red" could be demystified if we were able to make observations like the following about the lower level operations in the brain: "My resources have classified certain stimuli, and then made some representations of my situation, and some of my Critics changed certain plans I had made, and altered some ways in which I was perceiving things, and this led to the following sorts of cascades, and so forth." This emphasis of breaking mental operations down into smaller chunks obviously comes from the experiences and practices of Artificial Intelligence researchers where starting with simple primitives and combining them into more complex layers is probably the only feasible approach. However, for this reader the method has the disadvantage that it makes the story exceedingly dry and non-illuminating. Sure, I see how you can talk about "consciousness detecting critics" which recognize certain processes and then send signals other parts of the brain which would enable your language system to articulate your condition with words like conscious, aware, I am seeing red. But that doesn't give me any aha experience of understanding. I still don't know if Professor Minsky's red looks like my red.

I'm studying simaltaneously Koestler authored "the ghost in the machine". Both relates with philosophical perspectives rooted in the given civilization in terms of the fundamentals of the given humanities. Majored field is diferrent from.

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A very nice book of a very smart person.

Rende più visibile la connessione tra AI e machine lerning

This is a dense and interesting book for the person seriously thinking about artificial intelligence.