On Intelligence |  | Authors: Jeff Hawkins, Sandra Blakeslee
Publisher: St. Martin's Griffin
Category: Book
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Rating:  123 reviews
Sales Rank: 60,547
Media: Paperback
Edition: First Edition
Pages: 272
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Dimensions (in): 8.1 x 5.4 x 0.8
ISBN: 0805078533
Dewey Decimal Number: 612.82
EAN: 9780805078534
ASIN: 0805078533
Publication Date: August 1, 2005
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Amazon.com Review
Jeff Hawkins, the high-tech success story behind PalmPilots and the Redwood Neuroscience Institute, does a lot of thinking about thinking. In On Intelligence Hawkins juxtaposes his two loves--computers and brains--to examine the real future of artificial intelligence. In doing so, he unites two fields of study that have been moving uneasily toward one another for at least two decades. Most people think that computers are getting smarter, and that maybe someday, they'll be as smart as we humans are. But Hawkins explains why the way we build computers today won't take us down that path. He shows, using nicely accessible examples, that our brains are memory-driven systems that use our five senses and our perception of time, space, and consciousness in a way that's totally unlike the relatively simple structures of even the most complex computer chip. Readers who gobbled up Ray Kurzweil's (The Age of Spiritual Machines and Steven Johnson's Mind Wide Open will find more intriguing food for thought here. Hawkins does a good job of outlining current brain research for a general audience, and his enthusiasm for brains is surprisingly contagious. --Therese Littleton
Product Description
From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines
Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.
Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.
The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.
In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.
Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.
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Showing reviews 1-5 of 123
 Simply Indispensable October 8, 2004
Bruce Gregory (Deep River, Connecticut)
127 out of 146 found this review helpful
It is not very often that you encounter a book that alters, not simply what you think, but how you look at the world. On Intelligence is such a book. Jeff Hawkins develops a perspective on intelligence that makes sense of much of what I have discovered about learning over the past twenty years. His focus is on a unified model of how the cortex works, but in truth you do not need to have deep interest in neurobiology to see the power of the model. The book is very clear and readable, something I have learned to associate with Sandra Blakeslee's deft touch (see, for example, Phantoms In the Brain, by Ramachandran and Blakeslee). The heavy lifting occurs in the lengthy sixth chapter, "How the Cortex Works." You might want to skim this chapter or even omit it entirely on your first reading. It is well written, but requires a very thoughtful reading. The model Hawkins develops in this chapter underpins his view of intelligence, but it is not necessary to grasp the details to appreciate the power of the vision. If you have the slightest interest in the role of the brain in making us who we are, you owe it to yourself to read this book. I couldn't recommend it more highly.
A Great Intro to Even Greater Insights February 18, 2005
Jane E. Carroll
34 out of 36 found this review helpful
The accolades previous reviewers have lavished upon this book are all fully deserved. It is not, however, "the first time all these bits and pieces have been put into a coherent framework". The work of Stephen Grossberg explored all of these themes in the 1970s. Unfortunately Grossberg expressed his key insights in systems of differential difference equations that few could understand and fewer still could build upon or contribute to.
To his credit, Hawkins does cite Grossberg approvingly at several junctures in his argument, but he fails to take into account several of Grossberg's greatest insights into neocortical processing: his theory of how serial processing can be accomplised in a parallel anatomy and his theory of "rebounds". The latter is especially important since it explains how new memories are prevented from overwriting old memories. For example, when I learn a second language, it doesn't overwrite my first.
These criticisms, however, are in no way meant to detract in the slightest from Hawkins' superb book. It is an eminently readable account of neocortical computing, and correct in all its broad brush strokes. If you are as beguiled by "On Intelligence" as the other reviewers in this thread, my purpose is only to alert you to the even deeper wonders that are to be found in Grossberg's work. As I have said, his work is difficult, but his 1980 and 1982 Psychological Review articles will provide good entry-points. Those of you with an interest in brain and language will find an even better second course in neocortical computing in Loritz' "How the Brain Evolved Language" (Oxford University Press, 1999).
Central Dogma for the Brain September 29, 2004
Donald B. Siano (Westfield, NJ USA)
29 out of 31 found this review helpful
Jeff Hawkins is the man who was the architect of the PalmPilot, the Treo, and invented Graffiti, an alphabet for inputing data to a computer with a stylus. But this book is about his other love, the deciphering of the code that makes the human brain work. There is nothing like a big, important puzzle to get the blood working, and mine was powerfully pulled along . With the human genome project's sequencing of human DNA nearly completed, understanding the brain has got to be the most important scientific undertaking one can think of. Hawkins easily persuades us that there is a burning need for a "top down" model for the brain that can play a role something analogous to the Central Dogma of molecular biology, which guides and organizes research, prioritizing the myriad of possible tasks into something like that required for the logistics of a conquering army's march through an alien land.
He also persuaded me that he has some important insights of that model that I found tantalizing, new and exciting. His central model concerns the role of the cortex in producing intelligence. He makes the case for a central dogma he calls "the memory-prediction framework." This idea says that the cortex is a machine for making predictions for temporal sensory patterns based on memories of past patterns. The prediction algorithm carried out in the cortex is the same for all of the senses of vision, touch, hearing, etc., which accounts for, among other things, the basic physiological uniformity of the cortex, and the plasticity of the brain in adapting to such problems as blindness or deafness.
He argues that since the "clock" of the brain operates at a tick-rate on the order of 5 milli-seconds, and most of the functions of the brain (e. g. recognizing that a picture of a cat shows a cat) are carried out in less than 100 ticks. From the time that light enters the eye, to the time it takes to signify recognition takes less than a second. A computer would take billions of instruction steps, and even the fastest parallel computer available would not do it in less than millions of steps. So the brain doesn't really "compute" the answer, it retrieves it from memory, which requires far fewer steps than the computation. Sounds good to me.
His explication of the memory-prediction framework is clear and accessible even to the uninitiated like me, though I found some of it in the middle pretty heavy going. But this is something like reading Watson and Crick's paper on the structure of DNA. The part about turning the diffraction diagram and other insights into a workable model was a little above my head, but I could still see the importance of the answer, and how it addressed the problem of replication and how it gave clues as to how to "read the genes." I can only grasp part of what Hawkins has done, and I can see that there is still a long way to go. But I can still jump up and down about it!
Loved the book, holes in the theory October 19, 2004
Gary R. Bradski (Palo Alto, CA USA)
42 out of 49 found this review helpful
RECOMMENDATION:
===============
This is one of the few books to posit a theory of human and general intelligence, and the only book of the few that is clear and well written. I think it is seminal will re-ignite interest and activity in building intelligent machines. A pleasant interesting read even for those not working in the field.
COMMENT ON CONSCIOUSNESS:
==========================
In one sense, this is a side issue since one can probably build intelligent cars, vacuum cleaners and search agents without consciousness, but in another sense it's a crucial aspect of our experience. Hawkins claims that consciousness is just what it "feels" like to have a cortex. I differ. My guess builds precisely on Hawkins suggestion that the cortex is a generative (my word, his is associative completion) hierarchy. That is, we synthesize/simulate the external world inside our head. But, we're a social creature and place a lot of value, evolutionary and otherwise on being able to imagine/simulate the mental state of other people ("my boss will be angry if I do that", "she likes me", ...). Yet, as a mater of simple functioning, we must also simulate ourselves in the world to know how to act. In my mental world, I simulate myself when I consider whether I can squeeze through a gate or lift a weight. When our simulation of mental state became grafted to our simulation of self, I think consciousness came about as an epiphenomena - consciousness is our simulation of our selves, of our own internal state.
ONCE YOU GET THROUGH CHAPTER 6:
===============================
Some holes which might exist either in my brain or in Jeff Hawkin's theory:
ATTENTION:
P-173 Attention gets pretty short shrift in the presented theory down to an alternative, hierarchy bypassing pathway in the Thalamus that gets turned on by higher regions if unexpected events occur bellow or the higher region is directed externally - the last is somewhat circular reasoning: attention is turned on if attention is directed. John Reynolds at Salk has been studying visual attention in monkeys and is finding evidence of boosting or diminution of contrast is what visual attention is doing so that visual items win or loose the inhibitory competition between features and that this is perhaps what lets some items rise up to conscious notice.
Attention is fairly sequential and substantially bottlenecked for what it can process (see "change blindness" illusions http://viscog.beckman.uiuc.edu/djs_lab/demos.html ). In many of these illusions, you don't notice when huge portions of the visual scene change, items appear or disappear etc.
CEASLESS RECODING OF MEMORY:
Hebian learning is great, except that it also unlearns equally well. I quote Grossberg's term for the problem in caps above. Memory needs some kind of gating mechanism or it will rapidly turn into mud. Either memory is unidirectional (connections start out high and only shrink, or starts out low and only grows), and/or there is a gating mechanism that isn't well explained here. What stabilizes learning? P-136: a purple "bucket" became "indigo" (or a page earlier, orange is placed in "red"). First of all, this can shoot down a whole painstakingly learned hierarchy of learning above - in general, a bad move. Ever done visual tracking algorithms? - if you allow your template to adapt a little bit in say tracking a face, pretty soon a little bit of background "wall" starts entering the template and pretty soon "wall" becomes your (very stable) "face" template. The same thing will happen here - color buckets will randomly turn into each other, drift around - chaos. Just like our legal system, most new rulings should have very local effects and only very rarely will something ripple changes through the larger system. If this happens too often, the whole structure collapses.
Finally, this ignores all the critical period stuff in learning. Some things are laid down early and in clear order and they don't seem to change and if not learned early just cannot be learned. Famous study of this is "Kitten in the carousal" where kittens are raised in the dark and only get to walk in the light in short intervals where one cat can move but is mechanically yoked to another cat who sees the exact same things, but is stuck in a carousal (a little box) so that it's leg movements don't control it's movements. If this is done too long, the poor kitten in the carousal never learns to see (depth) at all! Even when let go into the light. If let go early enough, it will learn to see normally.
Long winded, but: Seems to me that some basic categories and features must be developed early and not allowed to change in order to have any chance of building a larger structure over them.
TIME:
Where did it go? I see sequences, but not timing - you can't control your muscles without actual timing, not just sequence. In fact, time itself is yet another unstated sense. There are clearly integration rates that are learned and used in recognition, planning decisions etc.
INVARIANCE/FEATURE SELECTION:
I still don't get exactly how invariance is found by this architecture beyond things that can be predicted which is somewhat of a tautology - yes, invariant features make your life easy, but beyond dumb luck, how do you find them? When you identify a dirt road by parallel tracks in the soil, what inside you is discovering the cross ratio projective invariance? How did we learn brightness normalization? Color constancy? Some of this stuff involves tricks in active diffusion of color information from edges and clever local integration. Is this learned or built in? Insects must have to deal with this and must be born with it. How do they do it?
GATING:
P-158: Thinking of doing becomes doing. Yes, but how to you stop this from happening? Indeed, how do you start one invariant representation of say the Gettysburg Address from being spoken, written by all limbs and done in interpretive dance once you think to do it?
Minor nits:
P-71 While I believe that the fundamental unit of processing is a kind of sequential associative memory, the fact that you think or recall serially doesn't prove this - perhaps you can recall everything in you house at once, but internal or external output is one thing at a time and nearby things just have a scotch more support.. Detailed motor execution is more compelling.
I could have done with a final summary 2 side to side page cortical sheet diagram with Thalamus, Hippocampus, and at least 2 layers of hierarchy with all the basic communication channels and their direction shown, even better with text referencing where these things were described.
Gary
Shows the way ahead... November 12, 2004
Ajay Bakshi (Philadelphia, USA)
22 out of 24 found this review helpful
I am a neurosurgeon and I picked up this book with a great deal of scepticism because in the past, neither through my professional studies nor through reading many popular books, have I really been able to answer some fundamental questions regarding our brain. Questions like how do we think? what is imagination? what happens during the so called "a-ha" phenomenon, when you suddenly understand something that you did not a moment ago and many other such questions have been plaguing me for years. And believe me, there arent many satisfactory answers floating around.
Hawkins has made a fantastic contribution by giving us A model to think about these questions. His memory-prediction paradigm is very attractive intuitively because it automatically explaines so many facts about our brains and their evolution that other theories just ignored. But even when tested in hard scientific experiments, I predict that the basic structure of his arguement will remain intact, though details may differ.
We still do not know a lot about the architecture of the brain, the way neurons are connected to each other and the way brains develop their enormous computive capabilities. As we learn more, it is likely that Hawkins paradigm will be refined, but in the long run, we will owe Hawkins gratitude for allowing our brains to understand how our brains work!!
Showing reviews 1-5 of 123
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