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On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines Adapted Edition, Kindle Edition

4.5 out of 5 stars 859 ratings

The inventor of the PalmPilot shares a compelling new theory of intelligence, brain function, and the future of artificial intelligence.

Tech innovator Jeff Hawkins reshaped our relationship to computers with devices like the PalmPilot. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself. In this book, Hawkins develops a powerful theory of human cognition and explains how, based on his theory, we can finally build intelligent machines.

According to Hawkins, the brain is a complex system that remembers sequences of events and their nested relationships. This style of organization reflects the true structure of the world and allows us to make increasingly accurate predictions. This memory-prediction process in turn forms the basis of intelligence, perception, creativity, and even consciousness.

In an engaging style accessible to the general reader, Hawkins shows how a clear understanding of brain function can be applied to building intelligent machines, in silicon, that will exceed our human ability in surprising ways. Written with acclaimed science writer Sandra Blakeslee,
On Intelligence is a landmark book in its scope and clarity.

“Brilliant and imbued with startling clarity . . . the most important book in neuroscience, psychology, and artificial intelligence in a generation.” —Malcolm Young, University of Newcastle

Editorial Reviews

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

From Publishers Weekly

Hawkins designed the technical innovations that make handheld computers like the Palm Pilot ubiquitous. But he also has a lifelong passion for the mysteries of the brain, and he's convinced that artificial intelligence theorists are misguided in focusing on the limits of computational power rather than on the nature of human thought. He "pops the hood" of the neocortex and carefully articulates a theory of consciousness and intelligence that offers radical options for future researchers. "[T]he ability to make predictions about the future... is the crux of intelligence," he argues. The predictions are based on accumulated memories, and Hawkins suggests that humanoid robotics, the attempt to build robots with humanlike bodies, will create machines that are more expensive and impractical than machines reproducing genuinely human-level processes such as complex-pattern analysis, which can be applied to speech recognition, weather analysis and smart cars. Hawkins presents his ideas, with help from New York Times science writer Blakeslee, in chatty, easy-to-grasp language that still respects the brain's technical complexity. He fully anticipates—even welcomes—the controversy he may provoke within the scientific community and admits that he might be wrong, even as he offers a checklist of potential discoveries that could prove him right. His engaging speculations are sure to win fans of authors like Steven Johnson and Daniel Dennett.
Copyright © Reed Business Information, a division of Reed Elsevier Inc. All rights reserved.

Product details

  • ASIN ‏ : ‎ B003J4VE5Y
  • Publisher ‏ : ‎ Times Books; Adapted edition (April 1, 2007)
  • Publication date ‏ : ‎ April 1, 2007
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 2.6 MB
  • Text-to-Speech ‏ : ‎ Enabled
  • Screen Reader ‏ : ‎ Supported
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Enabled
  • Print length ‏ : ‎ 284 pages
  • Page numbers source ISBN ‏ : ‎ 0805074562
  • Customer Reviews:
    4.5 out of 5 stars 859 ratings

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4.5 out of 5 stars
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Customers say

Customers find the book insightful, containing more insights into brain function and explaining various aspects of intelligence. Moreover, the writing is clear and concise, making complex subjects accessible, and customers describe it as exciting and entertaining. Additionally, the book receives positive feedback for its style and pattern recognition approach, with one customer noting how the neocortex stores sequences of patterns, and another highlighting its memory-based perfective model.

AI-generated from the text of customer reviews

126 customers mention "Insight"108 positive18 negative

Customers appreciate the book's insights into brain function and intelligence, with one customer noting it provides a solid basis for brain theory.

"...I think this theory of how the cortex works makes a lot of sense, and I am grateful to Hawkins and Blakeslee for writing it in a book that is..." Read more

"...brains are responsible for, he proposes that intelligence is best understood as predictive capacity and that it is basically a function of the cortex..." Read more

"...reading how the human brain is "wired." Rather, it explains more fully how the system "hangs together" and accomplishes the..." Read more

"...mechanisms of the cortex, we quickly learn how the brain can build intelligent machines. "Prediction is not just one of the things your brain does...." Read more

73 customers mention "Readability"73 positive0 negative

Customers find the book readable and enjoyable, with one customer noting it is well presented and another describing it as an excellent exploration of the human mind.

"...I think this is a fascinating and stimulating book...." Read more

"In this very well written book, Hawkins and Blakeslee describe a new model of how our human intelligence has evolved, how it "works" and..." Read more

"...with an interest in true artificial intelligence, this is a very stimulating book...." Read more

"...It's a quick read that will without a doubt have a significant impact on how you view the future of artificial intelligence...." Read more

59 customers mention "Clarity"48 positive11 negative

Customers appreciate the book's clarity, finding it written in a language that anyone can understand and presenting complex subjects in a clear and concise way.

"...any other dry and cold papers you may have read, this one is written in very personal and engaging style...." Read more

"...A plug for the book, though, is that it is engagingly written, and if you like to read about the science of what constitutes intelligence, how..." Read more

"...It's concise and to the point and if you have any interest whatsoever in AI you simply can not miss this it...." Read more

"...Yet, I'd still recommend this book, because it highly readable, and it'll make you think (even if it's about the outrageous claims)...." Read more

9 customers mention "Storytelling"9 positive0 negative

Customers enjoy the storytelling of the book, finding it exciting and entertaining, with one customer noting that Chapter 6 contains the most interesting information.

"...papers you may have read, this one is written in very personal and engaging style...." Read more

"This book is well worth the read. You'll find it engaging and the pages should turn quickly...." Read more

"Makes a complex subject readable and exciting, but I don't know to what extent author's explanation of physiology of memory is speculative or backed..." Read more

"Chapter 6 has most of interesting information, but even that chapter could have been more concise. Great overall idea though." Read more

9 customers mention "Style"9 positive0 negative

Customers appreciate the style of the book, finding it very enjoyable to read, with one customer noting that the theory is elegant and another mentioning how beautiful it is to see all the concepts come together.

"...Nature, he argues, chose a simpler, more elegant and, in the end, superior way: a simple patterning/predicting algorithm...." Read more

"...All in all a very enjoyable look at one man's vision for the future of intelligent machines in one nice, tidy, unified presentation." Read more

"...Their theory is elegant and, even though far from complete, I am thoroughly convinced that it is a decent approximation of what goes on in the brain...." Read more

"...It was beautiful to have it all come together. However, beyond the initial insight, there seemed to be little further use to the book...." Read more

8 customers mention "Pattern recognition"8 positive0 negative

Customers appreciate the book's approach to pattern recognition, noting that it correctly describes hierarchical pattern recognition and uses patterns predictively. One customer mentions that the neocortex stores sequences of patterns, while another notes how it handles variations in the world automatically.

"...performs the function of intelligence via a relatively simple, uniform algorithm, contrary to the general opinion in AI circles which presumes the..." Read more

"...These representations allow you to handle variations in the world automatically...." Read more

"...saying that the neocortex stores sequences of patterns, recalls patterns auto-associatively, stores invariant patterns, and stores patterns in a..." Read more

"...provides deep insights into the workings of the brain and proposes several models where none presently exist...." Read more

5 customers mention "Memory storage"5 positive0 negative

Customers appreciate the book's approach to memory storage, with one customer highlighting its hierarchical temporal memory structure, while another notes its focus on Big Data.

"...'s used this theory of intelligence as ability to predict and learn via memory storage to create the PalmPilot and the Graffiti handwriting software..." Read more

"...fields within the next 10 years - and with the current explosion of interest in Big Data, Machine Learning, and applications like SIRI it is hard to..." Read more

"...More specifically, I found the memory-based perfective model as base of the intelligence, not only fascinating, but also the best idea on the subject." Read more

"Describing the Hierarchical Temporal Memory structure/function of the human brain and how that structure and functionality may be used in..." Read more

Top reviews from the United States

  • Reviewed in the United States on April 26, 2010
    The book is about Hawkins' theory of how the mammalian cortex, especially the human cortex, works. Hawkins thinks it is only by understanding the cortex that we will be able to build truly intelligent machines. Blakeslee has aided him in presenting this theory so that it is accessible by the general public. I am very impressed by the theory of the cortex, but I do not agree that the cortex is the only way to achieve intelligence.

    Hawkins defines intelligence as the ability to make predictions. I think this is an excellent definition of intelligence.

    He says the cortex makes predictions via memory. The rat in the maze has a memory which includes both the motor activity of turning right and the experience of food. This activates turning right again, which is equivalent to the prediction that if he turns right, food will occur.

    The primate visual system, which is the sense best understood, has four cortical areas that are in a hierarchy. In the lowest area, at the back of the head, cells respond to edges in particular locations, sometimes to edges moving in specific directions. In the highest area you can find cells that respond to faces, sometimes particular faces, such as the face of Bill Clinton.

    But the microscopic appearance of the cortex is basically the same everywhere. There is not even much difference between motor cortex and sensory cortex. The book makes sense of the connections found in all areas of the cortex.

    The cortex is a sheet covering the brain composed of small adjacent columns of cells, each with six layers. Information from a lower cortical area excites the layer 4 of a column. Layer 4 cells excite cells in layers 2 and 3 of the same column, which in turn excite cells in layers 5 and 6. Layers 2 and 3 have connections to the higher cortical area. Layer 5 has motor connections (the visual area affects eye movements) and layer 6 connects to the lower cortical area. Layer 6 goes to the long fibers in layer 1 of the area below, which can excite layers 2 and or 3 in many columns.

    So there are two ways of exciting a column. Either by the area below stimulating layer 4, or by the area above stimulating layers 2 and 3. The synapses from the area above are far from the cell bodies of the neurons, but Hawkins suggests that synapses far from the cell body may fire a cell if several synapses are activated simultaneously.

    The lowest area, at the back of the head, is not actually the beginning of processing. It receives input from the thalamus, in the middle of the brain (which receives input from the eyes). Cells in the thalamus respond to small circle of light, and the first stage of processing is to convert this response to spots to response to moving edges.

    And the highest visual area is not the end of the story. It connects to multisensory areas of the cortex, where vision is combined with hearing and touch, etc.

    The very highest area is not cortex at all, but the hippocampus.

    Perception always involves prediction. When we look at a face, our fixation point is constantly shifting, and we predict what the result of the next fixation will be.

    According to Hawkins, when an area of the cortex knows what it is perceiving, it sends to the area below information on the name of the sequence, and where we are in the sequence. If the next item in the sequence agrees with what the higher area thought it should be, the lower area sends no information back up. But if something unexpected occurs, it transmits information up. If the higher area can interpret the event, it revises its output to the lower area, and sends nothing to the area above it.

    But truly unexpected events will percolate all the way up to the hippocampus. It is the hippocampus that processes the truly novel, eventually storing the once novel sequence in the cortex. If the hippocampus on both sides is destroyed, the person may still be intelligent, but can learn nothing new (at least, no new declarative memory).

    When building an artificial auto-associative memory, which can learn sequences, it is necessary to build in a delay so that the next item will be predicted when it will occur. Hawkins suggests that the necessary delay is embodied in the feedback loop between layer 5 and the nonspecific areas of the thalamus. A cell in a nonspecific thalamic area may stimulate many cortical cells.

    I think this theory of how the cortex works makes a lot of sense, and I am grateful to Hawkins and Blakeslee for writing it in a book that is accessible to people with limited AI and neuroscience.

    But I am not convinced that the mammalian cortex is the only way to achieve intelligence. Hawkins suggests that the rat walks and sniffs with its "reptilian brain", but needs the cortex to learn the correct turn in the maze. But alligators can learn mazes using only their reptilian brains. I would have been quite surprised if they could not.

    Even bees can predict, using a brain of one cubic millimeter. Not only can they learn to locate a bowl of sugar water, if you move the bowl a little further away each day, the bee will go to the correct predicted location rather than to the last experienced location.

    And large-brained birds achieve primate levels of intelligence without a cortex. The part of the forebrain that is enlarged in highly intelligent birds has a nuclear rather than a laminar (layered) structure. The parrot Alex had language and intelligence equivalent to a two year old human, and Aesop's fable of the crow that figured out to get what he wanted from the surface of the water by dropping stones in the water and raising the water level, has been replicated in crows presented with the problem.
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  • Reviewed in the United States on September 11, 2009
    Hawkins and his co-writer, Sandra Blakeslee, offer an intriguing analysis of what the brain does to produce intelligence, a very sticky subject any way you cut it. Separating intelligence from other familiar features of the conscious mind which brains are responsible for, he proposes that intelligence is best understood as predictive capacity and that it is basically a function of the cortex, the latest add-on to animal brains and which appears to be largest and/or most developed in humans among all other mammals. Wrapping the older parts of the brain (what Hawkins calls the "lizard brain"), Hawkins proposes that the cortex performs the function of intelligence via a relatively simple, uniform algorithm, contrary to the general opinion in AI circles which presumes the need for many complex and integrated algorithms.

    Taking his lead from Johns Hopkins neuroscience researcher Vernon Mountcastle back in the seventies, Hawkins presumes that the remarkably uniform appearance of the cortex (it basically consists, he tells us, of six layers of neuronal cells throughout) suggests that the various areas of the cortex, demonstrated by researchers to be responsible for different functions (vision, touch, hearing, conceptualizing, etc.), really do everything they do by performing the same processes. He is clear, of course, to emphasize that he is not talking about other things brains presumably do including emotions, instinctual drives, somatic sensations, etc. which he assigns to the lizard brain. It's just the intelligence part that he is interested in though he's certainly aware that for intelligence to work as it does in us it must be integrated with the broad range of other features found in consciousness including those produced in the lizard brain. So his argument is not that the cortex, in its special capacity, is a stand-alone but that it is a significant and inextricable add-on to the rest of our brain and works only with and in support of the other features.

    For Hawkins, the key to understanding how the cortex does intelligence comes down to understanding the pertinent algorithm. He argues that neuronal groups work in two hierarchical ways, both up and down the line in linked columns spanning the six layers of neurons, found more or less uniformly throughout the cortex, and also by combining and linking different cortical areas horizontally (responsible for different functions , e.g., shapes, colors, sound, touch, taste, smell, language, motor control) in other, non-physically determined (because non physically contiguous) hierarchies via links established between cortical layers through extension of myriads of cellular axons traveling transversely across the cortical areas AND to other parts of the lizard brain (each of which axon produces multiple connections, through the tree-like dendrites at its end points, resulting in difficult to estimate -- but likely in the hundreds of millions [or more] -- connections).

    The basic cortical algorithm, performed by all these interconnecting neurons in the cortex, on Hawkins' view, is one of patterning and of the capture and retention of so-called "invariant representations". He argues that human memory is not precise, the way computational memory is (a case made, as well, by Gerald Edelman in his own work). But, where Edelman (Bright Air, Brilliant Fire: On The Matter Of The Mind) emphasizes the dynamic and incomplete quality of human recollections, Hawkins emphasizes their general nature. We don't remember things precisely, in detail, he says, but, rather, in only general patterns (adumbrations rather than precise images).

    This, he suggests, is because of the basic patterning algorithm of the neuronal group operations in the cortex.

    When information flows in, he says, various neurons in the affected groups fire, in very fine detail, much as our taste buds operate in the tongue with different nerves for the different tastes which then pass the captured information up the line to combine further upstream via the brain's more comprehensive processes. In the vision parts of the cortex for instance, Hawkins notes that some cortical cells at the input end of the relevant cellular columns will fire in response to vertical lines, others to horizontals or diagonals, while others, nearby, presumably pick up color information, etc. The various firings pass up the line in increasingly broad (and more generalized) combinations, eventually losing much of the detail but generating patterns driven by the lower level details received.

    At the highest level of the cortex, Hawkins reasons we have only the broadest, most general pictures, combining the increasingly broad and more general patterns passed up from below with related general patterns from other areas (say visual patterns with touch patterns and sound patterns, etc.) to give us still larger patterns via associative linkage. When new inputs come in (as they are constantly doing) the passage of the information up the line encounters the stored general patterns higher up which respond by sending signals down the same routes (and also down our motor routes if and when actions are called for).

    The ability of the incoming inputs to match stored generic patterns higher up (when the information coming down the line matches the information heading up) is successful prediction. When there is no match, prediction fails and new general patterns form at the higher end of the cortical columns to replace the previous patterns. Thus memory in us is seen as an ongoing adjusting process with repetitive matches producing stronger and stronger traces of previously stored patterns.

    Because patterning happens at every level, a kind of pyramid of patterns from the lowest level in the cortex to the highest is seen. At all levels, associative mechanisms are utilized and, at the highest levels, these connect and combine multiple specialized patterns into still larger overarching representational patterns. The capacity to retain invariant representations at all levels, until adjustments are made, gives us the invariant representational capability that forms the basis of human memory and underlies prediction which, he thinks, is what we mean by "intelligence" (i.e., the dynamic process of matching old patterns to new inputs where the more successful the matching, the more "intelligent" we deem the operations performed).

    So the cortex, on this view, is a "memory machine" (as Hawkins puts it), using a patterning and matching mechanism to constantly fit the stored representations held in the cortex to the world. And intelligence is seen as the outcome of this massive process that is constantly going on in our brains, i.e., the ability to quickly adjust to incoming information and make successful predictions about it. It's this increasingly complex and generalizing capacity of cortexes, he argues, that gives us the ability to construct and use massively complex pictures of the world around us (the source of our sensory inputs)*.

    Hawkins thinks that this is a whole different way of conceiving of intelligent machines, replacing the notion prevalent in mainstream AI that the way to build machine intelligence is to construct massive systems of complex algorithms to perform intelligent functions typical of human capability. Instead, of that, he proposes, we need to concentrate on building chips that will be hardwired to work like cortical neurons in picking up, storing and matching/adjusting a constant inflow of sensory information and which can then be linked in a cortex-like architecture matching the cortical arrangements found in human brains.

    Such machines, he proposes, will learn about their world in a way that is analogous to how we do it, build pictures based on sensory information received, recognize patterns and connections and think out of the more confining algorithm-intensive computational box.

    Hawkins notes that we don't have to give such machines the kinds of sensory information available to humans and suggests that there is a whole range of different kinds of sensory inputs that might make more sense for such machines, depending on what complex operations they are built to perform (which may include security monitoring, weather prediction, automobile control or work in areas outside ordinary human safety zones, say in outer space, in high radiation areas or at great depths on the ocean floor). Nor does he think we have to worry about such machine intelligences supplanting us (a la The Matrix) since there is no reason, he argues, that we would have to give such machines drives or feelings, or even a sense of selves such as we have, any of which might make them competitors to humans in our own environment. (Of course, it bears noting that we don't really have any idea of how brains produce drives and selves, per se, so it's at least a moot question whether we can simply, as Hawkins suggests, resolve not to provide these to such machines. After all, what if the synthetic cortical array he envisions turns out to have some or all of the capabilities Hawkins now thinks are seated beyond the cortex in human brains? In such a case, mere resolve not to give such capabilities to the proposed cortical array machines might not be enough!)

    One of the main reasons Hawkins argues for a simple hardwired algorithm configured in a cortex-like architecture, versus a massively computational AI application (as envisioned in many AI circles), is that he believes even the most powerful computers today, with far faster processing capacities than any human brain, cannot hope to keep up with this kind of cortical architecture. He comes to this conclusion because he believes too many steps are involved in order to program intelligence comparable to what humans have, thus requiring a computational platform of vast, likely unwieldy, size, and detailed programming that must prove too monumental to undertake and maintain error-free. Nature, he argues, chose a simpler, more elegant and, in the end, superior way: a simple patterning/predicting algorithm.

    In many ways Hawkins is much better than Gerald Edelman in dealing with the brain since Edelman gets lost in complexities, vagueness and what look like linguistic confusions in trying to describe brain process or argue against the AI thesis. Hawkins, though he limits his scope to intelligence rather than the full range of consciousness features, gives us a much more detailed and structured picture of how the mechanism under consideration might actually work.

    In the end he gives us a picture best understood as arrays of firing cells (think flashing lights) that constantly do what they do in response to incoming and outgoing signal flows, with the incoming reflecting the array of sensory inputs we get from the world outside and the outgoing the stored general patterns that serve as our world "pictures" (not unlike Plato's forms, as he suggests, albeit without the platonistic mysticism) which are built up by the constant inflow.

    Thus, he envisions a constant upward and downward flow of signals in the cortical system which is not only dynamic based on the interplay of the dual directional flow of the signals but is reflective of the facts beyond the brain in the world through the compound construction of invariant representations (occurring at every level of cortical activity). To the extent the invariant representations he describes successfully match incoming signals, they are predicting effectively and the organism depending on them is more likely to succeed in its environment. To the extent they are unable to generate effective prediction, the organism depending on them suffers.

    A key weakness of Hawkins' explanation lies in his failure to either show exactly how the pattern matching and adjusting of the neuronal group hierarchies become the world of which we are consciously aware, in all its rich detail (how mere physical inputs become mind -- the components of our mental lives) and how the cortex integrates the many inputs of the rest of the brain. As John Searle (Minds, Brains and Science (1984 Reith Lectures) and Mind, Language, and Society : Philosophy in the Real World) has noted, our idea of intelligence is very much intertwined with our idea of being aware, being a subject, having experience of the inputs we receive, etc. If we understand something, it's not just that we can produce effective responses to the stimuli received but that we are aware of the meanings of what we're doing, what is going on, etc.

    Hawkins' "intelligence" looks to be a very much truncated form of this, albeit deliberately so, because he wants to argue for intelligent machines that will be "smarter" than computers but not quite smart enough to be a threat to us. Still, despite the fact that he has offered an intriguing possibility, which may well be an important step forward in the process of understanding minds and brains and of building real artificial intelligence, one can't escape the feeling he has still missed something along the way by distancing himself from the question of what it is to be aware -- to understand what one is doing when one is doing it.

    SWM

    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

    * One of the critical differences between us and mammals lower down the development scale, he suggests, is the relative size of our cortexes. Many mammals with smaller brains just have smaller cortexes and, thus, fewer cells there, while some mammals, e.g., dolphins, actually have larger brains but less dense cortexes -- three layers vs. our six. Thus, says Hawkins, the intelligence we have reflects a greater capacity to form representations (covering more inputs, including past and present and a greater capacity for abstraction).
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  • Jorge Luis Gutiérrez González
    5.0 out of 5 stars Just great
    Reviewed in Mexico on February 25, 2019
    Great book, it takes you through a different way of viewing your own intelligence and behavior. I've read half of it and it's great so far.
  • Mike Adams
    5.0 out of 5 stars The breakthrough book on brain science that the neuroscience community missed! Still relevant.
    Reviewed in Australia on September 30, 2018
    The breakthrough book on brain science that the neuroscience community missed!
    Still relevant and groundbreaking in 2018 as deep neural net AI proves Hawkins right.
    This book will change the way you think about your mind. When you understand sequence memory prediction you'll see the world in a different way - a true paradigm shift in the same league as the theory of evolution.
  • Inav
    5.0 out of 5 stars Consigli
    Reviewed in Italy on July 28, 2020
    Ho letto una recensione che critica il modo in cui l'autore espone troppo la sua "bravura". Credo sia stata troppo frettolosa.
    Se siete arrivati sin qui, vi invito ad acquistate il libro e a leggerlo per intero.
    L'autore si mostra come un amico: racconta i suoi successi (senza boria) e i suoi errori (cosa ha pensato erroneamente per molti anni e i "no" ricevuti, ammettendolo senza mezzi termini e senza vergongna).
    In questo libro si trovano dei concetti chiave molto interessanti, che non si trovano facilmente altrove.
    Scorrevole, ricco di esempi, si respira il fermento scientifico e le discussioni tra più discipline.
    Consigliatissimo.
    (Leggete la bibliografia. Si trovano diverse chicche.)
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  • M A Mohamed
    5.0 out of 5 stars Is this the M-Theory of the Intelligent Machines?
    Reviewed in the United Kingdom on September 8, 2014
    Every book you read has its own place in the year you read it. This book has already won my vote to be the best book I run during 2014. Remember, still we are not at the end of the year. I still have a chance to read more books before the end December 2014. Yet, this book has already won my vote.

    Jeff Hawkins and Sandra Blakeslee appear to be doing for Computer Science and Intelligent machines what Edward Witten had done for String Theory. Remember the madness that String Physicists went through till M theory was pronounced in the University of South California sometime 1995!

    If we allow JS to stand for the initials of the two authors, one may conclude Intelligent Machines can be defined as follows:

    JS= IM(neocortex). In other words, intelligent machine are function of our ability to understand and then imitate how the Neocortex works.

    The two others succeeded to simplify a complex subject that made us the dominant animals on planet earth, though we are yet prove our mastery of the space beyond our atmosphere. They truly shed a light on why the AI world and neural network proponents are still struggling to deliver what many of us thought was achievable by the end of the 20st century.

    Even if you do not understand differential equations or even basic algebra, this book will give you an insight of how your brain works in a language that is so simple and absorbing. Even if you are not coding or do not have nerdy or some kind crazy tendency, you will still appreciate understanding how the grey stuff between your ears makes you what you are and worth. You may truly even start training your brain to master other fields that you have not thought about before. The authors' attention may have not been to help you retrained your brain, but this would be a by-product of reading this.

    For those of us, who are striving to understand, decode and them emulate how our brains are so good in doing certain things, I think this book would help us to sit back and rethink about how we architect the software we develop, even if this is a small software that operates within the bully dark valleys - a.k.a black pools - that frightened John Lewis to write a book that painted an overweight chines nocturnal, writing a software in one night, with no unit, integration and acceptance testing that works well in the morning and beats the rest!

    The strange thing about this book is that as you keep reading it, you will simply and subtly learn how you behave, see this world, value your relationships and respect others would always depend on the quality of information fed into your Cortex from the day you were born to day. Hence, if we had one liberal school that every child in this world attends, perhaps, we would have lived in a fairer world, where we do not see abuse, unfairness and killings and so forth! While the authors do not mention, you would get to understand, during the end of the II World War, why PM Winston Churchill and his European counterparts believed in the art of Sphere of influence, while their North American counterparts abhorred this strange foreign policy.

    If you ever happened to watch the "Gifted hand', after you read this book, you would appreciate how an illiterate mother succeeded to get her son, Ben Carson, to become a renown neurosurgeon. Remember, when she asked her sons to go the library and read and read. And the did this and the young Ben becomes the best in his class. It was all about feeding his brain with information that made him more informative than his class mates. His Neocortex got the memory it needed to predict what his teachers expected from him. Every thing you look would make sense for you, once you have gone through this book. You would even further predict the what would have happened to young Ben, if his mother did not go to work for the professor with house of full of books!

    The authors also appear to have an unchallengeable knowledge of how a computers and programming languages work. They do understand how the SSDs has transformed the way we do use data, while they never mention the letters SSDs in their book and explain how we could make a memories that the applications we design can tap on demand without latency. They talk about the beauty of allowing machines to learn and then passing that knowledge from one machine to another, just like the way we use fast USB drivers to copy data from one place to another.

    They even go deep on explaining why it would be plausible that we do not build one humongous software that mimics the entire Cortex, but modules that can specialise on different functionalities. And, if the need arises, all of this can be brought together one day. Here it looks like they did not only tell you how the magic stuff works, but also how we can utilise the art of SOA so as to bring together different sensors, brain like software and even machines that can react to or commanded by this software.

    The authors view on the separation between the software and the mechanical parts is another design architect that can allow, for example, our intelligent devices to even share the same intelligent software hosted somewhere, where the art of SOA could be brought into play.

    Although the authors were hesitant to precisely predict when this Intelligent thing should happen, though they mentioned in 10 ten years this may start happening, I think unknowingly we are already in the era of Intelligent Software - here I am avoiding the word machines - as I do not want the fainthearted among us to think we are sleep walking into the SKYNET situation. Just think about the software that gives you a quick and accurate answer about the historical exchange rates by just calling simple Restful Web API, hosted somewhere in the world. The application does not retrieve any data from any HD. But it use a collection of objects that lives or resides in Memory. Although this is a tiny example, it is a microcosm of what is to come. Think about the current claims on Big Data and how this would aide the creation of Cortex memory that would one day do more than then crunching numbers. Think about the art of correlation instead of that of causation - the era of big data.

    I would urge every software architect, who had an interest in designing better applications, to read this book. This would help you think about the behaviour of your software from when the machine is turned on till it is switched off. This May also lead you to think about how much you could have achieved if you have used servers that never get switched off and argument it with Restful Web APIs as conduit for getting requests and returning what the client software wants; where this client software could be hosted on any lightweight devices.

    I would recommend to ever ordinary (non-nerdy/crazy) individual of us to read this book, as it would help you understand how the art of prediction works.

    However, I hope this book would not provide an excuse for those, who murder and abuse - from statesmen/women to ordinary individuals -to use this as an excuse by claiming that the horrendous acts they did was due to the corrupt memory they had in their Cortex!
  • sparerib
    5.0 out of 5 stars 科学ジャーナリストの著作ですが。。
    Reviewed in Japan on March 30, 2014
    これは、脳科学の研究を分かりやすく説明した著作の一つでしょう。
    邦訳されたのは、何とタイトルが『考える脳、考えるコンピューター』です。
    科学による知見をジャーナリズムが売らんかなの姿勢で食い潰す、そんな日本の
    酷い一面が浮き彫りになるかな~と思っています。

    http://www.amazon.co.jp/4270000600/

    邦訳本の中身は忘れましたが、原典の本書は"Aha transition"の原理が数か所で
    触れられており、一時ブームになった「アハ体験」はこういうものだったのか、と目が
    開かれるようです。

    ジャン・ピアジェの「認識論」と併せ、人間心理の真理に迫るライフワークの一環と
    して考えて行きたいですね。本著、それほど英語が難解ではありませんので、
    読解力向上と興味に任せた科学書の両様でお勧めします。

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