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on 3 October 2017
amazing book and skilled
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on 26 December 2009
This book explores the question "what is intelligence?", introducing rather surprising results of brain research in a way that should be understandable to a person of average intelligence.

Mr. Hawkins explains that intelligence is not what people usually think it is. For most of us (including me prior to reading this book), the word "intelligence" associates with "artificial intelligence" and that associates with computers that beat humans at chess, and begs the question: will computers smarter than us take over the Earth one day?
Of course, I have always noticed, as probably all serious chess players have, that computers play chess in a totally different way. In a nutshell, they tend to excel at short-term tactics while being ridiculously inept at endgame in which one needs a good grasp of long-term strategy. Humans often make the kinds of mistakes chess software would almost never make, and chess software often makes mistakes a human player would hardly ever make. I have, though, never been able to figure out what it is that makes the "artificial intelligence" so different from "human intelligence".
That's where this book steps in. It's not about chess, of course - I just brought that example to help you understand what the problem's all about. This book is about scientific research that convincingly refutes the popular belief that computer CPU's are "electronic brains", or that a human brain is just a very, very compact, very, very efficient computer.

I shall now try to explain briefly how this book describes the essential difference between brains and computers.
In an electronic circuitry, you have a certain module (or a network of modules) that turns a certain kind of input into a certain kind of output. For example, every time you press "escape" on your keyboard, a certain signal goes into the CPU that decides what to do about it. Every time there is a certain input, there will be the corresponding output.
Brain research reveals that a brain works in a different way. It isn't like each time you walk into your room, you think "there's the window, there's the desk, there's the chair etc". Your eyes would see all those things but you wouldn't be paying attention to them because they are as usual. But if someone removed the chair, you would enter the room and instantly notice THAT THE CHAIR ISN'T THERE. It's not like you think "there's the window, there's the desk etc", and you would have to somehow deduct that the chair isn't there. On the contrary, the chair's NOT BEING THERE would be the first thing you'd notice.
So it would appear that certain parts of the brain monitor certain aspects of the outside world, getting data from their incoming neurons, and fire their outgoing neurons ONLY when something in their input data was NOT AS EXPECTED. (In the book, you'll get a detailed description how this physically works with brain cells with various functions organised in various parts of the brain and various layers of brain tissue.) In other words, intelligence isn't calculating, it's predicting. Based on past experiences, the brain sets up expectations, and reacts when sensory input isn't as expected.

You'll learn other amazing things about the way the brain works differently from what most of us have imagined so far, but I don't want to go into too much detail here.

One thing in particular that fascinated me about this book was the way Mr. Hawkins illustrates highly abstract and technical concepts with simple examples. He says he does so because he likes simple things. So do I. Two thumbs up for that.

Mr. Hawkins's message is: the computers the way they are designed today can never become intelligent because the brain works in a totally different way. Mr. Hawkins insists that really intelligent machines can only be built by imitating the structure of the brain. (I would like to ask him if he thinks that such machines would obey our orders, but that's not the point right now.)

This is one hell of a remarkable book. In general, I am not too fond of neither philosophy nor abstract science. I prefer books that contain something that I can actually use to improve something in my life. That said, the abstract science in this book is so fascinating that I consider it one of the most valuable books I have ever read, even though I won't be able to put any of it to immediate practical use.
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TOP 500 REVIEWERon 15 September 2009
"Prediction is not just one of the things your brain does. It is the primary function of the neocortex, and the foundation of intelligence." (p. 89)

Perhaps the crux of Hawkins's insight into how our brains work and how that is different from how computers work can be gleaned from considering how to catch a ball in flight.

It used to be thought that such tasks were solved by the brain through calculation. The brain would calculate the flight of the ball, adjusting the muscles of the body appropriately so as to arrive at a spot where the ball would be and grab it. Artificial intelligence people working on robots used this method and found out that it was enormously complex, so much so that the robots remained clumsy (and not about to play centerfield for the New York Yankees).

What Hawkins is saying is that the brain does NOT calculate the flight of the ball but instead recalls from memory similar flights of balls while at the same time recalling again from memory the muscular workings of the body as it went after and caught or did not catch similar balls in flight. After a bit of practice (storing memories) a person can get very good at catching balls.

In other words the brain predicts where the ball is going to be not through a laborious and lengthy calculation but through memories of similar events. This is a startling insight. Hawkins shows how everything we do is based on our brain's ability to predict events based on previous experience. Here's how it works:

First there is a "sequence of patterns" of past events stored in the brain.

Second, the brain has an "auto-associative mechanism" that allows it to "recall complete patterns when given only partial or distorted inputs." (p. 73) Unlike computer intelligence, human intelligence can figure out that "Wass up?" means the same thing as "What's up?" or that a face seen from one angle is the same as that face seen from another angle or even seen in some sort of distortion. This is something computers cannot reliably do.

Third, the brain stores "invariant representations" of things seen, heard, felt, etc. "Invariant" in this context means unaffected by differences in light or tone or inflection or background or any one of millions of small, inessential differences that could throw us off. These representations are not exact. They are in a way like Plato's ideal forms except they are not ideal but generalized. They are memories of the relationships between and among various features. In the case of a human face, Hawkins writes that what makes a face recognizable "are its relative dimensions, relative colors, and relative proportions, not how it appeared one instant last Tuesday at lunch." (p. 81)

Hawkins's definition of intelligence in terms of predictive ability is what I found most exciting in the book. When people talk about intelligence I usually want to demand "intelligence for what?" since the criteria for defining intelligence has always been so muddied. One of the ways of establishing a theory in science is through its ability to make accurate predictions. To judge the brain the same way seems strikingly right. Not only that but no longer do we have to beg the question of what intelligence is. It is the ability to predict.

These predictions are about everything in our lives and they involve all of our senses. As Hawkins puts it, "All regions of your neocortex are simultaneously trying to predict what their next experience will be. Visual areas make predictions about edges, shapes, objects, locations, and motions. Auditory areas make predictions about tones, direction to source, and patterns of sound. Somatosensory areas make predictions about touch, texture, contour, and temperature." (pp. 88-89)

While the first five chapters are eminently readable and exciting, Chapter 6, "How the Cortex Works" (the longest in the book) might be a bit tedious and technical for the general reader. (I know it was for me.)

In Chapter 7, "Consciousness and Creativity" Hawkins writes, "Most of what you perceive is not coming through your senses; it is generated by your internal memory model." (p. 202) We do not experience the world directly and we do not interpret it objectively. Our predictions in a sense are prejudices or stereotypes that sometimes lead us astray. Hawkins writes, "...you could substitute the word 'stereotype' for 'invariant memory'...without substantially altering the meaning. Prediction by analogy is pretty much the same as judgment by stereotype." (p. 203)

In the final chapter, "The Future of Intelligence" Hawkins makes it clear that intelligent machines will not be taking over the world. He writes, "The computer in your home, or the Internet, has as much chance of spontaneously turning sentient as does a cash register." (p. 214) Furthermore, an intelligent machine "will not have a mind that is remotely humanlike unless we imbue it with humanlike emotional systems and humanlike experiences. That would be extremely difficult and, it seems to me, quite pointless." (p. 208). Finally, fears that machines will take over the world "rest on a false analogy...a conflation of intelligence...with the emotional drives of the old brain--things like fear, paranoia, and desire. But intelligent machines will not have these faculties. They will not have personal ambition. They will not desire wealth, social recognition, or sensual gratification. They will not have appetites, addictions, or mood disorders." (p. 216)

Hawkins goes on to predict that, with an approach based on learning and memory instead of brute calculation, we will build truly intelligent machines, the applications of which will be numerous and include applications impossible to predict.

I would like to point out that Hawkins' idea that our cortex is continually making predictions about the environment, predictions that we scarcely notice unless they are wrong, is similar to an idea that John McCrone presented in his book Going Inside: A Tour Round a Single Moment of Consciousness (2001), a book I also highly recommend.
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on 2 May 2014
I love this book and cannot say enough good about it.

It is, without a doubt, suitable for anyone who has an interest in artificial intelligence, from complete newcomers with no science background and no interest in maths or algorithms right up to established professors who feel stuck in a rut!

I have an MSc in Robotics and am undertaking my PhD in an AI related field. I have been very disillusioned with studies into AI that revolve around optimising algorithms for some specific task and entered into many arguments with academics who assert with absolute certainty that intelligence is, ultimately just a very complex algorithm.

This book argues about what intelligence is in a way that leaves the open-minded reader staggered and excited about the possibilities. Ultimately, it is all just guesswork and hypothesis but I for one shall be very disappointed if the author isn't uncomfortably close to the mark!
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on 10 December 2004
We often routinely talk about intelligence and we attempt to measure it for for a variety of purposes. But do we know what it is? Jeff Hawkins is one of the first people to present a specific and comprehesensive theory of intelligence with a leading role for the human neocortex. Hawkins starts by stating that Human intelliigence is fundamentally different from what a computer does.
But isn't artifical intelligence (AI) a good metaphor for human intelligence? No, says Hawkins. In AI a computer is taught to solve problems beloning to a specific domain based on a large set of data and rules. In comparison to human intelligence AI systems are very limited. They are only good for the one thing they were designed for. Teaching an AI based system to perform a task like catching a ball is hard because it would require vast amounts of data and complicated algorithms to capture the complex features of the environment. A human would have little difficulty in solving such everyday problems much easier and quicker.
Ok, but aren't neural networks then a good approximation of human intelligence? Although they are indeed an improvement to AI and have made possible some very practical tools they are still very different to human intelligence. Not only are human brains structurally much more complicated, there are clear functional differences too. For instance, in a neural network information flows only one direction while in the human brain there is a constant flow of information in two directions.
Well, isn't the brain then like a parallel computer in which billions of cells are concurrently computing? Is parallel computing what makes human so fast in solving complex problems like catching a ball? No, says the author. He explains that a human being can perform significant tasks within much less time than a second. Neurons are so slow that in that fraction of a second they can only traverse a chain of 100 neurons long. Computers can do nothing useful in so few steps. How can a human accomplish it?
All right, human intelligence is different from what our computers do. What then is it? I'll try to summarize Hawkin's theory.
The neocortex constantly receives sequences of patterns of information, which it stores by creating so-called invariant representations (memories independent of details). These representations allow you to handle variations in the world automatically. For instance, you can still recognize your friends face although she is wearing a new hairstyle.
All memories are stored in the synaptic connections between neurons. Although there is a vast amount of information stored in the neocortex only a few things are atively remembered at one time. This is so because a system, called `autoassociative memory' takes care that only the particular part of the memory is activated which is relevant to the current situation (the patterns that are currently flowing in the brain). On the basis of these activated memory patterns predictions are made -without us being aware of it- about what will happen next. The incoming patterns are compared to and combined with the patterns provided by memory result in your perception of a situation. So, what you perceive is not only based on what your eyes, ears, etc tell you. In fact, theses senses give you fuzzy and partial information. Only when combined with the activated patterns from your memory, you get a consistent perception.
The hierarchical structure of the neocortex plays an important role in perception and learning. Low regions in the structure of the neocortex make low-level predictions (about concreet information like color, time, tone, etc) about what they expect to encounter next, while higher-level regions make higher-level predictions (about more abstract things. Understanding something means that the neocortex' prediction fits with the new sensory input. Whenever neocortex patterns and sensory patterns conflict, there is confusion and your attention is drawn to this error. The error is then sent up to higher neocortex regions to check if the situation can be understood on a higher level. In other words: are there patterns to be found somewhere else in the neocortex, which do fit to the current sensory input?
Learning roughly takes place as follows. During repetitive learning memories of the world first form in higher regions of the cortex but as your learn they are reformed in lower parts of the cortical hierarchy. So, well-learned patterns are represented low in the cortex while new information is sent to higher parts. Slowly but surely the neocortex builds in itself a representation of the world it encounters. Hawkins: "The real world's nested structure is mirrored by the nested structure of your cortex."
This model explains well the efficiency and great speed of the human brain while dealing with complex tasks of a familiar kind. The downside is that we are not seeing and hearing precisely what is happening. When someone is talking we by definition don't fully listen to what he says. Instead, we constantly predict what he will say next and as long as there seems to be a fit between prediction and incoming sensory information our attention remains rather low. Only when he will say something, which is actively conflicting with our prediction, we will pay attention.
The author takes his model one step further by saying that even the motor system is prediction driven. In other words, the human neocortex directs behavior to satisfy its predictions. Hawkins says that doing something is literally the start of how we do it. Remembering, predicting, perceiving and doing are all very intertwined.
I think this is a fascinating and stimulating book. Many questions about intelligence may remain unanswered but I believe this book to be a step forward in our quest to understand intelligence. The author predicts we can soon build intelligence in computersystems by using the principles of the neocortex. He is optimistic about what will happen once we succeed in this. He (reasonably convincing) argues these systems will be useful for humanity and not a threat.
Coert Visser, [...]
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on 31 January 2013
This is an extremely important book in the field of Artificial Intelligence. The author reject this Artificial Intelligence because it identifies intelligence to the behaviors produced by this intelligence. Hence the machine simulates intelligent behavior but is not intelligent. Three things are essential goals to satisfy if we want to move towards intelligent machines. We have to take into account and integrate time. We have to include as architecturally essential the process of feedback. We have to take into account the physical architecture of the brain as a repetitive hierarchy. Strangely enough the main mistake is already present in this first programmatic intention. Jeff Hawkins does not include the productions of that intelligent brain. I mean language, all ideological representations or models of the world from religion to philosophy and science, not to speak of arts and culture. And strangely enough this mistake is locked up in an irreversible declaration:

"A human is much more than an intelligent machinre . . . The mind is the creation of the cells of the brain . . . Mind and brain are one and the same." (41-43)*

We cannot but agree with the first sentence, but the mind is not "created" by anything. It is produced, constructed by the brain from the sensorial impulses it gets from the various senses and the way it processes them in its repetitive and parallel hierarchical architecture. But the mind is a level of human intelligence of its own. Unluckily Hawkins will not see it. I have already said what it excludes from this human intelligence, but we must add the fact that this human intelligence lives in a situation that enabled this intelligence to develop and invent its first tools when Homo Sapiens started its journey on earth some 300,000 years ago. This situation requires from the weak animal that Homo Sapiens is to develop these tools to compensate for its weakness, and to coordinate its survival and development with communication and social organization which implied and required a culture, a model of the world to migrate, develop new productive means, and be able to develop as a species in order to expand all over the world: Homo Sapiens was a migrating species from the very start because of his very brain and the mind it could procude. Jeff Hawkins forgets about the phylogeny of Homo Sapiens. He takes intelligence as existing in itself without a genesis from nothing to what it is today. In other words he speaks of evolution but he does not study it and how this evolution brought this human species into developing intelligence, means of communication and means of production that did not exist before.

At the same time he does not consider the feelings and emotions of that human being and he at best locates them in the old brain, the brain inherited from the species before mammals since the cortex only developed with mammals. It is also obvious this is a mistake. Due to mirror neurons man is able (with some top mammals along with him, to develop empathy, the possibility to imitate (hence to learn through imitation and when language was invented to learn through repetition) and to share the feelings of others and one's own feelings with others. It is this ability more than the old brain that is at stake here and is neglected. That makes Hawkins neglect social aims, productive objectives, cultural targets, ideological psychological social motivations and of course social organization. To invent and develop intelligent machines would not even exist as a plan or a project or even a desire if Homo Sapiens had not been able to blaze and then run the track leading to development.

He is sure right on the fact that behavior is only the consequence of all this but by rejecting behavior because he rejects behaviorism (which is purely ideological on his part) he also locks himself out of the possible approach of human relations, human motivations towards others, hence concrete, material and also emotional and intellectual behaviors. And that prevents him from coming back to the situation that has to be controlled and set up collectively to reach collectively defined objectives. Globalization is right now the best example of how objectives have to be defined at the level of the planet and no longer at the level of particular countries or groups.

But apart from that the whole book is essential because Hawkins concentrates on the study of the brain and its hierarchical architecture, and I should say its double architectural structure, not double in nature but double in working.

The whole adventure starts with the senses and he straight away says there are a lot more than five senses even if we can consider there are only five basic sensorial organs: the eye, the ear, the tongue, the skin and the nose.

At the level of the eye we have to add motion, color, luminescence and spatial orientation. At the level of hearing we have to add pitch, length, intervals, timbre, spatial orientation and balance (vestibular system). At the level of touch we have to add pressure, temperature, pain, vibration but also spatial orientation and movement on the skin that will be useful both in torturing (along with pain) and eroticism or emotions (along with pleasure). At the level of smell we have to consider intensity, appeal (good, bad or somewhere in-between), spatial orientation. At the level of taste we have to add temperature, texture, appeal (good, bad or somewhere in-between), and even finer elements like sweet, salty, acid, alcoholic and many others. But, and he insists on that, the general senses of the body are essential too. The whole body is a network of sensors that checks and measures our joints and joint angles, all our bodily ,positions, and all proprioceptive receptors (sensory receptors, in muscles, tendons, joints, and the inner ear to detect motions or positions of the body or the limbs, that respond to stimuli arising within the body.) Note these are indispensible for walking, running, swimming and all movements, particularly coordinated movements like gymnastics and all kinds of martial arts And we should add the physiological sensors and mechanisms that measure our inner level of satisfaction, dissatisfaction, balance and unbalance of every single organ of ours. These last sensors are essential for a new born child since it is those he/she will use from the very start and that will prompt his first cry or call. And every single of these senses and sensors sends messages to the brain in temporally organized sequences. The eye reboots its vision three times per second, what is called a saccade.

The first hierarchy he takes is exemplified by vision. I will integrate the eye into it right away though the eye is more or less marginalized in Hawkins's approach. And here the eye sends many messages according to the particular abilities of the various retinal cells that capture the signal. I will insist on the fact that he neglects: the signals are sent from the retina and are spatially oriented right-side right and upside down. He neglects it because we do not have an "image" on the retina and it is not an "image" that the retina sends. But the spatial orientation of this "pattern" as he calls it is essential. The brain will have to interpret this orientation to reestablish the proper one thanks to the signals sent to the brain by the other senses and thanks to its experience starting right after birth. Experiments have been performed using glasses that inverted the orientation of the "pattern" on the retina and after a short while the brain corrected the initial correction and provided the mind with the proper spatial orientation.

In the neocortex, the capture of a visual stimulus is hierarchically organized and we must keep in mind that the signals are renewed three times a second. In the V1 area only many small segments and isolated characteristics like colors are deciphered. These numerous small elements are sent to the V2 area where they are regrouped into larger elements. Then they are sent to the V4 area where they are regrouped into recognizable elements like a nose, an eye, etc Then they are sent to the IT area where they are reconstructed into a face for example. Here Hawkins defines a pattern as being "a stable cell assembly that represents some abstract pattern" (p. 80). At each level after learning, hence after first stimulation by one unknown element (which is sent unanalyzed to the hippocampus that takes over, identifies it and sends it back into the system), an invariant representation of each identified pattern is memorized (cortical memory, p. 100) in the cells (he does not specify the electrical and chemical procedure nor the molecular level of it). The cortical procedure then, after learning, is a recognition procedure: the pattern received corresponds to one invariant representation previously memorized, otherwise it is sent up as far as the hippocampus if necessary. The last element we have to understand is that the identification is not done in detail but as corresponding to an invariant sketch of the element and that sketch accepts variations. That explains why we can recognize someone and yet be mistaken. The mind did not make a mistake it used some elements that corresponded to the sketch it had in memory, and that was the wrong sketch.

The three basic characteristic of this hierarchical functioning are:
1- its sequential memory (sequences of patterns hence spatial in the pattern and temporal because serialized);
2- its autoassociative nature (it memorizes a sketch and not the real detailed pattern when learning, though this detailed pattern is also memorized which enables us to realize we made a mistake when we took someone for someone else, and then it recognizes this sketch in the real pattern it receives after learning);
3- and finally its "invariant representation" dimension which is the identification of these sketches as referents for further use. Here instead of saying that these sketches have to be "named" he should have said that they have to be identified at each level with some kind of Cortical Identity (CI) and this when connected with the invention of language by Homo Sapiens, or the learning of language by children would have led him to the word "concept" that he uses rarely, and the operation of "conceptualization" that he does not use at all. Homo Sapiens seems to be the only animal who managed this conceptualization power of the neo-cortex (dominated by the hippocampus) into producing language.

We come then to the heart of the volume:

"The three properties of cortical memory . . . (storing sequences, auto-associative recall, and invariant representations) are necessary ingredients to predict the future based on memories of the past . . . Prediction . . . is the primary function of the neocortex, and the foundation of intelligence . . . Evolution discovers that if it tacks on a memory system (the neocortex) to the sensory path of the primitive brain, the animal gains an ability to predict the future . . . This new idea of the memory-prediction framework of the brain . . . " (p. 84-105)

We can notice there is an intellectual drift in his reasoning. Evolution does not have a mind or intelligence. Just as we can prove human articulated language is the result of the conceptualizing power of the brain on one hand, and of other physical mutations dictated by the long distance bipedal nature of Homo Sapiens (not the first hominid to have that characteristic but the first to be endowed with mutations that go a lot farther than before) that are absolutely necessary for survival on the other hand (low larynx, high level of innervation of the laryngeal-glottal-buccal masticatory and articulatory apparatus, high level of coordination of various organs and functions), we have to consider evolution as being a blind and unguided process that selects haphazard mutations when they are propitious to bringing a higher survival potential to a given species. It is quite obvious that the development of the neocortex of mammals into human neocortex provided Homo Sapiens with a higher survival potential. In other words Hawkins suffers of some teleological bias which is a way to escape from asking who did it and hence a way to exclude the possible religious answer. But that is wrong. We don't have to answer the question of where does the logic of evolution comes from because we cannot answer this question with any scientific final elaboration.

Then Hawkins moves to the second hierarchy, that of the neo-cortex structures. The neocortex is divided into columns that are perpendicular to the surface of it. It has six layers. The first layer has few cells that have myriads of small dendrites connected to their neighbors by synapses that can build and rebuild themselves. Then they have three axons, two horizontal and lateral in the first layer connecting this cell to distant other cells all over the brain on one side and on the other side, the famous spindle cells, and a third one going down into lower layers of the neocortex. When layers 1, 2, 3 are activated the activating pattern goes to layer 5 and then layer six. In layers 1, 2, and 3 the pattern is analyzed to be finally identified in layer 5. Then it is moved to layer 6 where a prediction might be performed about what may come next from this identified pattern. Then the transmission branches into part of it being sent to the Thalamus and then back to layer 1 as a feed back and part of it being send simultaneously to motor areas for processing. Layer 4 is the layer where a newly learned pattern, identified by the Hippocampus arrives to activate the column, that is to say layers 5 and 6 and beyond. This can be summarized in a triple hierarchy: the mind must first discriminate an element, then identify and eventually name that discriminated element, and finally classify ort conceptualize this identified and named element. This basic conceptualization that has to be constructed in a child through education, just the way it was constructed in Homo Sapiens through experience.

It is important then to cross this approach with a phylogenic and psychogenetic approach of language to understand how language was invented and how it is learned. That of course would require a lot of space and it is not here it can be presented. But let's say that three hierarchies can be seen in language and all of them can only be understood as the crossing of the neocortical capabilities of Homo Sapiens on one side, and the highly frail state of Homo Sapiens or the highly dependent state of a human newborn on the other side. These hierarchies are that of the word: consonantal roots, isolating characters or themes, and conjugation-declension fronds giving the three (maybe four) vast phylogenic families of languages: consonantal Semitic languages, isolating Chinese, Tibeto-Burman and Khmero-Vietic languages, and agglutinative (the vast Turkic family from Turkish to Siouan) or synthetic-analytic languages (Indo-European and Indo Aryan languages).

The triple syntax of any language: Categorial syntax (discriminating nouns and verbs, spatial units and temporal units), Functional syntax (building the sentence on the pattern [AGENT (feed-ER) - RELATION (feed) - PATIENT (feed-EE) - THEME (feed-Ø, food, fodder)] and finally Expressive syntax (expressing the mood and modalizations imposed onto the utterance by the speaker and his relations to his environment. These three syntactic functions are mapped onto the first hierarchy by making it all discursive in root-languages, making the last two discursive in theme-languages and only keeping the expressive level for discursive means in frond-languages. Note each one of these three syntaxes is a hierarchy too by themselves.

Taking language into account would have enabled Hawkins to understand that he cannot consider the mind is the brain. The mind is an abstract and absolutely virtual construct of the brain from the various patterns the brain has registered in its own cells and molecules. I insist here on molecules because Microtubule Associated Proteins have been proved as having a role to play in various mental operation, particular with the loss of ,their phosphorylation when activated by some stimulus, for one example. The mind is based on the hierarchical potential and architecture of the brain and this potential and architecture produce the conceptualizing potential that will produce the virtual mind and its tools. These tools are essential if we want to understand the emergence of Homo Sapiens as the superior intelligent mammal on earth and if we want to understand today's man and human society. The first of these tools is (spoken) language (note written language was invented only around 5 or 6 thousand years ago some 300,000 years after the invention of spoken language). Then Homo Sapiens invented all "ideological" tools to understand and explain the world in order to survive and expand in a state of great physical inferiority as compared to most of his predators. These tools are religion, astronomy, science, history, all constructed models of the world produced or that could be produced with the conceptualizing power of the human brain. Note here Neanderthals could not even invent fishing whereas Homo Sapiens just started with fishing to move onto agriculture, herd-husbandry, and so, and all that before inventing written language.

So I do not believe "the mind is just a label of what the brain does." (p. 204) and the mind the way I have sketched it is something that might be one day equaled by machines. But these minded machines will not be human since they will not be able to learn and develop their brain and mind the way man does it, from scratch and as the result of an intense and highly emotional intercourse between an individual and his/her linguistic, cultural, social and emotional environment. We are not speaking of a machine loving a man, but of a machine loving a machine not as something programmed but as something learned from experience. As a matter of fact the Terminator saga is a lot more instructive on that point than what Hawkins says. In the same way the intelligent machines are not the machines themselves but all the Mr. Smith taking over the earth by decision of the Architect who manipulates machines into attacking humans till one, two or three humans are able to negotiate the end of the war with machines who accepts on the basis of Neo being crucified in order to be able to defeat all the Mr. Smith and the Architect's matrix. Once again we are far away from what Hawkins says.

To conclude, Hawkins's book is the first important step against the apocalyptic messianic prophetic prediction the engineers turned theoreticians like Ray Kurzweil who are already taking all the necessary pills to be able to live long and merge with intelligent machines in less than fifty years, and thus become the nurtured cows of these intelligent machines, who would not be intelligent enough to understand that kind of slavery would be doomed to destruction just like any other form of slavery was and has been doomed to destruction. If these machines were humanly intelligent they would understand that as a basic requirement to qualify for intelligence.

But at the same time Hawkins does not reach the level of the mind. He locks himself in the physiological and biological brain pretending it is the mind mixing up the capacity and the potential. He thinks too much with metaphors and comparisons. To use one I would say that a plane CAN fly but that this plane is not the FLYING POTENTIAL itself. The plane has that potential but to realize it a whole procedure is necessary (with kerosene, air strips, engineers in the air traffic control tower, pilots, passengers, freight, stewards and stewardesses, etc) and flying can only become a reality when that procedure has been performed. Hence the FLYING POTENTIAL is a VIRTUAL capability of the plane, just like the MIND is a virtual construct of the brain using its POTENTIAL INTELLIGENCE, and this POTENTIAL INTELLIGENCE cannot produce any INTELLIGENT ACTION if the VIRTUAL MIND is not activated and used by the brain.

The first intelligent machine invented by man was language in order to satisfy the need for communication Homo Sapiens had. That language has had a long career in improving and developing man's lot. It has also transformed its inventor and his/her society.

There still is a long way to go to even approach such humanly intelligent machines. In the meantime we will invent and use more and more intelligent machines that will liberate our brain and body of innumerable tasks that would otherwise use our mental and physical time and energy. With this mental and physical time and energy we will develop new forms of intelligence that we cannot even imagine today, and we must not forget that evolution goes on and man is a natural species. The more contact he/she will have with intelligent machines the more chances there will be he/she will go through mutations and developments that will be retained by evolution and education as vastly increasing human intelligence. The more intelligent machines, the more chances man will become more intelligent.

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on 10 August 2008
Jeff Hawkins is the founder of Palm Computer, and the inventor of the Palm Pilot and Treo. After making his fortune, Hawkins turned his attention to neuroscience. Given that history, I was afraid that this book was only published because Hawkins is rich, successful and presumed smart. In fact, Hawkins is smart. More importantly, he has some very good ideas about how the brain works, and he presents them in a clear and concise way. This is an excellent book.

Hawkins presents a theory of how the brain makes predictions. Questions that are easily solved are solved at a lower level. If they cannot be solved, they move up to the next level -- something like. I'll let Hawkins explain it. He does a much better job.

"On Intelligence" could easily have been titled "How the Mind Works." In fact, that title is taken by another wonderful scientist and writer, Steven Pinker. The two books have very little in common after that. I highly recommend both.
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on 11 June 2007
Mr Hawkins has given a lot of thought to the question of building truly intelligent computers and it shows. Chapter 6 is a detailed account of his findings, where he identifies the fundamental issues involved. The other chapters are much easier to read. Had Mr Hawkins provided a more comprehensive list of references, it would have been better for the readers who plan to read further. Even so, since most of the fundamental issues at hand appear to have been discussed, this book is an eye-opener. It should be of interest to computer scientists and neurologists alike.
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on 8 September 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!
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on 22 March 2014
This is a wonderful book. A way of looking at the brain not as a computer at all but as a memory system. Unlike other books which emphasise the Swiss arm knife view of the brain (lots of different bits doing their stuff) the author argues that the whole neocortex basically works by running the SAME algorithm. One that uses stored invariant memories to make predictions. That is, unlike a computer the brain solves problems not by computing the solution step by step but by recalling stored memories of the experience. The author gives the example of catching a ball. Whereas a robot will have to try to calculate moment by moment the trajectory (which is so complicated not robot can at the moment - which should be a good clue that this is probably not what the brain is even attempting to do, since neurons are orders of magnitude slower than even a laptop), the brain simply recalls stored patterns of muscle commands activated by the sight of the ball. The author argues that these memories are "invariant" in form. That is, as the details of the experience get passed up the neural hierarchy (he explains this in clear and fascinating detail) the details are slowly left behind and a general sort of "overall" memory is left (my words, not his - he is far clearer!). We don't store all the details of our experiences, rather the relationships between the stimuli. Another example of his is our memory of music. We remember a pitch invariant version of a song. By went confronted by actual input, a note of the song or the sight of the ball, we predict what is going to happen next by using the stored relational template. The cortex runs an algorithm which constantly uses these stored invariant memories to identify patterns in our input and make suitable predictions. It is a simple theory or model. I loved the book because it is the first book I have ever read about the brain which has given a plausible theory of what the brain is actually doing. As the author says again and again, it is a first draft of a theory and may turn out to be wrong but I can't help feeling that the answer, whatever it is, will look something. Like this.
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