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Top positive review
7 people found this helpful
5.0 out of 5 starsAn hypothesis that rings true . . .
ByDave Kinnearon January 12, 2014
In this very well written book, Hawkins and Blakeslee describe a new model of how our human intelligence has evolved, how it "works" and what it means to have a “massive” cerebral cortex. Much of the description of the brain's neuronal structure will be familiar to those who follow developments in neuroscience. However, what's new here is a working model of how the brain uses extensive feedback loops to complete the complex task of information processing.
The authors assert that, "The brain uses the same process to see as to hear. The cortex does something universal that can be applied to any type of sensory or motor system." And, "The idea that patterns from different senses are equivalent inside your brain is quite surprising, and although well understood, it still isn’t widely appreciated." Further, the way the brain processes information is consistently applied to all that sensory data. This common processing algorithm and sensory input processing helps our brains to adapt to an ever changing environment. That is why we can live and function in this modern world. A world in which change, and our need to adapt, has certainly outstripped evolutionary time scales.
The hypothesis put forward in this book rings true to me based on my understanding of complex systems and from observing the actions of my fellow human beings. This model (new to me but not necessarily new to the neuroscience world) doesn't negate my understanding from other reading how the human brain is "wired." Rather, it explains more fully how the system "hangs together" and accomplishes the incredible feats we witness every day. It also lays the foundation for a better understanding of human consciousness.
Once again the fact that we can understand our material world only in a "second hand" manner is driven home by this model. We work only with a representation of the world, and it is represented by a limited number of sensory inputs. From the standpoint of how we deal with our fellow human beings, this challenging and interesting book makes it clear that we should all be a lot less
certain that what we "know to be true" is actually a true representation of reality.
In the authors' words: "Finally, the idea that patterns are the fundamental currency of intelligence leads to some interesting philosophical questions. When I sit in a room with my friends, how do I know they are there or even if they are real? My brain receives a set of patterns that are consistent with patterns I have experienced in the past. These patterns correspond to people I know, their faces, their voices, how they usually behave, and all kinds of facts about them. I have learned to expect these patterns to occur together in predictable ways. But when you come down to it, it’s all just a model. All our knowledge of the world is a model based on patterns. Are we certain the world is real? It’s fun and odd to think about. Several science-fiction books and movies explore this theme. This is not to say that the people or objects aren’t really there. They are really there. But our certainty of the world’s existence is based on the consistency of patterns and how we interpret them. There is no such thing as direct perception. We don’t have a “people” sensor. Remember, the brain is in a dark quiet box with no knowledge of anything other than the time-flowing patterns on its input fibers. . . Your perception of the world is created from these patterns, nothing else. Existence may be objective, but the spatial-temporal patterns flowing into the axon bundles in our brains are all we have to go on."
What does all this mean to our daily lives? To me it simply means that there are solid reasons to make sure we always question our assumptions, work to find as much objective empirical data as possible and allow for other people to have a different view of the patterns they discern. Our individual perspective is all we have, but it isn't necessarily the only one nor is it necessarily the most accurate representation.
The authors assert that, "The brain uses the same process to see as to hear. The cortex does something universal that can be applied to any type of sensory or motor system." And, "The idea that patterns from different senses are equivalent inside your brain is quite surprising, and although well understood, it still isn’t widely appreciated." Further, the way the brain processes information is consistently applied to all that sensory data. This common processing algorithm and sensory input processing helps our brains to adapt to an ever changing environment. That is why we can live and function in this modern world. A world in which change, and our need to adapt, has certainly outstripped evolutionary time scales.
The hypothesis put forward in this book rings true to me based on my understanding of complex systems and from observing the actions of my fellow human beings. This model (new to me but not necessarily new to the neuroscience world) doesn't negate my understanding from other reading how the human brain is "wired." Rather, it explains more fully how the system "hangs together" and accomplishes the incredible feats we witness every day. It also lays the foundation for a better understanding of human consciousness.
Once again the fact that we can understand our material world only in a "second hand" manner is driven home by this model. We work only with a representation of the world, and it is represented by a limited number of sensory inputs. From the standpoint of how we deal with our fellow human beings, this challenging and interesting book makes it clear that we should all be a lot less
certain that what we "know to be true" is actually a true representation of reality.
In the authors' words: "Finally, the idea that patterns are the fundamental currency of intelligence leads to some interesting philosophical questions. When I sit in a room with my friends, how do I know they are there or even if they are real? My brain receives a set of patterns that are consistent with patterns I have experienced in the past. These patterns correspond to people I know, their faces, their voices, how they usually behave, and all kinds of facts about them. I have learned to expect these patterns to occur together in predictable ways. But when you come down to it, it’s all just a model. All our knowledge of the world is a model based on patterns. Are we certain the world is real? It’s fun and odd to think about. Several science-fiction books and movies explore this theme. This is not to say that the people or objects aren’t really there. They are really there. But our certainty of the world’s existence is based on the consistency of patterns and how we interpret them. There is no such thing as direct perception. We don’t have a “people” sensor. Remember, the brain is in a dark quiet box with no knowledge of anything other than the time-flowing patterns on its input fibers. . . Your perception of the world is created from these patterns, nothing else. Existence may be objective, but the spatial-temporal patterns flowing into the axon bundles in our brains are all we have to go on."
What does all this mean to our daily lives? To me it simply means that there are solid reasons to make sure we always question our assumptions, work to find as much objective empirical data as possible and allow for other people to have a different view of the patterns they discern. Our individual perspective is all we have, but it isn't necessarily the only one nor is it necessarily the most accurate representation.
Top critical review
2 people found this helpful
3.0 out of 5 starsWhile it is an easy read and entertaining
Bygladwigon September 7, 2016
Kind of regret buying this one. While it is an easy read and entertaining, it is very out of date. In general, the graphics in the book are limited. There are many sections that would be better presented with a clear diagram.
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Bygladwigon September 7, 2016
Kind of regret buying this one. While it is an easy read and entertaining, it is very out of date. In general, the graphics in the book are limited. There are many sections that would be better presented with a clear diagram.
2 people found this helpful
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ByPaul R. Adamson November 23, 2006
This is an important book, though perhaps not for the reason the author intends. It's important because it forcefully advocates a view that most neuroscientists have, for various reasons, shamefully neglected: the neocortex is built to understand the world. It has rather distinctive and puzzling circuitry and physiology which is repeated in all areas and all mammals, and which somehow equip it for the general task of learning about an animal's world. Furthermore, the central task of neuroscience is to explain how this basic machinery works, by combining insight from experiment and theory.
Most neuroscientists think that different cortical areas, since they are clearly solving completely different problems (vision, audition, movement etc), which require completely different solutions, must operate quite differently: there would be no "canonical" cortical operation. Confronted with the overwhelming evidence for a standard circuit, they tend to dismiss it as an insignificant evolutionary vestige, like the navel, or else to shift the conversation to the undoubted variability of the wiring (the "fluff"). This approach is reminiscent of the way that biologists operated before Darwin.
A sizeable minority of neuroscientists does like the idea of a "canonical circuit" but none can agree what it is. The mistake they make is in trying to decide what is "canonical" without focussing on what is "distinctive". Many features of the neocortex (eg recurrent pathways) are also found in other brain areas (especially hippocampus and olfactory cortex). Adding these to the canonical recipe enormously complicates, and confuses, the task, and tends to hide the important things.
In a nutshell the distinctive features are: 6 layers, thalamic input (with burst/tonic transitions), slow/REM sleep, subplate waiting, inside-out development, and, especially, layer 6 connections. Hawkins touches on some of these things, but doesn't really seem at home in the cortical basement and attic.
The weakness of the book is in the more detailed speculations. The ideas are not rubbish, but they are (somewhat inevitably) not sufficiently clearly described. The biggest problem is that they are not linked to previous work, so that the reader has to struggle to understand the new stuff. The point here is not that Hawkins fails to be courteous or "academic". The point of extensively citing and explaining old ideas and findings is that this is the only way to explain new concepts. Hawkins is not trained in these areas, but he's smart and can travel lightly, without much academic baggage. But necessarily the ideas he is explaining are rather tricky, and the best way is to rely on shared knowledge. I suspect that if he made greater efforts to place his ideas in the context of previous work, he could also greatly improve his model.
Most neuroscientists think that different cortical areas, since they are clearly solving completely different problems (vision, audition, movement etc), which require completely different solutions, must operate quite differently: there would be no "canonical" cortical operation. Confronted with the overwhelming evidence for a standard circuit, they tend to dismiss it as an insignificant evolutionary vestige, like the navel, or else to shift the conversation to the undoubted variability of the wiring (the "fluff"). This approach is reminiscent of the way that biologists operated before Darwin.
A sizeable minority of neuroscientists does like the idea of a "canonical circuit" but none can agree what it is. The mistake they make is in trying to decide what is "canonical" without focussing on what is "distinctive". Many features of the neocortex (eg recurrent pathways) are also found in other brain areas (especially hippocampus and olfactory cortex). Adding these to the canonical recipe enormously complicates, and confuses, the task, and tends to hide the important things.
In a nutshell the distinctive features are: 6 layers, thalamic input (with burst/tonic transitions), slow/REM sleep, subplate waiting, inside-out development, and, especially, layer 6 connections. Hawkins touches on some of these things, but doesn't really seem at home in the cortical basement and attic.
The weakness of the book is in the more detailed speculations. The ideas are not rubbish, but they are (somewhat inevitably) not sufficiently clearly described. The biggest problem is that they are not linked to previous work, so that the reader has to struggle to understand the new stuff. The point here is not that Hawkins fails to be courteous or "academic". The point of extensively citing and explaining old ideas and findings is that this is the only way to explain new concepts. Hawkins is not trained in these areas, but he's smart and can travel lightly, without much academic baggage. But necessarily the ideas he is explaining are rather tricky, and the best way is to rely on shared knowledge. I suspect that if he made greater efforts to place his ideas in the context of previous work, he could also greatly improve his model.
ByJohn F. Connorson August 27, 2013
This work is informative and presents a compelling case for looking at the mind and its workings as a component of the brain and in the purely pathological realm. I was bothered by the author's presumptuous tone as he posited his foundations and never allowed for the possibility that he was incorrect. For example: In comparing the natural/organic to a manufactured devise the author stated (not opined) that an aircraft was far superior in performance to the "flapping wings" of a creature. This is not true at all; while a jet plane or piper cub may be able to exceed a duck in measureable speed, it requires much time and space to become airborne. A duck can displace enough ambient air in one or two "flaps" to create the lift necessary to begin flight - not even a helicopter can do that. There are other areas wherein this pompous tone is taken and, unless the reader is careful, he or she may easily be led to the author's desired conclusion which may, or may not, be accurate.
On intelligence is a good book and an engineer's discussion of how the brain works, processes information and experiences the world. For people wanting to understand how "wet ware" works then this is on the reading list.
The first two chapters are a waste of paper as they discuss Jeff Hawkins personal interest in the subject area -- so skip them. The remaining chapters are a good discussion of the physical properties and processes of the brain. Unfortunately these are presented as forgone conclusions and the final word in brain science, something that Hawkins admits is still really incomplete. Also Hawkins presents the material as if he invented it all, something that detracts from the power of the message.
The discussion is repetitive in places and surprisingly conservative in its outlook -- for example only humans have language, only humans are intelligent. That was a surprise that as the book seems to be fairly open on other issues.
The notion that the neocortex can basically learn anything and has few preconceived notions or hard wiring will provide ample ammunition for behavioralists and those who believe that behavior is learned and not part of nature.
In summary, I found myself skimming much of the discussion on particular ways things work as I can always go back and read it again. This makes for a good book, one that I am glad that I have read, but one that I would not recommend going out of my way to read.
The first two chapters are a waste of paper as they discuss Jeff Hawkins personal interest in the subject area -- so skip them. The remaining chapters are a good discussion of the physical properties and processes of the brain. Unfortunately these are presented as forgone conclusions and the final word in brain science, something that Hawkins admits is still really incomplete. Also Hawkins presents the material as if he invented it all, something that detracts from the power of the message.
The discussion is repetitive in places and surprisingly conservative in its outlook -- for example only humans have language, only humans are intelligent. That was a surprise that as the book seems to be fairly open on other issues.
The notion that the neocortex can basically learn anything and has few preconceived notions or hard wiring will provide ample ammunition for behavioralists and those who believe that behavior is learned and not part of nature.
In summary, I found myself skimming much of the discussion on particular ways things work as I can always go back and read it again. This makes for a good book, one that I am glad that I have read, but one that I would not recommend going out of my way to read.
In the field of artificial intelligence it seems there are as many definitions of intelligence as there are stars in the heavens. Each of these definitions seems plausible, and interestingly, they seem to get more difficult to satisfy with time. Thus progress in artificial intelligence seems to be non-existent, since the criteria used to designate a machine as being intelligent ten years ago are no longer used today. Researchers in AI used to believe for example that if a machine could beat a human in chess then it should definitely be deemed intelligent. That belief is hardly held by anyone in the AI community at the present time.
The author of this book proposes yet another definition of intelligence, and it is one that is inspired by his understanding of how the human brain functions. His justifications are interesting, but they are highly speculative, and border on mere philosophical musings. It would have been a better book if the author refrained from the random walks in conceptual space that are characteristic of philosophy, and justified his conception of intelligence with what is really currently known in neuroscience. He does quote the research of neuroscientists that have produced a detailed map of the monkey cortex, which revealed many different regions connected together in a complex hierarchy. The author then makes the assumption that the human cortex hierarchy has a similar hierarchy. This is not really an unreasonable assumption if viewed from the standpoint of neuroanatomy, but from the standpoint of the cognitive abilities of humans versus those of monkeys, it might indeed be an assumption that deserves intense scrutiny.
The author definitely wants to view intelligence as being one that can function over many different domains, i.e. an intelligent machine will be able to not only play chess for example, but could also analyze stock market data or perform some other function typically thought of as requiring careful thought. He expresses this by saying that the human cortex is "universal" in that it can be applied to any type of sensory or motor system, and that the "algorithm of the cortex" can be expressed independently of any particular function or sense. Certainly humans can think in many different domains, but one cannot conclude from this that humans possess the general intelligence that the author believes they do. There is in fact a large body of research that indicates that the human brain has a modular structure (the author discusses this research very briefly), with each module being responsible for functioning in a particular domain. If one of these modules ceases to function, this has no effect on the functioning of the others. This is a view of the brain as having a domain-specific structure. A domain-general notion of intelligence would mean that the brain can deal with several different domains, but that the same reasoning patterns or processes are used to think in these different domains. If one of these reasoning patterns or processes becomes non-functional, the rest of them will suffer. One could still view the brain as consisting of modules expert in different domains, but that these modules are "entangled" with each other in the sense just specified, i.e. damage in one module will affect the others.
In fairness to the author, there is also research in neuroscience that lends support to his notion of general intelligence and a single algorithm that can deal with all of the data presented to the human brain. He gives a few references that discuss this research, and he definitely emphasizes the need for feedback and the related notion of `auto-associative' memories. The brain in his view is a "pattern machine" and if one is to construct truly intelligent machines one must make use of this pattern manipulating capability of the human cortex. Thus intelligent machines will be a result of this "neocortical inspired" computing, and the author spends a lot of time explaining why these machines will mimic the ability of the brain to solve a problem using memory, and not by computing a solution. The cortex, in his view, creates "invariant representations" which can handle the intricate variability of the world it is confronted with. He summarizes this viewpoint by saying that the neocortex stores sequences of patterns, recalls patterns auto-associatively, stores invariant patterns, and stores patterns in a hierarchy. His explanations of how it does this are interesting, but again are very speculative, and in the absence of a prototype for a machine that possesses this kind of intelligence, it is difficult to assess the validity of his assertions.
This reviewer strongly disagrees with the assertion from the author that there are no machines today that express true intelligence. A strong case can be made for the existence of myriads of intelligent machines in the world today, but this case would again be dependent on a particular definition of intelligence. Machines that have intelligence as the author defines it are nowhere in sight, and this is no doubt due to the lack of commercial value in the domain-general intelligence that the author advocates. The intelligent machines of today can learn, adapt, and manage, and do many other different things, but they only do these things in specific domains. There is absolutely no need for these machines to have expertise in more than one domain, both for the sake of efficiency and also because of economics. In managing a network for example, there is no need for a machine to have expertise in some other area, such as chess playing or backgammon. Business demands thus dictate the kind of domain-specific intelligence that is so prevalent in hundreds of intelligent machines performing many useful functions in business and industry.
The author of this book proposes yet another definition of intelligence, and it is one that is inspired by his understanding of how the human brain functions. His justifications are interesting, but they are highly speculative, and border on mere philosophical musings. It would have been a better book if the author refrained from the random walks in conceptual space that are characteristic of philosophy, and justified his conception of intelligence with what is really currently known in neuroscience. He does quote the research of neuroscientists that have produced a detailed map of the monkey cortex, which revealed many different regions connected together in a complex hierarchy. The author then makes the assumption that the human cortex hierarchy has a similar hierarchy. This is not really an unreasonable assumption if viewed from the standpoint of neuroanatomy, but from the standpoint of the cognitive abilities of humans versus those of monkeys, it might indeed be an assumption that deserves intense scrutiny.
The author definitely wants to view intelligence as being one that can function over many different domains, i.e. an intelligent machine will be able to not only play chess for example, but could also analyze stock market data or perform some other function typically thought of as requiring careful thought. He expresses this by saying that the human cortex is "universal" in that it can be applied to any type of sensory or motor system, and that the "algorithm of the cortex" can be expressed independently of any particular function or sense. Certainly humans can think in many different domains, but one cannot conclude from this that humans possess the general intelligence that the author believes they do. There is in fact a large body of research that indicates that the human brain has a modular structure (the author discusses this research very briefly), with each module being responsible for functioning in a particular domain. If one of these modules ceases to function, this has no effect on the functioning of the others. This is a view of the brain as having a domain-specific structure. A domain-general notion of intelligence would mean that the brain can deal with several different domains, but that the same reasoning patterns or processes are used to think in these different domains. If one of these reasoning patterns or processes becomes non-functional, the rest of them will suffer. One could still view the brain as consisting of modules expert in different domains, but that these modules are "entangled" with each other in the sense just specified, i.e. damage in one module will affect the others.
In fairness to the author, there is also research in neuroscience that lends support to his notion of general intelligence and a single algorithm that can deal with all of the data presented to the human brain. He gives a few references that discuss this research, and he definitely emphasizes the need for feedback and the related notion of `auto-associative' memories. The brain in his view is a "pattern machine" and if one is to construct truly intelligent machines one must make use of this pattern manipulating capability of the human cortex. Thus intelligent machines will be a result of this "neocortical inspired" computing, and the author spends a lot of time explaining why these machines will mimic the ability of the brain to solve a problem using memory, and not by computing a solution. The cortex, in his view, creates "invariant representations" which can handle the intricate variability of the world it is confronted with. He summarizes this viewpoint by saying that the neocortex stores sequences of patterns, recalls patterns auto-associatively, stores invariant patterns, and stores patterns in a hierarchy. His explanations of how it does this are interesting, but again are very speculative, and in the absence of a prototype for a machine that possesses this kind of intelligence, it is difficult to assess the validity of his assertions.
This reviewer strongly disagrees with the assertion from the author that there are no machines today that express true intelligence. A strong case can be made for the existence of myriads of intelligent machines in the world today, but this case would again be dependent on a particular definition of intelligence. Machines that have intelligence as the author defines it are nowhere in sight, and this is no doubt due to the lack of commercial value in the domain-general intelligence that the author advocates. The intelligent machines of today can learn, adapt, and manage, and do many other different things, but they only do these things in specific domains. There is absolutely no need for these machines to have expertise in more than one domain, both for the sake of efficiency and also because of economics. In managing a network for example, there is no need for a machine to have expertise in some other area, such as chess playing or backgammon. Business demands thus dictate the kind of domain-specific intelligence that is so prevalent in hundreds of intelligent machines performing many useful functions in business and industry.
3.0 out of 5 starsYou don't need to be an expert to propose new theories of how the human mind works.
ByAAGon December 8, 2013
The thoughts of someone who made it big, formulating and sharing answers to some of life's biggest questions and proposing alternatives to efforts to develop thinking machines.
ByPlatoon May 15, 2013
A very good book for psychologists, neurologists, educators and philosophers but probably of little interest to most others. It is very informative but technical in nature and requires some backgound and interest in the nature of intelligence.
ByRaghu K. Gantion June 3, 2015
Some key points that were nice, but 40-50% of the book is a bit of a rambling!
ByRon January 24, 2016
This book was released in 2005. Still there are only close to 250 ratings on this book. The ratings, if not excellent, are good but before deciding to read the book I was wondering why this book may not have gained enough momentum in ten years? It may be that people didn't read it at all, or those who did didn't review it because they couldn't really express what this book was about.
Readers may not have been able to understand the book because almost half of it talks about theory of brain system. As you go through that part, it feels like you are sitting in an algorithms class where the instructor tried to simplify the concepts but in the effort to do so, made it completely mundane and tiresome. The author hints on the fact that this part can be skipped but if you skip it, you will lose the continuity of what the book is trying to talk about, which bring us to the question - what is the book all about?
Well, the author starts with a conjecture that the book will be about why current state of Artificial Intelligence may not be able to design intelligent machines. For some time, the author talks about himself and then tries to explain the basic details of Neural Networks. If you already know about Neural Networks and Machine Learning, the part of the book would make sense. Then the author talks about how the brain works (possibly to explain how it is different from Neural Networks). It is at this point that the author goes into gory details of hierarchical structure, which although makes sense but seems irrelevant to the line of argument.
Nevertheless, by the time this argument ends, you are left with only one more chapter where author tries to condense the gist of the entire argument by talking about what the intelligent machines should look like, how it should be designed and what would it achieve. In between, there is some explanation about cautiousness and awareness which although the author explains clearly has no relevance to the topic in discussion.
In the end, you will be utterly confused about the book. What was it about - Neural Networks, Psychology, Biology, Author? Oh, the book was about how in its current state of Artificial Intelligence may not be able to do what a human brain does - it could probably be explained in 50 pages. Ironically only about 50 pages are devoted to that. Rest of the pages could have been blank as well!
Readers may not have been able to understand the book because almost half of it talks about theory of brain system. As you go through that part, it feels like you are sitting in an algorithms class where the instructor tried to simplify the concepts but in the effort to do so, made it completely mundane and tiresome. The author hints on the fact that this part can be skipped but if you skip it, you will lose the continuity of what the book is trying to talk about, which bring us to the question - what is the book all about?
Well, the author starts with a conjecture that the book will be about why current state of Artificial Intelligence may not be able to design intelligent machines. For some time, the author talks about himself and then tries to explain the basic details of Neural Networks. If you already know about Neural Networks and Machine Learning, the part of the book would make sense. Then the author talks about how the brain works (possibly to explain how it is different from Neural Networks). It is at this point that the author goes into gory details of hierarchical structure, which although makes sense but seems irrelevant to the line of argument.
Nevertheless, by the time this argument ends, you are left with only one more chapter where author tries to condense the gist of the entire argument by talking about what the intelligent machines should look like, how it should be designed and what would it achieve. In between, there is some explanation about cautiousness and awareness which although the author explains clearly has no relevance to the topic in discussion.
In the end, you will be utterly confused about the book. What was it about - Neural Networks, Psychology, Biology, Author? Oh, the book was about how in its current state of Artificial Intelligence may not be able to do what a human brain does - it could probably be explained in 50 pages. Ironically only about 50 pages are devoted to that. Rest of the pages could have been blank as well!
ByDavid S. Wellhauseron February 19, 2015
On Intelligence is what happens when a computer scientist, with little sympathy for non-scientists, attempts to write about about AI, the human brain, and the future of sentient machines.
Jeff Hawkins did a pretty good job, but the arguments tended to get very technical, very quickly and the non-specialist reader might find themselves struggling to keep up. This is the main negative of the book.
Where the author may lose the sympathy, even tolerance, of many readers is near the end of the text when the issue of 'consciousness' is raised. Here the author appears to demonstrate, possibly, key markers of psychopathy...more likely as not this has more to do with not expressing themselves as well as they might have. However, there is a scene where a group of scientists, Hawkins among them, take their wine down to the river and begin to speak of consciousness. The author appears to morph into a psychic and emotive robot and is very aggressive about deconstructing the arguments and persons of those who have disagreed with him. This was a very disconcerting, even repellant, scene and it is one of the reasons this book only received 3 out of a possible 5 stars.
Still, Hawkins' theory of mind, which is only the byproduct of the brain interacting with its environment, is quite interesting. The brain is a pattern recognition memory system and not a computer. Whether or not the argument will prove out is unclear. Every generation, maybe every decade, someone or a school comes along determined that they have 'almost' solved the conundrum of consciousness...or mind [see above definition of this]. In each case, these individuals or schools fail. Some of their failures are epic and others tiresome, but failures they have all been.
This does not mean science and humans should stop looking for the answers but it does mean readers should be circumspect about accepting what they read too quickly.
On Intelligence gets a mild recommendation. Computer Scientists and Radical Secularists will find this interesting...maybe...but others will be left cold.
Jeff Hawkins did a pretty good job, but the arguments tended to get very technical, very quickly and the non-specialist reader might find themselves struggling to keep up. This is the main negative of the book.
Where the author may lose the sympathy, even tolerance, of many readers is near the end of the text when the issue of 'consciousness' is raised. Here the author appears to demonstrate, possibly, key markers of psychopathy...more likely as not this has more to do with not expressing themselves as well as they might have. However, there is a scene where a group of scientists, Hawkins among them, take their wine down to the river and begin to speak of consciousness. The author appears to morph into a psychic and emotive robot and is very aggressive about deconstructing the arguments and persons of those who have disagreed with him. This was a very disconcerting, even repellant, scene and it is one of the reasons this book only received 3 out of a possible 5 stars.
Still, Hawkins' theory of mind, which is only the byproduct of the brain interacting with its environment, is quite interesting. The brain is a pattern recognition memory system and not a computer. Whether or not the argument will prove out is unclear. Every generation, maybe every decade, someone or a school comes along determined that they have 'almost' solved the conundrum of consciousness...or mind [see above definition of this]. In each case, these individuals or schools fail. Some of their failures are epic and others tiresome, but failures they have all been.
This does not mean science and humans should stop looking for the answers but it does mean readers should be circumspect about accepting what they read too quickly.
On Intelligence gets a mild recommendation. Computer Scientists and Radical Secularists will find this interesting...maybe...but others will be left cold.
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