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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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5.0 out of 5 starsBy late 2012, the short term predictions in this book have already happened. Might be worth paying attention to the rest.
ByM.J.on September 9, 2012
This book was a great read, very accessible and might prove to be a very important book one day. It's concise and to the point and if you have any interest whatsoever in AI you simply can not miss this it. It's a quick read that will without a doubt have a significant impact on how you view the future of artificial intelligence.
As a testament to it's relevancy today (I'm writing this Sept 2012, seven years after the book was published) he predicts three technological applications that may become available in the short term (5-10 years) due to breakthroughs in the kind of trainable AI this book discusses:
Computer vision and teaching a computer to tell the difference between a cat and a dog (this was successfully demonstrated in a study published in June 2012 - the paper is called "Building High-level Features Using Large Scale Unsupervised Learning" and is available online, or just search for "computer learns to recognize cats" for articles)
PDAs (as they were called back then) will understand naturally spoken instructions like "Move my daughter's basketball game on Sunday to 10 in the morning" (this kind of sentence, copied from the book verbatim, is exactly where Apple's AI application SIRI shines)
Smart/autonomous cars - in Aug 2012, Google announced that their self driving cars have logged 300 K accident free miles in live traffic on public roads, exceeding the average distance a human drives without accident.
The thing to note here is that when he wrote the book these three things had hurdles that we did not know how to solve, and at the time there was no clear linear progression of existing solutions that would guarantee they would be solved. His prediction is that we'll be able to train computers to recognize patterns by themselves which will allow us to eventually solve the problems (and this is exactly how the computer learned to recognize cat faces from youtube videos)
Furthermore, he predicts that AI will become one of the hottest 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 deny that it lookslike we're right in the midst of seeing just this happen.
The grander implications of the model of this book won't be known for another 10-20 years or more, but 7 years in his general predictions about the field of AI have been very accurate.
As a testament to it's relevancy today (I'm writing this Sept 2012, seven years after the book was published) he predicts three technological applications that may become available in the short term (5-10 years) due to breakthroughs in the kind of trainable AI this book discusses:
Computer vision and teaching a computer to tell the difference between a cat and a dog (this was successfully demonstrated in a study published in June 2012 - the paper is called "Building High-level Features Using Large Scale Unsupervised Learning" and is available online, or just search for "computer learns to recognize cats" for articles)
PDAs (as they were called back then) will understand naturally spoken instructions like "Move my daughter's basketball game on Sunday to 10 in the morning" (this kind of sentence, copied from the book verbatim, is exactly where Apple's AI application SIRI shines)
Smart/autonomous cars - in Aug 2012, Google announced that their self driving cars have logged 300 K accident free miles in live traffic on public roads, exceeding the average distance a human drives without accident.
The thing to note here is that when he wrote the book these three things had hurdles that we did not know how to solve, and at the time there was no clear linear progression of existing solutions that would guarantee they would be solved. His prediction is that we'll be able to train computers to recognize patterns by themselves which will allow us to eventually solve the problems (and this is exactly how the computer learned to recognize cat faces from youtube videos)
Furthermore, he predicts that AI will become one of the hottest 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 deny that it lookslike we're right in the midst of seeing just this happen.
The grander implications of the model of this book won't be known for another 10-20 years or more, but 7 years in his general predictions about the field of AI have been very accurate.
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ByRyanon November 18, 2011
If, like me, you're a software developer with an interest in true artificial intelligence, this is a very stimulating book. Hawkins applies his own engineer's mind to an effort to discern and describe the human brain's underlying "cortical algorithm", the means by which intelligence "works". As Hawkins sees it, the neuroscience community has been too focused on the minutae of how neurons function, without giving adequate consideration to the brain's overall learning and decision-making architecture, while the computer science community has been too absorbed in traditional symbolic and procedural computation methods, ignoring insights that might be gleaned from studying the most powerful problem-solving system in nature. Of course, it's untrue that neuroscientists and comp-sci academics aren't interested in each other's disciplines, but the crossover is still a long way from mainstream. For coders working in industry (like me), Hawkin's thoughts may be revelatory.
The author focuses most of his attention on the cortex, the most recently evolved part of the human brain, and the one responsible for many functions of higher intelligence. His speculation is that this system uses the same generalized learning/prediction algorithm throughout, with little difference in how input from vision, hearing, touch, and other senses are processed. All this data is just sequences of patterns that the cortex filters through its multilayered hierarchy, each layer discerning trends in the input from lower layers, and forming models of the world.
This may sound like the traditional AI concept of "neural networks", but Hawkins breaks from that model with his view that the cortex uses massive amounts of feedback from higher, more time-invariant layers (which view the world more abstractly) to lower, more time-variant layers (which deal with more concrete experience), activating many context switches. He sees the cortex as a blank slate upon birth, which follows relatively simple programming to accumulate and categorizes knowledge. As our minds form, we find ourselves experiencing the world less through our sensory input, and more through our pre-formed models. Only when there is conflict between those models and our input sequences, is our conscious attention drawn to our senses.
In terms of biological neuroscience, this is all probably overly simplistic and not completely accurate (Hawkins doesn't give a lot of attention to the older, more instinctive parts of the brain), but if he's even partly right, his ideas have huge implications for artificial intelligence. If much our human intelligence really does boil down to a generalized memory-prediction algorithm -- one that may be complex, but not beyond our understanding -- the effects on the future will be astounding. Even if Hawkins wasn't able to prove his claims, they're fascinating to contemplate, and the next few decades will certainly shed a lot of light on their truth.
If this book speaks to you, consider also reading Marvin Minsky's A Society of Minds, which contains a lot of complementary ideas.
The author focuses most of his attention on the cortex, the most recently evolved part of the human brain, and the one responsible for many functions of higher intelligence. His speculation is that this system uses the same generalized learning/prediction algorithm throughout, with little difference in how input from vision, hearing, touch, and other senses are processed. All this data is just sequences of patterns that the cortex filters through its multilayered hierarchy, each layer discerning trends in the input from lower layers, and forming models of the world.
This may sound like the traditional AI concept of "neural networks", but Hawkins breaks from that model with his view that the cortex uses massive amounts of feedback from higher, more time-invariant layers (which view the world more abstractly) to lower, more time-variant layers (which deal with more concrete experience), activating many context switches. He sees the cortex as a blank slate upon birth, which follows relatively simple programming to accumulate and categorizes knowledge. As our minds form, we find ourselves experiencing the world less through our sensory input, and more through our pre-formed models. Only when there is conflict between those models and our input sequences, is our conscious attention drawn to our senses.
In terms of biological neuroscience, this is all probably overly simplistic and not completely accurate (Hawkins doesn't give a lot of attention to the older, more instinctive parts of the brain), but if he's even partly right, his ideas have huge implications for artificial intelligence. If much our human intelligence really does boil down to a generalized memory-prediction algorithm -- one that may be complex, but not beyond our understanding -- the effects on the future will be astounding. Even if Hawkins wasn't able to prove his claims, they're fascinating to contemplate, and the next few decades will certainly shed a lot of light on their truth.
If this book speaks to you, consider also reading Marvin Minsky's A Society of Minds, which contains a lot of complementary ideas.
ByOlivia Shafferon 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.
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.
I read this book when it was first published (in 2004) and recently re-read it while preparing for an interview of one of countless thought leaders who have acknowledged their great debt to Jeff Hawkins for what they have learned from him and, especially, for what they learned from this book. Written with Sandra Blakeslee, this book provides a superb discussion of topics that include
o Artificial intelligence
o Neural networks
o The structure and functions of the human brain
o A "new framework of intelligence" (more about that later)
o How the cortex works
o Consciousness and creativity
o Hawkins' thoughts about the future of intelligence
As Hawkins explains, his goal "is to explain [his] new theory of intelligence and how the brain works in a way that anybody will understand." However, I hasten to add, this is not a book written for dummies and idiots who wish to "fool" people into thinking they know and understand more than in fact they do.
Early on, Hawkins acknowledges his skepticism about artificial intelligence (AI) for reasons that are best explained within his narrative, in context. However, it can be said now that after extensive research, Hawkins concluded that three separate but related components are essential to understanding the brain: "My first criterion was the inclusion of time in brain function...The second criterion was the inclusion of feedback...The third criterion was that any theory or model of the brain should account for the physical architecture of the brain." AI capabilities, Hawkins notes, are severely limited in terms of (a) creating programs that replicate what the human mind can do, (b) must be perfect to work at all, and (c) AI "might lead to useful products, but it isn't going to build truly intelligence machines." At least not until we gain a much better understanding of the human brain.
The material in Chapter 7, "Consciousness and Creativity," is of special interest to me as I continue to read recently published books that offer breakthrough insights on creativity, innovation, and the processes by which to develop them. (The authors of many of those books, to borrow from a 12th century French monk, Bernard of Chartres, are standing on Dawkins' "shoulders." It must be getting crowded up there.) Hawkins asserts that creativity does not require high intelligence and giftedness, and defines creativity as "making predictions by analogy, something that occurs everywhere in cortex and something you do continually while awake. Creativity occurs along a continuum...At a fundamental level, everyday acts of perception are similar to rare flights of brilliance. It's just that the everyday acts are so common we don't notice them." I call this phenomenon "the invisibility of the obvious."
I am among those who are curious to know the answers to questions such as "Why are some people more creative than others?" "Can you train yourself to be more creative?" "What is consciousness?" and "What is imagination?" Hawkins has formulated answers to these and other questions and shares them in this chapter. Much of the structure of the "new framework of intelligence" to which I referred earlier is in place by the conclusion of this chapter. Then Hawkins concludes the book by looking to the future and offers with eleven predictions. Here's #8: "Sudden understanding should result in a precise cascading of predictive activity that flows down the cortical hierarchy." In other words, revelations (whatever their nature and scope) help us, not only to connect dots but to connect those that are most important.
o Artificial intelligence
o Neural networks
o The structure and functions of the human brain
o A "new framework of intelligence" (more about that later)
o How the cortex works
o Consciousness and creativity
o Hawkins' thoughts about the future of intelligence
As Hawkins explains, his goal "is to explain [his] new theory of intelligence and how the brain works in a way that anybody will understand." However, I hasten to add, this is not a book written for dummies and idiots who wish to "fool" people into thinking they know and understand more than in fact they do.
Early on, Hawkins acknowledges his skepticism about artificial intelligence (AI) for reasons that are best explained within his narrative, in context. However, it can be said now that after extensive research, Hawkins concluded that three separate but related components are essential to understanding the brain: "My first criterion was the inclusion of time in brain function...The second criterion was the inclusion of feedback...The third criterion was that any theory or model of the brain should account for the physical architecture of the brain." AI capabilities, Hawkins notes, are severely limited in terms of (a) creating programs that replicate what the human mind can do, (b) must be perfect to work at all, and (c) AI "might lead to useful products, but it isn't going to build truly intelligence machines." At least not until we gain a much better understanding of the human brain.
The material in Chapter 7, "Consciousness and Creativity," is of special interest to me as I continue to read recently published books that offer breakthrough insights on creativity, innovation, and the processes by which to develop them. (The authors of many of those books, to borrow from a 12th century French monk, Bernard of Chartres, are standing on Dawkins' "shoulders." It must be getting crowded up there.) Hawkins asserts that creativity does not require high intelligence and giftedness, and defines creativity as "making predictions by analogy, something that occurs everywhere in cortex and something you do continually while awake. Creativity occurs along a continuum...At a fundamental level, everyday acts of perception are similar to rare flights of brilliance. It's just that the everyday acts are so common we don't notice them." I call this phenomenon "the invisibility of the obvious."
I am among those who are curious to know the answers to questions such as "Why are some people more creative than others?" "Can you train yourself to be more creative?" "What is consciousness?" and "What is imagination?" Hawkins has formulated answers to these and other questions and shares them in this chapter. Much of the structure of the "new framework of intelligence" to which I referred earlier is in place by the conclusion of this chapter. Then Hawkins concludes the book by looking to the future and offers with eleven predictions. Here's #8: "Sudden understanding should result in a precise cascading of predictive activity that flows down the cortical hierarchy." In other words, revelations (whatever their nature and scope) help us, not only to connect dots but to connect those that are most important.
ByJacob Johnsonon September 29, 2013
As a student at Georgia Tech studying Biomedical engineering, I decided to take an Intro to Neuroscience class this fall semester. One of the assignments that was given to us during the first week of school was to pick a Neuroscience related book, read it, and write a review on Amazon. Having not a clue of what book to choose, I searched through Google and Amazon in order to find an enjoyable read that would prove to be both intriguing and insightful. Stumbling upon Jeff Hawkins book, On Intelligence, I had no idea what to expect. From the summaries, I could only guess that this book would relate the intelligence processed between humans and computers. In addition, I discovered that Hawkins was the original inventor of the once famed Palm Pilot that took America by storm just prior to the new millennium.
It is rare that I come across and academic-related book and find myself fully engaged. However, On Intelligence seemed to do exactly that. Hawkins provides an alternate perception of how the brain works and gives us a secret mechanism that can be unlocked to predict the future. This new approached immediately substituted my view on the way the brain functions. Having always thought that the brain was simply a computer executing various commands, I quickly learned that the real function is its ability to make predictions about the future. Explaining the intricate details of the individual 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. It is the primary function of the neo-cortex, and the foundation of intelligence". What was even more fascinating was the concept of how we can make these predictions base on memories that are built in our brain overtime. On Intelligence, was a truly an eye-opening book for me and forced me to turn to the next page after each chapter.
Hawkins gives his own outline of the book in order for the reader to better understand the goal of his writing. Starting with a background on some of the previous attempts at understanding intelligence and how those theories have failed, Hawkins develops an essential theory of what he calls the memory-prediction framework. He uses thought experiments to illustrate the extensiveness of prediction as well as evolutionary comparisons to explain the brains function as it relates to intelligence. Although Hawkins doesn't dwell to long on the biological functions and mechanisms of how these processes are performed, he does spend quite a bit of time on the methods of operation of how the cortex utilizes a hierarchy of invariant memories and sequences to make futuristic predictions. The shift is then made towards the final chapters of the book in which Hawkins starts tying the connections between intelligence and the creative side of the brain. He explains how this source of power can be used to our advantage and determines the success rate for years to come. Lastly, Hawkins makes some predictions of involving the fears of intelligent machines, "Throughout the twenty-first century, intelligent machines will emerge from the realm of science fiction into fact". This final section made me ponder the reality of all of those science fiction books and movies where AI can develop the impossible characteristics of emotions, creativity and self-learning. Most of these plots nearly always have a negative outcome.
Hawkins choice of style and structure of the book proves to be an interesting one. He immediately delivers his personal experiences by explaining the method in which he came upon his theory. The first chapter is dedicated to his personal history and how he became interested and involved in the area of neuroscience. Failing to be accepting into MIT, Hawkins explains the scientific establishment has always rejected the link between neuroscience and artificial intelligence. After explaining his past and his previous thoughts and ideas, Hawkins dives into the main section of the book that includes his primary theory. As stated before, he develops the concept that the brain is a mechanism that has the ability to predict the future. Hawkins predicts that in all areas of the cortex, "anticipatory cells" can be found that fire only in anticipation of a sensory event. He then goes on to explain the primary functions of each of the cortexes throughout corresponding chapters.
Hawkins is able to attract the reader so quickly by his ways of illustrating specific examples or inquiring about memory tests for the reader to actually perform. He uses these concepts and analogies in order to better explain the framework for his ideas and concepts of cortex function. I think these methods were extremely crucial in achieving the goal set out by Hawkins; to enlighten all audiences interested in how the brain functions. Although many neuroscientists today can agree that very little is actually known about the processes and mechanisms of neural functions with validation, Hawkins provides his alternative approach to possibly diminish this vast gap of our understanding. Although some readers may be overwhelmed by the enormous amounts of information confined within this book, I believe it still gets the message across in a significant way as to avoid the loss of the concept and still spark the interest for future understanding.
My recommendation for future readers would be to do some outside research prior to reading this book. Although you can come in with absolutely no knowledge of brain function, it would not hurt to get some basic knowledge of the different systems, such as the visual, auditory and sensory systems work in individually and in unison. This will most likely reduce the overwhelming factor previously mentioned and truly aid in your fascination of the theories described. Perhaps check out some other neuroscience books as well. However, when it comes to the primary focus of this book and how it relates to artificial intelligence, you will not find a better read. For that case, I have given Jeff Hawkin's masterpiece, On Intelligence, 5 out of 5 stars.
It is rare that I come across and academic-related book and find myself fully engaged. However, On Intelligence seemed to do exactly that. Hawkins provides an alternate perception of how the brain works and gives us a secret mechanism that can be unlocked to predict the future. This new approached immediately substituted my view on the way the brain functions. Having always thought that the brain was simply a computer executing various commands, I quickly learned that the real function is its ability to make predictions about the future. Explaining the intricate details of the individual 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. It is the primary function of the neo-cortex, and the foundation of intelligence". What was even more fascinating was the concept of how we can make these predictions base on memories that are built in our brain overtime. On Intelligence, was a truly an eye-opening book for me and forced me to turn to the next page after each chapter.
Hawkins gives his own outline of the book in order for the reader to better understand the goal of his writing. Starting with a background on some of the previous attempts at understanding intelligence and how those theories have failed, Hawkins develops an essential theory of what he calls the memory-prediction framework. He uses thought experiments to illustrate the extensiveness of prediction as well as evolutionary comparisons to explain the brains function as it relates to intelligence. Although Hawkins doesn't dwell to long on the biological functions and mechanisms of how these processes are performed, he does spend quite a bit of time on the methods of operation of how the cortex utilizes a hierarchy of invariant memories and sequences to make futuristic predictions. The shift is then made towards the final chapters of the book in which Hawkins starts tying the connections between intelligence and the creative side of the brain. He explains how this source of power can be used to our advantage and determines the success rate for years to come. Lastly, Hawkins makes some predictions of involving the fears of intelligent machines, "Throughout the twenty-first century, intelligent machines will emerge from the realm of science fiction into fact". This final section made me ponder the reality of all of those science fiction books and movies where AI can develop the impossible characteristics of emotions, creativity and self-learning. Most of these plots nearly always have a negative outcome.
Hawkins choice of style and structure of the book proves to be an interesting one. He immediately delivers his personal experiences by explaining the method in which he came upon his theory. The first chapter is dedicated to his personal history and how he became interested and involved in the area of neuroscience. Failing to be accepting into MIT, Hawkins explains the scientific establishment has always rejected the link between neuroscience and artificial intelligence. After explaining his past and his previous thoughts and ideas, Hawkins dives into the main section of the book that includes his primary theory. As stated before, he develops the concept that the brain is a mechanism that has the ability to predict the future. Hawkins predicts that in all areas of the cortex, "anticipatory cells" can be found that fire only in anticipation of a sensory event. He then goes on to explain the primary functions of each of the cortexes throughout corresponding chapters.
Hawkins is able to attract the reader so quickly by his ways of illustrating specific examples or inquiring about memory tests for the reader to actually perform. He uses these concepts and analogies in order to better explain the framework for his ideas and concepts of cortex function. I think these methods were extremely crucial in achieving the goal set out by Hawkins; to enlighten all audiences interested in how the brain functions. Although many neuroscientists today can agree that very little is actually known about the processes and mechanisms of neural functions with validation, Hawkins provides his alternative approach to possibly diminish this vast gap of our understanding. Although some readers may be overwhelmed by the enormous amounts of information confined within this book, I believe it still gets the message across in a significant way as to avoid the loss of the concept and still spark the interest for future understanding.
My recommendation for future readers would be to do some outside research prior to reading this book. Although you can come in with absolutely no knowledge of brain function, it would not hurt to get some basic knowledge of the different systems, such as the visual, auditory and sensory systems work in individually and in unison. This will most likely reduce the overwhelming factor previously mentioned and truly aid in your fascination of the theories described. Perhaps check out some other neuroscience books as well. However, when it comes to the primary focus of this book and how it relates to artificial intelligence, you will not find a better read. For that case, I have given Jeff Hawkin's masterpiece, On Intelligence, 5 out of 5 stars.
ByRichard Salinason September 30, 2017
This was my first exposure to artificial neural networks and how neural networks work. I read this as a hardback a few years ago... it's dated, but full of relevant nuggets. Worth my recent buy as an ebook so that I can utilize the search feature.
ByRobert D. Craneon January 9, 2010
This book provides some very stimulating insights into how human being go about the process of thinking and how the brain functions. It helps you understand why things like Artificial Inteliigence are no where near matching the marvel that is the human brain.
To get the most of this book you will need to sit down and concentrate because there is plenty in here to digest. It is not something I'd call bed time reading. If you are interested in learning how the human brain functions and what makes it do what it does then this book is for you.
Personally, it has changed the way that I look at many things about brain functions. I also reckon it is going to help me better understand my own brain and get more from it. In short, a really worthwhile read.
To get the most of this book you will need to sit down and concentrate because there is plenty in here to digest. It is not something I'd call bed time reading. If you are interested in learning how the human brain functions and what makes it do what it does then this book is for you.
Personally, it has changed the way that I look at many things about brain functions. I also reckon it is going to help me better understand my own brain and get more from it. In short, a really worthwhile read.
ByKoo Ping Shungon March 25, 2018
I do a lot of self-study (self-funded) on artificial intelligence especially when it overlaps to a good extent with data science(but they are not equivalent). This book definitely opens up another horizon, to know that the current hype on deep learning may not be the ‘right’ approach in building AGI. I’m no genius but time will tell if deep learning is the right path or the path proposed by the author is correct.
BySotto voceon November 24, 2006
The first half looks at the organic brain machine. It makes sense out of the myriad of informational facts we have about the mind. Hawkin's kind of takes the jigsaw puzzle and creates the outer edge of the picture.
Artificial intelligence and raw power computation get hammered in the 2nd half as way to simplistic. The brain seems to solve the most complex problems in <200 steps vice terraflops of calculation. For the brain, simple is better. For computation, complex iterations yielding ambiguity seem like a dead end.
The second half is not an easy read. The first half is worth the price of the time.
Artificial intelligence and raw power computation get hammered in the 2nd half as way to simplistic. The brain seems to solve the most complex problems in <200 steps vice terraflops of calculation. For the brain, simple is better. For computation, complex iterations yielding ambiguity seem like a dead end.
The second half is not an easy read. The first half is worth the price of the time.
Jonathan Hawkins's concern in "On Intelligence" is to outline a theory of what intelligence is that differs from ones floated around in various artificial intelligence (AI) circles. First, most theories of how to build "intelligent machines" focus exclusively on "intelligent behavior" without focus on the "thought" that must be behind it. (Think about Alan Turing's test of an intelligent machine: if its behavior seems intelligent to humans, it must be intelligent. Purely behavioral.) Also, Hawkins is concerned that those few AI folks who have given thought to what intelligence is, apart from behavior, see intelligence as "ability to computer" and analogize it to a computer. But, Hawkins rightly notes, what we see as human intelligence -ability to synthesize disparate information, create novel solutions, apply old knowledge to new problems - is much more than computation.
Hawkins offers a very different theory to explain intelligence; intelligence, he writes, is the ability to predict outcomes. Hawkins goes a long way towards demonstrating how predicting outcomes is so much a part of what we do that we hardly even notice how much we do this. Every time we act, we predict (very subtly) the outcome of the action, and, because we predicted the outcome, are surprised when the plan goes awry. When I walk down the steps, I predict where each step will be so that I move my leg accordingly, and become alarmed when the step is not there. When I speak, I predict how the listener will interpret my words, and problem solve when the listener "misinterprets."
As an educator, this book speaks to a very relevant part of my job. In order to enrich students' intelligence, it is good to have an idea of what intelligence is so that we can teach the correct things via the correct methods. Hawkins theory that intelligence is the ability to predict and problem solve based on prediction seems a good description of what educators mean when we speak of intelligence. Intelligence tests do seem to test on tasks necessary for prediction. Some tests assess the ability to recognize patterns and predict future patterns (1, 3, 5, ?). Other tests look at the ability to recognize the relevant information from written passages ("Choose the option that best paraphrases the passage.") while other tests assess the ability to openly make predictions ("What will happen next in the story?").
If there is one flaw in Hawkins book, it is the discussion on creativity, which he reduces to the ability to make predictions. He makes a compelling case that creativity can be reduced this way in some instances: the mathematician solving a theorem. His case involving artistic creativity, and its blend of novelty and recycling, is a bit more tenuous. Despite Hawkins's argument, I cannot see how writing a poem in any way involves making a prediction (unless the prediction is about how the poem will be received). My guess is that some aspects of creativity involve intelligence (what words rhyme with ___? Will readers understand this metaphor?") Other areas (what metaphor can I create for this experience? Does an "open" or "closed" vowel sound better to end this line?) cannot be reduced to basic prediction. (I also have a problem with Hawkins's discussion of what consciousness is, which I think is question-begging, but I won't elaborate here. Read it for yourself and see what you think.
Anyhow, Hawkins book and theory is very eye-opening and, in my view, more "on the money" than the computational or behavioral model of intelligence. This is especially so in light of the fact that Hawkins's used this theory of intelligence as ability to predict and learn via memory storage to create the PalmPilot and the Graffiti handwriting software it uses. Much recommended for anyone interested in reading a different theory of intelligence - especially educators. [For those interested in a book that compliments this one, read What Intelligence Tests Miss: The Psychology of Rational Thought, which broadens our standard definition of intelligence to include the ability to reason and solve problems based on stored information, much like Hawkins does.
]
Hawkins offers a very different theory to explain intelligence; intelligence, he writes, is the ability to predict outcomes. Hawkins goes a long way towards demonstrating how predicting outcomes is so much a part of what we do that we hardly even notice how much we do this. Every time we act, we predict (very subtly) the outcome of the action, and, because we predicted the outcome, are surprised when the plan goes awry. When I walk down the steps, I predict where each step will be so that I move my leg accordingly, and become alarmed when the step is not there. When I speak, I predict how the listener will interpret my words, and problem solve when the listener "misinterprets."
As an educator, this book speaks to a very relevant part of my job. In order to enrich students' intelligence, it is good to have an idea of what intelligence is so that we can teach the correct things via the correct methods. Hawkins theory that intelligence is the ability to predict and problem solve based on prediction seems a good description of what educators mean when we speak of intelligence. Intelligence tests do seem to test on tasks necessary for prediction. Some tests assess the ability to recognize patterns and predict future patterns (1, 3, 5, ?). Other tests look at the ability to recognize the relevant information from written passages ("Choose the option that best paraphrases the passage.") while other tests assess the ability to openly make predictions ("What will happen next in the story?").
If there is one flaw in Hawkins book, it is the discussion on creativity, which he reduces to the ability to make predictions. He makes a compelling case that creativity can be reduced this way in some instances: the mathematician solving a theorem. His case involving artistic creativity, and its blend of novelty and recycling, is a bit more tenuous. Despite Hawkins's argument, I cannot see how writing a poem in any way involves making a prediction (unless the prediction is about how the poem will be received). My guess is that some aspects of creativity involve intelligence (what words rhyme with ___? Will readers understand this metaphor?") Other areas (what metaphor can I create for this experience? Does an "open" or "closed" vowel sound better to end this line?) cannot be reduced to basic prediction. (I also have a problem with Hawkins's discussion of what consciousness is, which I think is question-begging, but I won't elaborate here. Read it for yourself and see what you think.
Anyhow, Hawkins book and theory is very eye-opening and, in my view, more "on the money" than the computational or behavioral model of intelligence. This is especially so in light of the fact that Hawkins's used this theory of intelligence as ability to predict and learn via memory storage to create the PalmPilot and the Graffiti handwriting software it uses. Much recommended for anyone interested in reading a different theory of intelligence - especially educators. [For those interested in a book that compliments this one, read What Intelligence Tests Miss: The Psychology of Rational Thought, which broadens our standard definition of intelligence to include the ability to reason and solve problems based on stored information, much like Hawkins does.
]
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