On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines
On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines book cover

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

Hardcover – October 3, 2004

by
Price
$13.01
Format
Hardcover
Pages
272
Publisher
Times Books
Publication Date
ISBN-13
978-0805074567
Dimensions
6.14 x 0.62 x 9.21 inches
Weight
1.2 pounds

Description

Jeff Hawkins, the high-tech success story behind PalmPilots and the Redwood Neuroscience Institute, does a lot of thinking about thinking. In On Intelligence Hawkins juxtaposes his two loves--computers and brains--to examine the real future of artificial intelligence. In doing so, he unites two fields of study that have been moving uneasily toward one another for at least two decades. Most people think that computers are getting smarter, and that maybe someday, they'll be as smart as we humans are. But Hawkins explains why the way we build computers today won't take us down that path. He shows, using nicely accessible examples, that our brains are memory-driven systems that use our five senses and our perception of time, space, and consciousness in a way that's totally unlike the relatively simple structures of even the most complex computer chip. Readers who gobbled up Ray Kurzweil's ( The Age of Spiritual Machines and Steven Johnson's Mind Wide Open will find more intriguing food for thought here. Hawkins does a good job of outlining current brain research for a general audience, and his enthusiasm for brains is surprisingly contagious. --Therese Littleton From Publishers Weekly Hawkins designed the technical innovations that make handheld computers like the Palm Pilot ubiquitous. But he also has a lifelong passion for the mysteries of the brain, and he's convinced that artificial intelligence theorists are misguided in focusing on the limits of computational power rather than on the nature of human thought. He "pops the hood" of the neocortex and carefully articulates a theory of consciousness and intelligence that offers radical options for future researchers. "[T]he ability to make predictions about the future... is the crux of intelligence," he argues. The predictions are based on accumulated memories, and Hawkins suggests that humanoid robotics, the attempt to build robots with humanlike bodies, will create machines that are more expensive and impractical than machines reproducing genuinely human-level processes such as complex-pattern analysis, which can be applied to speech recognition, weather analysis and smart cars. Hawkins presents his ideas, with help from New York Times science writer Blakeslee, in chatty, easy-to-grasp language that still respects the brain's technical complexity. He fully anticipates—even welcomes—the controversy he may provoke within the scientific community and admits that he might be wrong, even as he offers a checklist of potential discoveries that could prove him right. His engaging speculations are sure to win fans of authors like Steven Johnson and Daniel Dennett. Copyright © Reed Business Information, a division of Reed Elsevier Inc. All rights reserved. From Scientific American "This book and my life are animated by two passions," writes Hawkins in On Intelligence. Those passions are mobile computing and brains. This curious combination becomes less puzzling when one realizes that Hawkins is a founder not only of two leading mobile computing companiesx97Palm Computing and Handspringx97but also of the Redwood Neuroscience Institute in Menlo Park, Calif., which explores memory and cognition. Hawkins contends that the human brain and intelligence have little in common with todayx92s computing systems. Therefore, he offers his perspective on artificial intelligence, neural networks, cognition, consciousness and creativity, with the goal of explaining the mind. The book is elegantly written with Blakeslee, a veteran science writer for the New York Times. At its core, the book puts forth Hawkinsx92s "memory-prediction framework of intelligence"x97a model of cognition positing that the main function of the human neocortex, and the basis of intelligence, is to make predictions. The brain constantly compares new sensory information with stored memories and experiences and combines the information to anticipate the future. In essence, as we wander around, we build a reserve of information from which we construct an internal model of the world. But we constantly update that model. When we see a friend wearing a new hat, the brain automatically predicts what that person ought to look like and contrasts that prediction with the new sensory rendering, updating its model. Brain prediction "is so pervasive," Hawkins says, "that what we x91perceivex92... does not come solely from our senses." The continuous interplay of sensory input, memory, prediction and feedbackx97which occurs instantly through parallel processing in the neocortexx97ultimately gives rise to consciousness and intelligence. "Correct predictions," Hawkins contends, "result in understanding." Hawkins argues that creativity and imagination emerge from prediction as well. Imagination utilizes a neural mechanism to transform predictions into a form of sensory inputx97which is why our fantasies have such a strong "feel." Moving on, Hawkins says that true machine intelligence will arise only if it is rooted in the same principles as brain-based intelligence. By the bookx92s end, Hawkins proffers a "comprehensive theory of how the brain works," of "what intelligence is," and of "how your brain creates it." He acknowledges that many aspects of his theory have been developed by other scientists and that his role is to weave a comprehensive explanation. As such, this book provides some provocative thoughts on how the brain and the mind may actually function. Richard Lipkin From Booklist A successful designer of handheld computers, Hawkins here explains (with help from New York Times science writer Blakeslee) his passion for artificial intelligence (AI). He holds that AI research has been on an unpromising path toward developing a program big and fast enough to be pronounced "intelligent." Such a brute-force approach is not how the human brain functions, so by way of proposing an alternative AI strategy, Hawkins explains how our brains work, admitting that his views are speculative. He delves into the anatomy of the neocortex, the thin structure that covers the brain and is the seat of higher-level thought. Hawkins virtually encapsulates for a popular audience the scientific literature on how the neocortex constructs a model of the world. The author becomes quite detailed in his explanations of memory formation yet never digresses from his core precept that intelligence is prediction. His argument is complex but comprehensible, and his curiosity will intrigue anyone interested in the lessons neurobiology may hold for AI. Gilbert Taylor Copyright © American Library Association. All rights reserved “ On Intelligence will have a big impact; everyone should read it. In the same way that Erwin Schrödinger's 1943 classic What is Life? made how molecules store genetic information then the big problem for biology, On Intelligence lays out the framework for understanding the brain.” ―James D. Watson, president, Cold Spring Harbor Laboratory, and Nobel laureate in Physiology“Brilliant and embued with startling clarity. On Intelligence is the most important book in neuroscience, psychology, and artificial intelligence in a generation.” ―Malcolm Young, neurobiologist and provost, University of Newcastle“Read this book. Burn all the others. It is original, inventive, and thoughtful, from one of the world's foremost thinkers. Jeff Hawkins will change the way the world thinks about intelligence and the prospect of intelligent machines.” ―John Doerr, partner, Kleiner Perkins Caufield & Byers Jeff Hawkins , co-author of On Intelligence ,xa0is one of the most successful and highly regarded computer architects and entrepreneurs in Silicon Valley. He founded Palm Computing and Handspring, and created the Redwood Neuroscience Institute to promote research on memory and cognition. Also a member of the scientific board of Cold Spring Harbor Laboratories, he lives in northern California. Sandra Blakeslee has been writing about science and medicine for The New York Times for more than thirty years and is the co-author of Phantoms in the Brain by V. S. Ramachandran and of Judith Wallerstein's bestselling books on psychology and marriage. She lives in Santa Fe, New Mexico. Excerpt. © Reprinted by permission. All rights reserved. From On Intelligence :Let me show why computing is not intelligence. Consider the task of catching a ball. Someone throws a ball to you, you see it traveling towards you, and in less than a second you snatch it out of the air. This doesn't seem too difficult-until you try to program a robot arm to do the same. As many a graduate student has found out the hard way, it seems nearly impossible. When engineers or computer scientists try to solve this problem, they first try to calculate the flight of the ball to determine where it will be when it reaches the arm. This calculation requires solving a set of equations of the type you learn in high school physics. Next, all the joints of a robotic arm have to be adjusted in concert to move the hand into the proper position. This whole operation has to be repeated multiple times, for as the ball approaches, the robot gets better information about its location and trajectory. If the robot waits to start moving until it knows exactly where the ball will land it will be too late to catch it. A computer requires millions of steps to solve the numerous mathematical equations to catch the ball. And although it's imaginable that a computer might be programmed to successfully solve this problem, the brain solves it in a different, faster, more intelligent way. Read more

Features & Highlights

  • From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines
  • Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.Written with acclaimed science writer Sandra Blakeslee,
  • On Intelligence
  • promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.

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Most Helpful Reviews

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interesting, but not convincing

This is an interesting book, but I'm not at all convinced of most of its major theses. There are way too many statements like "I believe xyz" in the book, and way too few along the lines of "Empirical evidence shows xyz." Hawkins seems to have committed himself to certain dogmas, many of which are probably oversimplifications. For instance, he insists that all the areas of the neocortex are essentially instances of the same software; for a completely contrary view, see Steven Pinker's The Language Instinct and How the Mind Works. Pinker, unlike Hawkins, starts by painstakingly laying out the evidence for the things he really knows empirically are true, and only then indulges in wild speculation.
29 people found this helpful
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Exciting new theory on intelligence

We often routinely talk about intelligence and we attempt to measure it for for a variety of purposes. But do we know what it is? Jeff Hawkins is one of the first people to present a specific and comprehesensive theory of intelligence with a leading role for the human neocortex. Hawkins starts by stating that Human intelliigence is fundamentally different from what a computer does.

But isn't artifical intelligence (AI) a good metaphor for human intelligence? No, says Hawkins. In AI a computer is taught to solve problems beloning to a specific domain based on a large set of data and rules. In comparison to human intelligence AI systems are very limited. They are only good for the one thing they were designed for. Teaching an AI based system to perform a task like catching a ball is hard because it would require vast amounts of data and complicated algorithms to capture the complex features of the environment. A human would have little difficulty in solving such everyday problems much easier and quicker.

Ok, but aren't neural networks then a good approximation of human intelligence? Although they are indeed an improvement to AI and have made possible some very practical tools they are still very different to human intelligence. Not only are human brains structurally much more complicated, there are clear functional differences too. For instance, in a neural network information flows only one direction while in the human brain there is a constant flow of information in two directions.

Well, isn't the brain then like a parallel computer in which billions of cells are concurrently computing? Is parallel computing what makes human so fast in solving complex problems like catching a ball? No, says the author. He explains that a human being can perform significant tasks within much less time than a second. Neurons are so slow that in that fraction of a second they can only traverse a chain of 100 neurons long. Computers can do nothing useful in so few steps. How can a human accomplish it?

All right, human intelligence is different from what our computers do. What then is it? I'll try to summarize Hawkin's theory.

The neocortex constantly receives sequences of patterns of information, which it stores by creating so-called invariant representations (memories independent of details). These representations allow you to handle variations in the world automatically. For instance, you can still recognize your friends face although she is wearing a new hairstyle.

All memories are stored in the synaptic connections between neurons. Although there is a vast amount of information stored in the neocortex only a few things are atively remembered at one time. This is so because a system, called `autoassociative memory' takes care that only the particular part of the memory is activated which is relevant to the current situation (the patterns that are currently flowing in the brain). On the basis of these activated memory patterns predictions are made -without us being aware of it- about what will happen next. The incoming patterns are compared to and combined with the patterns provided by memory result in your perception of a situation. So, what you perceive is not only based on what your eyes, ears, etc tell you. In fact, theses senses give you fuzzy and partial information. Only when combined with the activated patterns from your memory, you get a consistent perception.

The hierarchical structure of the neocortex plays an important role in perception and learning. Low regions in the structure of the neocortex make low-level predictions (about concreet information like color, time, tone, etc) about what they expect to encounter next, while higher-level regions make higher-level predictions (about more abstract things. Understanding something means that the neocortex' prediction fits with the new sensory input. Whenever neocortex patterns and sensory patterns conflict, there is confusion and your attention is drawn to this error. The error is then sent up to higher neocortex regions to check if the situation can be understood on a higher level. In other words: are there patterns to be found somewhere else in the neocortex, which do fit to the current sensory input?

Learning roughly takes place as follows. During repetitive learning memories of the world first form in higher regions of the cortex but as your learn they are reformed in lower parts of the cortical hierarchy. So, well-learned patterns are represented low in the cortex while new information is sent to higher parts. Slowly but surely the neocortex builds in itself a representation of the world it encounters. Hawkins: "The real world's nested structure is mirrored by the nested structure of your cortex."

This model explains well the efficiency and great speed of the human brain while dealing with complex tasks of a familiar kind. The downside is that we are not seeing and hearing precisely what is happening. When someone is talking we by definition don't fully listen to what he says. Instead, we constantly predict what he will say next and as long as there seems to be a fit between prediction and incoming sensory information our attention remains rather low. Only when he will say something, which is actively conflicting with our prediction, we will pay attention.

The author takes his model one step further by saying that even the motor system is prediction driven. In other words, the human neocortex directs behavior to satisfy its predictions. Hawkins says that doing something is literally the start of how we do it. Remembering, predicting, perceiving and doing are all very intertwined.

I think this is a fascinating and stimulating book. Many questions about intelligence may remain unanswered but I believe this book to be a step forward in our quest to understand intelligence. The author predicts we can soon build intelligence in computersystems by using the principles of the neocortex. He is optimistic about what will happen once we succeed in this. He (reasonably convincing) argues these systems will be useful for humanity and not a threat.

Coert Visser, [...]
18 people found this helpful
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The Amazing Prediction Machine

On Intelligence takes us down two paths. The first and least interesting is a survey of the moribund state of artificial intelligence, or AI. Jeff Hawkins claims that AI applications haven't lived up to their hype because they focus on machine logic, connectivity and processing power instead of understanding and replicating the decision-making capabilities of the human brain. Which is his second and much more compelling path: an exploration of how the human brain produces intelligent thought.

Hawkins focuses on the neocortex, a relatively recent evolutionary addition to the human brain. The size of our cortex (a sheet of cells about the size of a dinner napkin) relative to our body mass gives us a huge advantage over other mammals and other species. Hawkins' central assertion, derived from the theories of Vernon Mountcastle, is that the human cortex functions in the same way no matter what kind of data it's processing. Details brought in through our sensory organs - seeing, hearing, touching - are converted into neural patterns, then processed up through a hierarchy of cortical levels. Information gathered in the present moment is matched against previously built neural patterns stored in the cortex.

Intelligence, then, is matching the data you pick up from your current environment against the representations of reality stored in your cortex in order to make predictions about the future. Your cortex is essentially a prediction machine. Its complexity lies not in its structure but in the trillions of neural connections linking different cortical layers and levels to one another. As you learn something, you drive its stored representations down to lower levels of the cortex, freeing up capacity to learn more sophisticated representations. An expert, whether it's a plumber, stockbroker, or oboe player, is someone who has stored more representation, and can therefore make more sophisticated predictions.

What Hawkins proposes makes intuitive sense. You can test it out yourself by taking a walk and observing how your mind works. On a beach, for instance, you'll take in the warmth of the sun, the force of the wind, the sound of the waves, and match them up against mental templates to make a prediction. If the weather is benign, you'll predict a leisurely walk down the beach. If something is off - a cloud blocking the sun, a stronger wind, a bigger boom when the waves crash - you'll run this up against other mental patterns and predict the onset of a storm. In all cases you are relying on the inputs of your senses and matching them against stored representations of past experiences.

The book becomes dense with scientific detail only in Chapter 6 which is a level by level description of how the cortex works. Even here, the prose is relatively jargon-free, and the essential points are accessible to the dedicated non-scientist.

Hawkins' goal - a unified theory of how the brain works - is admirable, and his hypotheses seem well-reasoned, at least to this non-scientist. In the interests of simplicity and clarity, though, he steps lightly over some of the messier aspects of mental functioning such as individual consciousness (how do I know it's me writing this?) and the subjective quality of human perception (is the red I see the same red you see?). Hawkins doesn't ignore these issues, but he doesn't address them in any detail either.

Except for the thalamus, (seen as an essential organ for sequencing information in representational patterns) he also doesn't spend a lot of time dealing with the ways in which other brain and body parts affect our pattern-making processes. Emotions and mental pathologies, not to mention false memories, have a huge impact on our ability to predict future outcomes. Which is why some writers and philosophers see the brain not as a well-oiled prediction machine, but as a hallucination engine. Mental pathologies aren't the focus of Hawkin's investigations, but some good companion works to read would include Antonio Damasio's The Feeling of What Happens, and Oliver Sack's The Man Who Mistook His Wife for a Hat.

These issues aside, Hawkins deserves a lot of credit for injecting some provocative new thinking into the neurobiological debates, and for doing it in clear and accessible prose. He even provides samples of testable predictions that can be run as experiments to prove or disprove his hypotheses. Whether you ultimately agree or disagree with Hawkins, you'll come away with some new ways of looking at that amazing arrangement of neurons situated between your ears.
15 people found this helpful
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Too Much of an Attack on AI

Mr. Hawkins has some interesting ideas, but I think the presentation of the ideas is seriously marred by the presentation as an attack on the areas of AI that Mr. Hawkins doesn't like for one reason or another. He spends an inordinate amount of time presenting what are in reality fairly superficial critiques of various AI theories and methodologies. There seems to be several roots of his distaste for these methods, but many seem to be rooted in Mr. Hawkins view of AI as a way to understand the brain, but that's not the focus of most AI researchers. Most AI researchers are trying to solve problems and don't tie slavishly tie themselves to trying to replicate the human brain. After all we already have human brains, why replicate them in silicon when silicon has different properties and capabilities.

I suppose my main criticism is that Mr. Hawkins doesn't seem to have spent nearly as much time reviewing his own theories as he has looking for flaws in other theories. This manifests itself as bevy of rather shallow, self-serving and easily refuted dismissals of various competing AI theories and methodologies.
12 people found this helpful
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Engaging and informative

This is a light and engaging read on the structure and function of the brain for lamen. The first few chapters debunk historical artificial intelligence efforts. The author then continues on to propose his theory on the function of the neocortex. Which is a hierarchal mesh of pattern recognizing nodes that propagate both forward and backward to predict the world and to react to changing responses.

I enjoyed this book. It was a bit of a struggle in the first few chapters, but after he got into developing his own theories it turned into a page flipper that was hard to put down.
9 people found this helpful
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The stage is set but where is the lead act?

I strongly believe this is the most important research paper that has come out in recent times. Unlike any other dry and cold papers you may have read, this one is written in very personal and engaging style.

The author points out what is missing in current AI research and why theories such as neural networks are not sufficient to realize truly intelligent systems and then he goes on to establish the need for understanding exactly how brain works, his struggle to assimilate this knowledge and establishing a need for a framework for understanding the intelligence that comes along with biological brain.

I read a lot of original scientific papers - from Einstein's original relativity papers to Maxwell's 3rd paper to Newton's Principia and so on and if all these works were published the way Jeff Hawkins published his On Intelligence, the world would be a different place, with possibly more then half the population knowing what General Relativity is all about :). The most important element of Jeff's writing style, which I believe some of the greatest works in science do not have, is that Jeff walks you through his thought chains rather then just describing and formally justifying the end results of the thought chain. This is exactly the kind of work I was looking for a long time.

On the negative side, many people would raise their eyebrows if I insist calling this book as a research paper because it lacks technical accuracy, structure and overall strength. The author doesn't bother about "mathematising" his framework. This is a HUGE weakness and just that fact alone can cost its formal acceptance in the field. The book solely tries to survive in English description of its founding ideas.

Another weak point of the book is the pages and pages of repetition and redundancy in describing key concepts and that can really get you tired. The book strongly lacks structure which can just make you feel clueless about what you read so far and what to expect next. In my second pass reading, I always keep a pencil with me and underline the key points that author is making so I don't have to read those tiringly repetitive pages again to come to a point. My assertion is that the book could have been written in 10 pages without missing anything important or loosing its informal style. That's another bad thing: this one of the most important paper is available only if you are willing to spend money to buy it as a printed book. Do all these authors forget about the existence of Internet for sharing and collaboration of their works instead of locking it up exclusively inside expensive magazines and books that only academia has an access?

The presentation quality of the book is probably its next biggest weakness. This book could have used popup boxes like those modern technical books, bullet list of facts, many more diagrams, content divided in to logical hierarchical sections, each chapter ending with summery, what to expect next, references, bulleted list of unanswered questions for that section and so on. The readers who really want to research on authors ideas and write prototype have to carry the burden of filtering redundancy and structuring the content.

The book also has companion website where you can find some work from a guy from Stanford, Dileep Geaorge, in formalizing author's ideas and even a working software prototype (however from the first look I felt as if it's merely an extension of Bayesian networks and HMM rather then actually putting author's thoughts in structured form and mathematical grounds). There are obviously many missing links but I've hardly any doubts that the direction that this paper establishes is the right direction.
8 people found this helpful
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Bright and well-grounded

For those who are interested in this field, it is hard finding good books. Many writers are technologist and many of them try to defend positions hard-to-defend (Brooks or Minsky should be good examples). Others are cognitive-psychologist and have bought the merchandise of the first ones about the brain as an information processor (Pinker should be an example of these). Others are essentialists and refuse to discuss the idea of intelligent machines because intelligence is human and that's all. At last, some of them are visionaries like Kurzweil or others.

Luckily, there are writers, coming from technology, philosophy, sociology or whatever, that escape from that classification and it is a real pleasure reading them: Dennett, Searle, Maturana, Varela, Hofstadter, Dreyfuss, Hillis and....Hawkins.

Hawkins has a double background: Technology and Neuroscience. His definitions of intelligence and his explanation about how the brain works and how this knowledge could be used to build intelligent machines is outstanding.

Before reading "On intelligence" and being familiar with the state-of-art in technology, I was convinced that building an intelligent machine was impossible. After reading this book, I am almost convinced that it is possible to build intelligent machines. This is only a matter of time and having people like Hawkins.
5 people found this helpful
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In Quest of Searle's Chinese Room and Real Intelligence

On Intelligence, either you agree with Jeff Hawkins point of view or not, is an interesting or informative read. Jeff promotes the idea that intelligent machines can only be created by understanding and replicating the way human brain works. He argues that current models of artificial neural networks, statistical learning and decision support systems aren't truly intelligent; the vision of intelligence is beyond mere data processing and thinking creative doesn't come from learning models evolved from this thinking. This challenges almost the entire foundation work of Artificial intelligence and depicts a new paradigm for machine learning. I recommend all CS/EE related people to read it.

-Adnan Masood

MSc. MCSD.NET

[...]
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I see a pattern....

Mr. Hawkins will be the first to admit this is not a book to explain everything about intelligence but an important first step. He puts forward a rational explanation of how intelligence in man works in practical mechanical terms. It is a theory but he has much to back-up his conjectures. His theory gets the ball rolling for a frank discussion of why, how, and what we really doing in our mind when we think. He avoids general behavior and philosophical musing about the human mind (that we are all to often inundated with on books about intelligence). I enjoyed every bit of it. At the very least you'll enjoy he very different approach to this age old question.

This book is a really great start to addressing the problem of how we humans think. Good job Mr. Hawkins.
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Interesting and possibly important

This book presents strong arguments that prediction is a more important part of intelligence than most experts realize. It outlines a fairly simple set of general purpose rules that may describe some important aspects of how small groups of neurons interact to produce intelligent behavior. It provides a better theory of the role of the hippocampus than I've seen before.

I wouldn't call this book a major breakthrough, but I expect that it will produce some nontrivial advances in the understanding of the human brain.

The most disturbing part of this book is the section on the risks of AI. He claims that AIs will just be tools, but he shows no sign of having given thought to any of the issues involved beyond deciding that an AI is unlikely to have human motives. But that leaves a wide variety of other possible goals systems, many of which would be as dangerous. It's possible that he sees easy ways to ensure that an AI is always obedient, but there are many approaches to AI for which I don't think this is possible (for instance, evolutionary programming looks like it would select for something resembling a survival instinct), and this book doesn't clarify what goals Hawkins' approach is likely to build into his software. It is easy to imagine that he would need to build in goals other than obedience in order to get his system to do any learning. If this is any indication of the care he is taking to ensure that his "tools" are safe, I hope he fails to produce intelligent software.
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