On Intelligence |
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PRODUCT DESCRIPTION
Binding: Hardcover
Format: Bargain Price
Number Of Items: 1
Number Of Pages: 272
Publication Date: 2004-10-03
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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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Summary: Thought-provoking.
Comment: Ultimately, Hawkins conveys that the ideas which make up his brain theory are not completely set in stone; instead, he feels they are a conceptual start in understanding how the brain can perform some of the tasks that seem very complex and tasks that seem mundane. He describes this as a top-down approach which helps brings structure and progress to a field (neuroscience) which is largely dealing with scattered, abundant, and often conflicting pieces of data. In this way, the data can be sorted in a relevant way which allows more knowledge to be gleaned about the brain's functionality and so that the theory may be refined over time. Imagine the vast astronomical data gathered by Tycho Brahe that was quite confounding until a valid theory of planetary motion was in place to make sense of it all.
At the same time, Hawkins underlines the importance that the brain is probably not as complex as we make it out to be, and if we rid ourselves of the notion that there is a secret layer of "magic sauce" to our consciousness and the fundamental ways our brain operates, we could get around many of the barriers that have been keeping conventional computers and artificial intelligence techniques from approaching some of the simpler feats of the human mind.
Hawkins leads us in a discussion of how artificial intelligence and machine learning pursuits have had hopeful, yet ephemeral, successes. What once promised to be an ever-expanding field of study has stagnated to mindless, specialized robotics. The problem is outlined as a fundamental difference in the way computers and brains operate. Until this difference is addressed, Hawkins believes that artificial intelligence approaches will continue to fail.
So how does the brain really work, and what has been missing in our previous models? Both the clue and answer to this question, as Hawkins demonstrates through a variety of thought experiments, is prediction. After a near bombardment of these experiments, many of which may be tested easily in the comfort of your own home, it is almost easy to begin seeing your own brain as Hawkins describes -- devoid of any "magical sauce". Previous models were ignoring the feedback within the hierarchy of the brain.
Amazingly, feedback has been assumed negligible in brain models when Hawkins points out that the brain is dominantly wired to maintain this operation. It is easy to think of the brain like one would a computer: Given some input, a variety of uni-directional manipulations are made to produce some output. This is incorrect according to Hawkins, and actually takes away entirely from the function of the brain.
After bringing feedback to the forefront of his argument, it seems almost stupid to assume the brain, in all it's abilities,would operate without this kind of feedback. To make predictions (be intelligent), one must simultaneously feed sensory inputs of what is happening against what has already happened. If some threshold of error is reached between cross-validating current experience with past experience, this triggers a "surprise" in our brain which sends the unexpected pattern through our hierarchy until it can be appropriately classified or saved for later reference. This method of compartmentalization is extremely fascinating and believable in terms of recreating the kind of behavior we see in humans, if not entirely accurate in the way our brains actually work. Keep in mind, that my description is a gross oversimplification of Hawkins' explanation. I'll leave the technicalities to Hawkins in his chapter on how the neocortex must function based on the concepts of his theory.
Though many of Hawkins' ideas may have occurred to you before, the sequence in which he presents them simultaneously emphasizes the beauty and simplicity of the seemingly mundane tasks of day-to-day living. Many times, Hawkins calls on the authority of Vernon Mountcastle, a modern neuroscientist, who asserted the uniformity of basic function in the brain through the uniformity of its structure. That is, though certain regions of the brain seem to be devoted to certain types of sensory activity, the flexibility of perception reaches far beyond this assumption. This has been demonstrated in experiments on blind and deaf people who have re-devoted regions of the brain in which they are impaired to alternate senses. There are a plethora of examples in the book like this, and Mountcastle's assertion has implications beyond general sensory perception.
The final chapters of "On Intelligence" attempt to answer some of the more controversial questions tied to many of Hawkins' claims. Hawkins does a decent job of sorting through metaphysical and other presuppositions that have hindered progress in the past. While not completely demeaning some of the more sensitive questions, Hawkins suggests that by supposing our brains are mysterious and incomprehensible, we are disabling ourselves from discovering an otherwise profound truth. And while it may not be a satisfactory answer to some, his theory itself actually simplifies the answer to some of these thought-provoking questions -- which I find appealing.
However, I warn against buying into a theory simply because its concepts inherently counter-argue, ignore, or nullify opposing views. This could mean that the theory is sound; nevertheless, it is equally likely that the concepts are built around unknowns for the sake of placating uncertainty, similar to religious explanations. Still, when observing Hawkins' stance, it is clear that he is not satisfied with the whole of his answers as they stand and fully expects many of his ideas to be revised, if not disproven.
Hawkins has devised a framework to guide a new understanding of the brain. Based on what we know, the brain must be able to "do" certain things because of our own abilities. The details of "how" may be irrelevant. We are reminded by Hawkins of the wheel. The wheel has the same function as legs in nature, yet it is actually more efficient in many cases. Similarly, an intelligent machine could have the same function as the brain, but it may operate in a completely different manner.
I must also note that negative reviews on this book seem to stem from Hawkin's negative views on artificial intelligence, and - in particular - neural networks. Hawkins actually defends the concepts of more advanced neural networks that incorporate feedback in order to explain what he supposes actually goes on in the brain (just in an extremely simplified manner).
Hawkins is essentially handing those in AI and Machine Learning a dead end because, at the root of this book, he is saying typical microprocessor computers and software can't solve intelligence problems by their very nature (google: "Chinese Room"). This may leave those in the field with a bitter taste in their mouth. Certainly, there are plenty of advances to be made in AI and machine learning, but that is not Hawkins' concern. He presents adequate information to show that AI and machine learning techniques will converge to a limit and are not "truly" intelligent; whereas, a system built on his theory has potential to be expanded beyond human intelligence and reshape what we define as intelligence altogether.
I definitely recommend this book to anybody interested in computer science, neuroscience, or both. Because of many references to computer science and neuroscience, I am unsure if I should recommend this to a general audience, but Hawkins does an excellent job of providing hypothetical situations and analogies to his ideas that make them easy to understand. The concepts get a little muddy when he delves into the details of cortical layers, hierarchy, and the connections between them. That is to be expected from his statements early on that his theory is a work in progress, but the overall message of the book is resilient.
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Summary: A chimerical project based on an extremely interesting interpretation of brain functioning
Comment: During the past half-millennium the history of anatomy documents the peculiar custom of using the most advanced technology of each era as the definite model of the human brain. The first match was with clockworks during the sixteenth century; then with the steam engine, in the nineteenth century; one hundred years later with telephone switchboards in the first half of the twentieth century, and in the recent decades, naturally and expectedly, with electronic computers. However sound they might have appeared at each time, all these comparisons proved inadequate after a while. All have fallen short when matching up manmade machines with the extraordinary prodigy of the human organ that designed them.
Twenty years ago Jeff Hawkins, the architect of many technologies and a successful Silicon Valley entrepreneur, decided to turn the metaphor all the way around and walk it in the opposite direction. Instead of starting from already invented equipment to develop explanatory models, Hawkins decided to first understand the way the brain operates--more specifically, how the cerebral cortex works--and design from there on a new technology. With such a challenge in mind, after studying neurology on his own and co-working with many scientists, the ambitious businessman initiates a monumental (if not chimerical) project to design and build electronic equipment that is to operate similarly to the human brain. Numenta, a company founded by Hawkins in 2005, has the mission to make this initiative a reality. His book ON INTELLIGENCE, written with science journalist Sandra Blakeslee, describes the reasoning behind his adventure, the factors that support the idea, the obstacles that make it extremely complex and the scientific developments that will contribute to its realization.
There is only one chapter in the book complex and difficult to read (the author warns about this) that presents his view of a detailed model of the functioning of the cerebral cortex, the thin layer of thirty billion neurons that surrounds the brain. Even with this exception, ON INTELLIGENCE is an entertaining and educational book. The description of the four attributes of the cerebral cortex that make it radically different from electronic computers is fascinating. The first attribute is the storage of sequences of patterns (instead of isolated data interrelated by data models and database software) that enables the recording and recalling of stories or sequences. The second is the ability to pick the full story or sequence from only a fraction of any part of whole without the need to access the complete pattern (we recognize a song by just listening a bit of it). The third is the conservation of the essence of every pattern although the rest of the information might be variable (this is why we recognize incomplete objects or identify people we have not seen in years despite changes of age, contexture or makeup). The fourth, the difficult-to read chapter of the book, is the storage of the patterns in a hierarchical structure.
These attributes provide the cerebral cortex an intellectual capacity quite different from those put forward in previous interpretations. According to Hawkins the cortex is an organ of prediction; predicting is the main function of the human brain and this capability is the very foundation of intelligence. The neurons involved in any activity (or some associated neurons yet to be discovered) are activated prior to the arrival of the corresponding sensory signals, be they visual, auditory or tactile, anticipating the coming events from some sort of extrapolation of all the patterns that the cortex has already in its memory. For example, when someone enters a restaurant where he never has been, he can "predict" with a good degree of certainty in what direction are the bathrooms. When the event is completed, if the result matches expectations (this happens most of the time), the owner of the brain does not even realize that a verification transaction was performed. If, on the contrary, expectations do not coincide with reality, there is a surprising reaction, followed by corrections and learning lessons that eventually lead to the creation of new patterns.
In Hawkins's perspective, the human brain is an organ that builds models based on patterns and analogies and generates with them creative predictions. When it does not find correlations, the brain invents them anyway with minimum consideration on how preposterous they may turn out. Pseudoscience, prejudices, intolerance and religions are the result of these inventions.
The concept of prediction that Hawkins developed in 1986--we should remember that he did not graduate in neurology--was later confirmed in independent scientific studies. For example, Rodolfo Llinás, a neuroscientist at the New York University School of Medicine establishes in 2001: "The capacity to predict the outcome of future events--critical to successful movement--is, most likely, the ultimate and most common of all global brain functions."
I believe the development of truly intelligent machines is an unfeasible project. Its endeavor, nevertheless, will lead to many new scientific discoveries. The brilliant entrepreneur recognizes that his target is neither the invention of an electronic model of human consciousness nor the production of machines that arrogantly say "I." His main interests aim at the development of computers with vision, the design of thinking robots and the construction of machines with capacity to learn. The invitation to the greed of the young generations to join in some way the great idea is outside the context and beauty of the whole project. Contributing to human growth or making a difference--not plain utilitarianism--should be the driving forces of scientific research. Still, from my perspective of cognitive science enthusiast, I consider that the very description of the functioning cerebral cortex (I suppose that a few neuroscientists may disagree with it) and the concept of prediction as the fundament of human Intelligence far deserve the reading of this excellent book.
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Summary: Bigger, Faster, Stronger
Comment: All of human intelligence exists in a 2ml. six sheet layered region of the brain called the neo-cortex as large as a dinner napkin when laid out side to side. This region processes information regardless of sensory form, and is present in all mammals to varying sizes; most of us are lucky to have the largest among all mammals. All incoming sensory information from sight, smell, touch, sound etc. is processed using the same algorithm. Contrary to what most scientists believe, there are no modules specializing in specific tasks such as language. Any part of the neo-cortex is equipped and flexible enough to manage any task, and all parts of this region of the brain process information based on pattern/time recognition. Mr. Hawkins believes throughout the learning process, certain areas of the neo-cortex will specialize to particular tasks, but this specialization is not pre-designed.
The neo-cortex does not operate like a computer; instead of computing exhaustively until a solution is found, the neo-cortex utilizes stored invariant representations of actual events in memory to predict solutions, and continuously compares results to predictions to validate. "Prediction", as Mr. Hawkins asserts, "is the primary function of the neo-cortex, and the foundation of intelligence.... Intelligence is measure by the capacity to remember and predict patterns in the world." Bobby Fischer, one of the greatest chess players of all time had an incredibly retentive memory, able to recall most of his speed chess games move by move. It's not surprising, therefore, to expect Fischer to have an IQ score in excess of 180 as his school records indicated (memory + predictive power from pattern recognition = intelligence).
Mr. Hawkins asserts the inner workings of the neo-cortex is not magic. We can understand it, and ultimately build intelligent machines that work on the same principles. Intelligent machines can only be built utilizing this memory-prediction framework of the neo-cortex with a hierarchial design (different layers of the neo-cortex manage differing levels of information complexity).
Artificial Ingelligence (AI) and Neural Networks (NN), the first two forays into building intelligent machines have failed so far because they focus on behavior and outcomes. Mr. Hawkins believes behavior is a manifestation of intelligence, not intelligence itself. An intelligent being can possess intelligence without exhibiting any kind of behavior. AI researchers believe the only impediment to their effort of devising intelligent machines is lack of processing power. Hawkins is highly critical of these two fields for ignoring the biological aspect of intelligence.
In the latter part of the book, Mr. Hawkins states fear of intelligent machines taking over the world is unfounded because machines will lack desire, ambition, the pursuit of social status and wealth. But it doesn't take much for this doomsday scenario to materialize. It can happen with one well networked intelligent machine that develops ambition or the resentment of slavery to humans. If intelligent machines surpass the power of the human brain some day, which no one doubts they will, then they will also exceed the ability of humans to dominate their environment. Even a malicious electronic virus, most likely the creation of some miscreant is sufficient to generate the ambition necessary to eliminate all conceivable obstacles to dominance.
Mr. Hawkins also neglected to mention the prospect of merging biology and machine. A future man-made intelligent entity may turn out to be part human, part machine. Brain cells, axons, neurons etc. may be reproducible in petri-dishes with minor tweaks and additions of devices to optimize algorithms and neuron connections to magnify the potential of the brain multi-fold.
"On Intelligence" is a readable and novel publication. Chapter 6 was the only part of the book with technical material not well suited for the lay person, and skipping over the entire chapter doesn't take away from understanding the rest of the concepts presented. The more advanced material was thankfully included in the Appendix section only.
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Summary: Great Book!
Comment: This is a great book. I think the ideas here will change the world!
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Summary: Interesting, albeit silently deterministic point of view
Comment: Excellent philosophy on the intelligence algorithm in mammals, neatly condensed into a computer-programmable structure.
My primary gripe is that the author blindly, almost pretentiously, assumes that all intelligence comes from within the brain, and that everything is contained and deterministic. I don't blame him, but he could have spent at least a sentence acknowledging the possibility of self-determinism, rather than poorly debunking it. I think there is still potential for a continuum of self-determinsim, regressing to this algorithm when fully or partially absent.
If nothing else, however, this book describes a good algorithm for the projection of true intelligence on the mechanical universe perceived by the author. Now, go make some smart robots!
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