On Intelligence,   ISBN:9780805078534

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After listening to a podcast interview with Jeff Hawkins, I picked up this book because although I'm by no stretch of the imagination an AI expert, Hawkins' arguments regarding the failures of AI research over the decades made sense, and so I thought it would be interesting to learn more about his particular take on the topic. Hawkins certainly delivers in that regards, offering a theoretical framework for his conclusion that the brain is essentially a highly organized prediction machine which manages to outperform even the most powerful of today's computers despite the brain being woefully slow comparatively.

To be clear, this is not a book about "artificial intelligence", but rather focuses on how the human brain operates. The authors devote a mere 30 pages (Chapter 8: The Future of Intelligence) to a specific discussion of how Hawkins' theory might apply to technology development. Do not construe this as being a shortcoming, because Hawkins' intent (as I understand it) is to right his perceived listing of the AI ship by first rethinking the concept of intelligence before applying these theories to silicon.

If you've any interest in the science behind what may one day make the machines of "I, Robot" a reality, consider reading this book.

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Jeff does an excellent job of helping you to visualize how his theories of the brain work in this book. What he presents is convincing and I am excited to see how his framework evolves. I look forward to applying his theories in experimenting with intelligent programs. I felt that Jeff delivered this information in just the right way to convey the concepts, nice work.

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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.

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This review will attempt to summarize the book and also provides my opinion on Jeff Hawkins's memory-prediction model of intelligence and his application to other human intelligence aspect. Hawkins writes up a fairly decent account on intelligence in a conceptual sense. Though he never gets into the biological details, Hawkins delivers an interesting framework and definition of intelligence. I believe this book to be instructional and informative for those interested in topics of brain function and human and machine intelligence.

Hawkins proposes that intelligence is the result of the relatively newly evolved cortex and its ability to detect, memorize, and predict patterns. The author provides a brief history of artificial intelligence and neural networks to familiarize the readers with the current approaches to creating intelligent machines. From that, Hawkins switches to the human brain and explains the basic details the cortex structure: its layered structure, a short description of cells in the cortex, and a description of hierarchy in the brain. With the structures established, Hawkins explains three of the four attributes he gives to memory; sequence of pattern storage, auto-associative recollection, and invariant memories. The basics explained, Hawkins lays out his new intelligence framework based on prediction. Thought experiments are used to show the pervasiveness of prediction and evolutionary comparisons to explain the significance of the cortex to intelligence. The methods of operation are explained in the next chapter, and Hawkins delves into the details on how the cortex uses hierarchy, invariant memories, and sequences to make higher-order predictions. The final portions of the book deal with the more abstract portions of human intelligence, creativity, consciousness, imagination, etc., and Hawkins explains all of these aspects within his framework. The last section is Hawkins explaining the utility and feasibility as well as allaying some of the fears of intelligent machines.

Hawkins's explanations of cortex function and structure are done, from a scientific point, rather simply. He never delves into the biological processes nor the physiological reasoning behind brain activity. Rather, Hawkins uses concepts, simple analogies, and though experiments in order to explain his framework or cortex function. His overall writing style is engaging and is capable of keeping readers going so long as a spark of interest is retained but also is not daunting nor overly complicated.

Hawkins has an interesting approach to explaining his memory-prediction framework. He provides us with his experience and guides us to how he came about his framework. This framework, itself, is rather interesting as well. To think that all inputs to eyes, nose, ears, or whatever sensory organ is constantly changing is counter intuitive to what we perceive. And yet, experiments exist that prove this is true; eyes are constantly performing additional movements (saccades) that should distort or blur our vision. On top of all that, the structure of eyes causes a permanent blind spot that is never detected. But when I look at my computer it is stable and clear; there are no gaps in its form, there is no blur from eye movement. Hawkins's idea that the brain makes predictions to fill in or assume the gaps is not new. However, he extends it say that the prediction mechanism is the basis of intelligence, that our ability to predict at higher levels than other animals is what gives us our intelligence. And this seems to be true, given how our examinations are done. I believe Hawkins is correct with this memory-prediction model of intelligence.

Now the method that Hawkins uses to tackle the idea of creativity seems very simplistic. He claims that creativity is when you make new associations between memories or change the outcome of a predictable pattern. But this actually aligns with a definition of creativity, new associations between existing ideas, and a common idea of creative works. The artist that adds in that extra feature to a portrait breaks our prediction of what a face looks like but its still a face and if done correctly we would acknowledge the painting as creative. And that small detail of 'correctness' is very important and something that Hawkins glosses over. For the purposes of his book, an introduction to a new definition of intelligence, it is not necessary to explain the details of everything. However, in an elaboration of creativity, I would like to know why we find certain types of prediction-changes to be creative and others to not be. If it were true that breaking predictions leads to creativity then a computer program that changes a handful of pixels in an established painting should lead to a creative work. And given enough iterations, such a creative work should arise and individuals would detect the creativity differently based on their learned patterns and memories. But a vast majority of those changes would not be creative, we would view them as simply changes, mistakes or possible insults to the original. Maybe it is simply my human ego thinking creativity cannot be this simple, I can not help but think that though it aligns with the definition, creativity is much more than pattern-breaking and more details need to be supplied to accurately paint what creativity is.

Overall I feel that Hawkins provides a convincing definition of intelligence, one that I hope would lead to some interesting technologies. For anyone interested in brain function and computer programmed intelligence this book may be able to answer some important questions. For anyone that simply wants to learn some more about human intelligence this a good book, the writing style is easy to pick up and though one section may be a bit technical its nothing like reading a scientific journal article. I highly suggest this to anyone interested, but like all neuroscience topics remember to read with a bit skepticism.

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"Prediction is not just one of the things your brain does. It is the primary function of the neocortex, and the foundation of intelligence." (p. 89)

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

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

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

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

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

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

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

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

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

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

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

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

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

I would like to point out that Hawkins' idea that our cortex is continually making predictions about the environment, predictions that we scarcely notice unless they are wrong, is similar to an idea that John McCrone presented in his book Going Inside: A Tour Round a Single Moment of Consciousness (2001), a book I also highly recommend.