An enjoyable review of correlates of intelligence and their architectural and computational requirements. Despite the book being published now almost 20 years ago this approach remains important. To look at human intelligence characteristics translated to computation systems characteristics. ( )

This book includes many detailed descriptions of the author's hypotheses on how the brain functions, with insufficient diagrams to make it easy to follow. ( )

Jeff Hawkins has a knack for abstracting and interpreting the structure of our brain. His ideas are very thought provoking mostly because he makes it all seem so simple (even if he did not intend it to be so). I don't want to say he is wrong and the optimist in me even hopes he is right since a simple solution is usually nicer. ( )

Author is one of the top people in consumer tech (created Palm Pilot), and is deeply interested in AI. He does a pretty good job of presenting a few elements of the field (neural networks, primarily, and that prediction is the most key activity in the neocortex) to a general audience, and then includes some of his own theories and predictions (which is tricky because it's hard for a non-expert to know which parts are broadly accepted and which are his own theories...). Overall, a very interesting book, and since it's nearly 20 years old, it's interesting to see which of his predictions were accurate (things took about 10-20% longer than he predicted, I think, but were much more successful than he predicted); always neat when someone's errors in "wild predictions" are that they were too conservative in some way.

I honestly don't know anywhere near enough about neuroscience to really evaluate that portion or his presentation, but the more general information/cs part was pretty solid.

Most interesting thing to me was the theory that the neocortex evolved to make predictions better in animals, which is a great way to have it merge with the sensory and motor control parts of lower elements of the brain, and provides an incremental and continuous benefit from even slight levels of new capability all the way up to what we have in humans today. ( )

Very insightful and hard to believe this book was published more than 16 years ago in 2004. It brings a fresh perspective on the current debate about the dangers of AI and how likely we are to make machines that are super-intelligent. It does a great job of demystifying human intelligence and explaining why it has been so hard to replicate in computers. I suspect it will turn out to be quite prescient and one of the more important books on the subject. I was particularly impressed with the author's personal journey vis-a-vis his interest in brains and how he was willing to challenge conventional wisdom and the way the AI industry approached the problem. My only gripe is that after introducing the reader to the problem of invariant representation as one of the greatest scientific mysteries of our time, the author didn't dwell much on the nature of the problem, why it is so difficult and how over the coming years or decades it may be solved.
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