This is very easy to read book to Jeffs ideas on AI.

Jeff does come across as a bit pompous and self assured at times and I am not so sure about the originality of his ideas as he is. He keeps claiming that others in the field have come up against dead ends because they did not understand the full implications of feed back in neural networks. Yet before I read the book I thought they worked as he broadly describes, and I had picked that knowledge up over the years by reading various bits and pieces. It makes me wonder if he is building a bit of a strawman to make his own position stronger.

Regardless it is a very interesting book. If I had the time I would get stuck into the Numenta software that his company has made and is now available. ( )

For the detailed, nit-picking review : http://www.perichoresis.com/onintelli...

In brief - lots of good stuff to chew on about what makes us smart, marred by Hawkins being a sloppy philosopher. ( )

This comprehensive theory of intelligence is easily one of the best popular science books I've ever read. If I were just starting college, reading this book would have radically changed my career goals. It may even now. ( )

I liked the fairly straightforward description and explanation of the premise - that intelligence comes from a memory-prediction architecture that is hierarchical. However, in trying to speak to a general, mainstream audience, I think the author may have aimed too low . It seemed to me that a lot of the examples were overly simple and tiresome, in some cases. At times, it also felt like it was more of a common-sense persuasive argument than hard-core science. All in all, though, it was a quick read, and it motivated me to look for more detailed accounts elsewhere. ( )

Fairly easy read but still contains some deep ideas about the mind. He argues that the mind is not, as popularly believed, a giant computer, but rather a huge memory system. Specifically an “auto-associative” memory that is able to retrieve complete memories based on only partial memories given as input, much like we can recall entire songs given only the first few notes. This in itself is not a new idea, but the way the author ties it all together is new. He avoids too much detail and neuroscience jargon so it is very accessible to the general science reader. Chapter 6 on how the cortex works is the most difficult section and occupies about a quarter of the book. By difficult I mean it will take some concentration and a fairly close reading in order to not get lost. The authors do a splendid job explaining a complicated topic and I found the book a very enjoyable read.

Here is a quote to give you a feeling for the writing:

"I realized that if someone had invented the concept of a computer with a graphical user interface and a spreadsheet application, and presented it to me on paper, I would have rejected it as impractical. I would have said it would take forever to do anything. It was a humbling thought because it did work. It was then that I realized my intuitive sense for the speed of the microprocessor and my intuitive sense for the power of hierarchical design were inadequate. There is a lesson here about the neocortex. It isn't made of superfast components and the rules under which it operates are not that complex. However, it does have a hierarchical structure that contains billions of neurons and trillions of synapses. If we find it hard to imagine how such a logically simple but numerically vast memory system can create our consciousness, our languages, our cultures, our art, this book, and our science and technology, I suggest it is because our intuitive sense of the capacity of the cortex and the power of its hierarchical structure is inadequate. The neocortex does work. It isn't magic. We can understand it. And like a computer, ultimately we can build intelligent machines that work on the same principles". (pg. 175) ( )