I read this book when it came out a few weeks ago and enjoyed it, and share a lot of similar criticism as the author of this post. To restate briefly, the book'

I read this book when it came out a few weeks ago and enjoyed it, and share a lot of similar criticism as the author of this post. To restate briefly, the book's main thesis is: - The neocortex is a thin layer of neurons around the old brain. This is the wrinkled outer layer of the brain you think of when you see a picture of a brain. - The neocortex is made of 1MM cortical columns. Cortical columns are clusters of neurons about the size of a grain of rice. They contain a few thousand neurons each. - Cortical columns form a sort of fundamental learning unit of the brain. Each column is learning a model of the world. All cortical columns are running essentially the same algorithm, they are just hooked up to different inputs. - Columns are sparsely connected to other columns. Columns take into account the predictions of other columns when making their own predictions. So the overall brain will tend to converge on a coherent view of the world after enough time steps. - Columns learn to model the world via reference frames. Reference frames are a very general concept that take a while to wrap your head around what Hawkins means. A physical example would be a model of my body from the reference frame of my head. Or a model of my neighborhood from the reference frame of my house. But reference frames can also be non-physical, e.g. a model of economics from a reference frame in supply/demand theory. - Thus, very generally, you can think of the neocortex -- made up of this cortical column circuit -- as a thing that is learning a map of the world. It can answer questions like "if I go north from my house, how long until I encounter a cafe?" and "if I don't mow the lawn today, how will my wife react?". - The old "reptilian" brain uses this map of the world to make us function as humans. Old reptilian brain says "I want food, find me food". New neocortex says "If you walk to the refrigerator, open the door, take out the bread and cheese, put them in the toaster, you will have a nice cheese sandwich". I, like the author of this post, find Hawkins' handwaving of machine intelligence risks unconvincing. Hawkins' basic argument is "the neocortex is just a very fancy map, and maps do not have motivations". I think he neglects the possibility that it might be incredibly simple to add a driver program that uses that map in bad ways. He also rejects the notion of intelligence explosion on the grounds that while a silicon cortical column may be 10000x faster than a biological one, it still has to interact with the physical world to gather data, and it can't do that 10000x faster due to various physical limits. I find this convincing in some fields, but totally dubious in others. I think Hawkins' underestimates the amount of new knowledge that could be derived by a superintelligence doing superhuman correlation of the results of already-performed scientific experiments. It does not seem completely impossible to me that a superintelligence might analyze all of the experiments performed in the particle colliders of the world and generate a "theory of everything" based on the data we have so far. It's possible that we have all of the pieces and just haven't put them together yet. Overall, though, I really enjoyed the book and would recommend it to anyone who is interested in ML.