I have some experience with both, but can't give a great comparison. The tldr is I've always had a better impression of Hinton than Hawkins, and have studied/fo

I have some experience with both, but can't give a great comparison. The tldr is I've always had a better impression of Hinton than Hawkins, and have studied/followed Hinton's approaches much more carefully. In late 2006/early 2007 I was working a lot with standard two layer feed-forward neural networks (first for my research and then for my job). Hinton had a great paper on practical deep networks at NIPS 2006 (a big AI/machine learning conference), which sparked my interest in more complex neural networks. I had read Hawkins' book a few years earlier, and my impression of it was somewhat negative; I thought it was a really interesting book, but it was too fluffy and high-level to be intriguing. He hit a lot of points about hierarchies in intelligence that were intriguing but not new or drastically insightful. After NIPS I downloaded some of Numenta's code (numenta is Hawkins' company) and it was pretty slow on toy problems so I didn't spend too much time with it - this isn't a judgement of their code, I just didn't have the time to dig deeply into it. My impression at the time, which may be unfair, is that Numenta's approach was ad-hoc while Hinton's was principled. I was negatively biased by Hawkins' book and my professors' opinions of him vs Hinton.