I also read On Intelligence and it had a large impact on my reading habits. I was not previously aware that Andrew Ng had a similar experience, which leads me to wonder how many people became interested in neuroscience as a result of that one book.
On a side note: the only significance of Andrew Ng's stated belief that AGI is far is as an indicator that he doesn't see a route to get there in the near term. On a related note, he gave a kind of wierd comment recently at the end of a conference talk to the effect of "Worrying about the dangers of machine superintelligence today is like worrying about overpopulation on Mars."
In one sense, the "one learning algorithm" hypothesis should not seem very surprising. In the fields of AI/machine learning, essentially all practical learning algorithms can be viewed as some approximation of general Bayesian inference (yes - this includes stochastic gradient descent). Given a utility function and a powerful inference system, defining a strong intelligent agent is straightforward (general reinforcement learning, AIXI, etc.)
The difficulty of course is in scaling up practical inference algorithms to compete with the brain. One of the older views in neuroscience was that the brain employed a huge number of specialized algorithms that have been fine tuned in deep time by evolution - specialized vision modules, audio modules, motor, language, etc etc. The novelty of the one learning hypothesis is the realization that all of that specialization is not hardwired, but instead is the lifetime accumulated result of a much simpler general learning algorithm.
On Intelligence is a well written pop sci book about a very important new development in neuroscience. However, Hawkin's particular implementation of the general ideas - his HTM stuff - is neither groundbreaking, theoretically promising, nor very effective. There are dozens of unsupervised generative model frameworks that are more powerful in theory and in practice (as one example, look into any of Bengio's recent work), and HTM itself has had little impact on machine learning.
I wonder also about Hassibis (founder of DeepMind) - who studied computational neuroscience and then started a deep learning company - did he read On Intelligence? Regardless, you can see the flow of influence in how deep learning papers cite neuroscience.
Related: Even When Contrarians Win, They Lose
I had long thought that Jeff Hawkins (and the Redwood Center, and Numentia) were pursuing an idea that didn't work, and were continuing to fail to give up for a prolonged period of time. I formed this belief because I had not heard of any impressive results or endorsements of their research. However, I recently read an interview with Andrew Ng, a leading machine learning researcher, in which he credits Jeff Hawkins with publicizing the "one learning algorithm" hypothesis - the idea that most of the cognitive work of the brain is done by one algorithm. Ng says that, as a young researcher, this pushed him into areas that could lead to general AI. He still believes that AGI is far though.
I found out about Hawkins' influence on Ng after reading an old SL4 post by Eliezer and looking for further information about Jeff Hawkins. It seems that the "one learning algorithm" hypothesis was widely known in neuroscience, but not within AI until Hawkins' work. Based on Eliezer's citation of Mountcastle and his known familiarity with cognitive science, it seems that he learned of this hypothesis independently of Hawkins. The "one learning algorithm" hypothesis is important in the context of intelligence explosion forecasting, since hard takeoff is vastly more likely if it is true. I have been told that further evidence for this hypothesis has been found recently, but I don't know the details.
This all fits well with Robin Hanson's model. Hawkins had good evidence that better machine learning should be possible, but the particular approaches that he took didn't perform as well as less biologically-inspired ones, so he's not really recognized today. Deep learning would definitely have happened without him; there were already many people working in the field, and they started to attract attention because of improved performance due to a few tricks and better hardware. At least Ng's career though can be credited to Hawkins.
I've been thinking about Robin's hypothesis a lot recently, since many researchers in AI are starting to think about the impacts of their work (most still only think about the near-term societal impacts rather than thinking about superintelligence though). They recognize that this shift towards thinking about societal impacts is recent, but they have no idea why it is occurring. They know that many people, such as Elon Musk, have been outspoken about AI safety in the media recently, but few have heard of Superintelligence, or attribute the recent change to FHI or MIRI.