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In addition to what people clarified in this thread, you probably will be interested in this: Neural network was not a popular research area before 2005. In fact, the AI nuclear winter in the 90s left such a bitter taste that most people thought that NN is a dead end, so much so that Hinton could not even get enough funding for his research. If it were not for Canada's (I forgot the institution's name) miraculous decision to fund Hinton, LeCunn, and Bengio with $10M for 10 years, they probably wouldn't be able to continue their research. I was a CS student in the early 2000s in U of T, a pretty informed one too, yet I did not even know about Hinton's work. At that time, most of the professors who did AI research in U of T were into symbolic reasoning. I still remember I was taking courses like Model Theory and abstract interpretation from one of such professors. Yet Hinton persevered and changed the history.

I don't think Hinton cared about fame as you imagined.




I remember in 2010 a postdoc came to teach a course on model checking and the classroom was just packed with CS students.

I never took it but it will be interesting to see what kind of synthesis between traditional logic and neural network paradigms can be achieved.


"it will be interesting to see what kind of synthesis between traditional logic and neural network paradigms can be achieved"

Ben Goertzel talks about his work on something like this at around the 16 minute mark in this video:

https://m.youtube.com/watch?v=MVWzwIg4Adw




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