> I think dismissing an entire branch of research simply because it goes against one's argument is a little funny I don't think it goes against my argument at

> I think dismissing an entire branch of research simply because it goes against one's argument is a little funny I don't think it goes against my argument at all. Deep learning is a place where many structural factors are currently optimized, so it has a glut of prominent researchers. The big names in deep learning like Hinton, LeCunn, Bengio took their first post PhD appointments 30 to 40 years ago. All three put out some good work between 1985 and 2010, but all three became an order of magnitude more productive after 2010. They went from being members of a mostly irrelevant and overlooked research niche (neural networks were considered a dead end in ML) to being heads of research at multi-billion dollar companies. If it is really all about the individual, why weren't they that productive from the start? What changed? Well, the number of GPU cores hit an inflection point. Technologies for GP-GPU programming like cuda reached maturity. As I said, there is truth to what Hamming said too. The 1920s were primed for a revolution in physics, but there were thousands of physicists contemporary to Einstein, de Broglie, Heisenberg, and Bohr who didn't do anything of note. So what set the ones who won Nobel prizes apart from the rest? That's where Hamming's advice comes in. In other words, I can be convinced that Einstein or de Broglie would have reached the tops of their fields in any time or place and in nearly any field. But you cannot convince me that they would have made the history books if they were marine biologists in the 1990s. It doesn't matter how hard you think about where the next revolution in your field will be if there is no revolution to be had.