I don't know. On the one hand, at the conceptual level, unless you are at the cutting edge of CS theory, I'm pretty sure almost anything else that is done in c

I don't know. On the one hand, at the conceptual level, unless you are at the cutting edge of CS theory, I'm pretty sure almost anything else that is done in computer science is a mere re-wording of something that was done in the 1970-1980s. So there is no "holier than thou" at this level. On the other hand, in terms of practical results in context, there are many important consequences of being able to take old concepts and run them faster, because the hardware has improved and well, generally, the entire world is different. A big part of "popularizing" a technique is having a good implementation that takes advantage of advances in computing speed. So the author of the article misses the practical value of popularizing. At the same time, what he says is valuable because he touches on a fundamental choice that we all make: do you want to be a groundwork layer or a popularizer? The problem of course is that groundwork layers are mostly forgotten, with their contributions recognized posthumously, as that's how far out you have to be to lay any new groundwork, and it's difficult to predict what will be the foundation for the next hundreds of years. It's not just deep learning, it's basically that anything that becomes popular enough to be noticed here probably has a long history behind it, and if we are to move forward we need to be in the headspace of those who had the sense back then to form it, and not be in the space of popularizing or being the tool of the popularizer.