[Fomite]

"The simple answer is: funding. There is no (public) money to revisit "known" ideas and results to help clarify them, apply them, or place them in better context. And I would not be the one to explain to the public to spend their money on known ideas rather than discover something new and shiny."

Which is a pity. There's a prominent member of my field who does this for causal inference work, and it has been invaluable.

[tomcat27]

The problem at heart is research pace. Slowing the pace should help. i.e., it should be ok for faculty to produce half the papers they now produce and still retain their hopes on tenure, or whatever grants and glory.

I cannot get creative under pressure. I don't know how anyone can.

There is insurmountable amount of papers produced every year in AI, and everyone's in a rat race for some reason I don't understand. There is no way someone can exist in a department for more than a year without pushing a paper.

At least in AI, the courses students are taught, professors have an understanding are out of touch with the kind of mathematical rigor needed.

[bmitc]

I would guess that the rat race in that case is probably driven by the ability of the machine learning industry to pull people from academia. So the rat race is a literal one, with people trying to get hired by Google, Meta, and the like.

In general though, I am with you. I don’t understand why people are in such a hurry all the time these days. I have a personal principle that I move slow to move fast. Moving slow and methodically is almost always faster in the end.