timemachiner

Fashion statements come and go in academia. There are academics doing good research in unfashionable areas and doing just fine. Plus, it's hard to see where the latest trends will go. Who thought Software Defined Networks would be a thing in the 1980s?

> Once they're on the freeway, I would think most automation would work (relatively) well.

That's assuming nice weather conditions. Mudslides, flash floods, extreme code, ice, snow, etc. needs to be taken into consideration.

To be extremely rigorous one would have to write formal proofs, that includes all steps. This is a type of proof that could be verified by a computer and may be tedious and very hard for a human to understand. Mathematicians tend to prove things informally that is easy for others to read and understand. It's almost akin to writing programs in machine code vs writing programs in a high level programming language.

I agree, hence I wrote "Universities are not trade schools (nor should they be)"

This makes me wonder. What is the "best" way to teach computer science to students? Universities are not trade schools (nor should they be), but it seems apparent that CS graduates in general are unprepared entering the workforce. The other extreme (bootcamps) seem to produce graduates that are more "industry ready" but only at a superficial level. These graduates seem to lack rigor/theory. Makes me wonder if there is a more optimal training path for training students.

Since it seems to be a thing to report that X person specializing in machine learning has moved from Y company to Z, it makes me wonder if other areas of computer science is seen as relevant by the general public.

One rarely hears Dr. John Doe from Florida State University (or insert non-Standford university here) in Distributed systems has moved from Microsoft Research to NetApp. These are arbitrary names. The point is you rarely hear about people from other areas of CS outside of machine learning/universities outside of Stanford moving from one company to another. The field of CS is vast and there are multitude of practical and theoretical problems outside of machine learning that are worth looking into (ones that aren't currently considered hip or cool by the public).

That's Apples problem. Hopefully Apple gets the message that consumers want actual pro laptops. What I'm suggesting is let's discuss something more important on HN at this point.