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Comment by zenithd | Hacker News Reader

> math from 65 years ago

Calculus and linear algebra are a tad older than that.

> isn't relevant today as determined by the community itself?

Which community? NeurIPS/ICML/ICLR/et al.? That's only a subset of the AI community.

Also, I'm not really sure that "pure ML/DL" is even a particularly in-demand field of research expertise right now; we've been absolutely saturated with supply for at least junior phd students in that subfield for close to an academic generation by now... whereas there is now a genuine drought of folks who speak "both languages" in AI. I think these days you really need a second field in order to not be in a horrendously bleak over-saturated labor market. E.g., systems and ML? God, please yes. "Symbolic AI" tradition and ML/DL? Yup, lots going on there right now. But pure NeurIPS et al. style work? You've gotta the best in a very, very, very, very crowded room.

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That's right. Starting today as a PhD student in deep learning is career suicide, even if it may look like this is the thing to do. The number of papers put on arxiv each month must number in the thousands and the top machine learning conferences are so awfully crowded it's impossible to get a paper through.

From my point of view and much like you say, the interesting, groundbreaking work has moved outside strict deep learning research. I mean, I sure would think so, but here's the website of the International Joint Conference on Learning and Reasoning, that brings together a bunch of disparate neurosymbolic and symbolic machine learning communities for the first time:

http://lr2020.iit.demokritos.gr/

This is an active field of research with plenty of space for new entrants and full of intersting problems to solve and virgin territory to be the first to explore. I'm hoping we'll soon see an influx of eager and knowledgeable new graduates disappointed with the state of machine learning research and willing to do the real hard work that needs to be done for progress to begin again.

>Starting today as a PhD student in deep learning is career suicide

lol maybe if you want to go into academia but, speaking from experience, FAANG is paying handsomely for this skillset.

It's the same thing in FAANGs. The competition for the over-inflated salaries they offer is such that it's a matter of chance whether someone gets hired or not. You might as well invest your money at the blackjack table in your local casino.

Edit: as a personal recommendation, try not to start your comments with "lol". It makes the comment sound shallow and detracts from your point.

Having gone through the process and coaching a friend though it, it's as much of a crapshoot as studying for any standardized test (which is to say not at all). The things on the test are standard grad school ML stuff with a dash of systems engineering. You know what's not on this test though: peano axioms or set theory lol.

No, of course, but who said they were?

Also, I'm getting the feeling all the lols are meant to underline something clever and pointed, but I fail to see it. Can you clarify?

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