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Comment by thwayunion | Hacker News Reader

Now is a terrible time to start a PhD in ML. If you do a CS PhD, pick literally any other subfield.

I'd argue that cross field is a good avenue for a PhD in CS. Work in collaboration with one of the sciences. There are plenty of papers to be had in application. Not everything in ML research needs to be algorithms. You'll likely find places where the models break down and need algo work too

People have been saying this for as long as I've been hanging on ML forums, meanwhile the professors I work with can't keep up with industry demand for ML and ML adjacent roles and have to open up new classes.

Sounds a lot like my dad telling me to go to law school instead of getting into cs. He thought the ultimate goal of cs is to make everyone in the field obsolete... well maybe he was right in the end.

Lawyers too (maybe they are first)

Why?

It's trendy, and in general it's best to avoid trendy fields.

But if you actually care about ML -- I think it's one of the best things in the world -- go for it! ML has never been more accessible.

Trendy fields often have lots of money in them in both the short term(grants, associateships, funding), and the medium term(first job prospects) as well as benefits like preferential appearance in journals(if two equally insightful articles are up for one spot, would you rather take the article on the topic many are interested in or the article from a much more niche area?) and possibly more low-hanging fruit since there is so much more work to piggyback on/respond to/extend.

The subfield is oversupplied with labor relative to the supply of good ideas worth working on for six years and advising capacity.

because the very best machine learning models are distilled from postdoc tears.

I think every significant model has come from industry. us postdocs just tweak hyperparameters and publish answers to questions about them that no one is ever likely to ask

It's like the "will it blend" series, but for machine learning

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