A.I. researchers are making more than $1M, even at a nonprofit

How replicable is this thing? Looking from a monetary perspective, is it worth getting a Ph.D. in AI? Assuming you like both academic research and working in the industry, if you were to make a choice whether to do a Ph.D. or not, how would you make it?

I have a couple of questions -

- Would you say that you'd have got an AI researcher position without a Ph.D.?

- Also, why is NEURIPS or ICML papers is not a hiring guarantee? I thought they're highly sought after.

> A third big name in the field, the roboticist Pieter Abbeel, made $425,000, though he did not join until June 2016, after taking a leave from his job as a professor at the University of California, Berkeley. Those figures all include signing bonuses.

425k after signing bonus is probably closer to 300-350k base + annual bonus, which feels low for someone in such a distinguished position (close to a typical specialized physician or big tech software engineer with 5-10 years of experience). After finishing my masters degree in machine learning in the mid-2000s, I immediately got job offers higher than my much more talented professors. To me, the real story is that academia has set the compensation bar low for people adding so much value.

Edit: to be clear I know these salaries are high and people in my field are lucky to be making so much. But I also don’t think it’s accurate that people in the field are overpaid, which titles like OP may make it seem.

Remember that Hinton and other deep learning pioneers labored in obscurity for over a decade, probably closer to two.

Techniques like deep learning were out of fashion for a long time. Google was making tons of money on non-AI techniques; I remember Sergey Brin saying he was surprised by the deep learning breakthroughs in 2012 because the conventional wisdom was that "AI doesn't work".

And people who did Ph.D's under Hinton and others 10+ years ago also were not following trends.

You have to take risk to get reward. The way to do that now would also be to labor on an obscure subfield of AI that isn't certain to even work, let alone become commercially viable. (And there are plenty of people who did that on fields that looked more promising than deep learning 10+ years ago.)

There's definitely a need for new AI techniques, see this other current story:

https://news.ycombinator.com/item?id=21104037

> Would you say that you'd have got an AI researcher position without a Ph.D.?

It's difficult to get any true researcher position without a PhD. It doesn't mean that PhD has to be in AI. Research involves a lot of reading and writing papers, which a PhD is supposedly training you how to do.

That said most places will say "equivalent practical experience" and it's entirely possible to be competent in AI/ML without a PhD.

I did a PhD in space science, I now do machine learning in ecology and spent the summer working on machine learning for disaster management. The interesting jobs (to me) are where domains cross, and it's also (hint hint) much easier to get a job doing AI for X than it is doing "fundamental AI". In any case, you're often doing stuff that nobody has done before anyway, but you don't need to spend your life hunting for the new ResNet.

> Also, why is NEURIPS or ICML papers is not a hiring guarantee?

What the OP probably might be implying is that everyone has a publication in NeurIPS nowadays.

I think it goes deeper than that though, publishing in machine learning is broken. Having 10k people at one conference is not an efficient way to distribute research. You have to submit a full paper in November for a conference next Summer - pretty much only computer science does this madness.

What's interesting is how unique this attitude is. In astronomy, for example, conferences are a fun place to catch up with folks in your niche. There might be a few hundred people and probably it'll be single-track. We publish whatever journal is the most relevant and they're generally all considered equivalent. Nobody cares if you publish in ApJ vs A&A vs MNRAS, if your research is good.

There are also concerns that the quality of these venues is decreasing because the pressure to publish in them is so high.

See https://arxiv.org/abs/1807.03341