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Comment by echelon | Hacker News Reader

All these people don't understand how hireable and desirable they are now. They need to get out of academia and plugged into AI positions at tech companies and startups.

Their value just went up tremendously, even if their PhD thesis got cancelled.

Easily millionaires waiting to happen.

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edit: Can't respond to child comment due to rate limit, so editing instead.

> That is not how it works at all.

Speak for yourself. I'm hiring folks off 4chan, and they're kicking ass with pytorch and can digest and author papers just fine.

People stopped caring about software engineering and data science degrees in the late 2010's.

People will stop caring about AI/ML PhDs as soon as the challenge to hire talent hits - and it will hit this year.

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That is not how it works at all. You won't get hired if you don't have the academic pedigree in the first place. That means a completed Ph.D and good publications in good journals.

Hired in academia? Sure.

Hired in industry. That's the opposite. I've had a friend who had to hide that they had a PhD to be hired...

I guess we are living in two different universes. Any job ad for an ML role or ML adjacent role says Ph.d required or Ph.d preferable. Maybe it is also a matter of location. I am in Germany.

For a plain SWE role a Ph.d might be a disadvantage here too, but for anything ML related it is mandatory from what I can see.

In my hiring experience as an interviewer, 90% of candidates with PhD or not will actually have mediocre grasp on ML. It is a rare happy day when I get a good candidate. We interview for months for one hire. I got to interview candidates worldwide so I've seen people from many countries.

Was this hiring for ML positions?

As someone who hired for this in general we'd use PhD (or maybe a Masters degree) as a filter by HR before I even saw them.

It's true that a PhD doesn't guarantee anything though. I once interviewed a candidate with 2 PhDs who couldn't explain the difference between regression and classification (which was sort of our "ok lets calm your nerves" question).

Yeah, you don't want to be anywhere near a place claiming to hire HS graduates/4chan posters in disciplines requiring advanced knowledge for successful product development, unless, idk, they have demonstrated mathematical talent through well-established media e.g. math olympiads, thesis on some relevant discipline.

Almost all the time, they're shitty startups, where bankruptcy is a matter of time, run by overpromising-underdelivering grifter CTOs pursuing a get-rich-quick scheme using whatever is trendy right now -crypto, AI, whatever has the most density on the frontpage-.

Yeah true, I've had to work with too many fresh college grads to not relate to this. People try to take some rare case and generalize when that's really not applicable.

I am in France. That was in bio-cryptography, which strongly uses ML.

That was a few years ago, though.

Sorry, that's patently untrue. Perhaps it's anecdotal, but I know a host of undergrads who got head hunted into quite elite tech positions either directly from Uni where I studied, or due to private projects they were in. And I even know a few that doesn't even have any uni edu that got hired to very high technical positions. Usually they were nerdy types who had worked with or had exposure to large systems for whatever reason, or who showed some promise due to previous work, demos or programs they'd made. But sure, most people have to go the edu route. It's the safest way into tech, as you are - at least in principle - fully vetted before you apply. Thinking that you can get a data science or hacker job just by installing Kali is ofc also very untrue.

I think my post is more representative of the truth than yours. I am sure you are telling the truth, but these unique talents you are talking about are not representative of the bulk of people working in research.

(My posting rate limit went away)

The demand for AI/ML will fast outstrip available talent. We'll be pulling students right out of undergrad if they can pass an interview.

I'm hiring folks off Reddit and 4chan that show an ability to futz with PyTorch and read papers.

Also, from your sibling comment:

> Maybe it is also a matter of location. I am in Germany.

Huge factor. US cares about getting work done and little else. Titles are honestly more trouble than they're worth and you sometimes see negative selection for them in software engineering. I suspect this will bleed over into AI/ML in ten years.

Work and getting it done is what matters. If someone has an aptitude for doing a task, it doesn't matter where it came from. If they can get along with your team, do the work, learn on the job and grow, bring them on.

Thanks for the insight. I hope you are right of course. Unfortunately, Germany is a bit hopeless in this respect.

I'm DevOps engineer and I became super interested in AI recently. Any tips on how can I shift to an AI/ML career?

I recommend taking all the introductory courses you can find on both AI and ML. If you like the introductory courses, and you feel compelled to move on, then chances are you'll do well in a job regarding AI or ML. There are also several ways into it, either through pure mathematics, statistical modelling, data science, particularly through learning about various algorithms and reading papers, or even through practical application within data warehousing or day-to-day programming. I'd say it helps to have an academic background in either IT, statistics or mathematics, though, but depending on what you're aiming for it doesn't need be a firm prerequisite. Btw. linguists or anyone interested in natural language ought also apply!

Sorry, you don't need the Ph.D. publications at top 10 NLP venues are enough

Just as an fyi some of the top AI folks at OpenAI don't have PhDs. I remember reading that on Twitter (I think).

I think Greg Brockman falls into this category.

Chris Olah was at OpenAI but is now one of the founders of Anthropic but doesn't have any degree (he joined Google Brain after dropping out of his undergrad degree).

I think they are pretty exceptional though - most people on the ML team do AFAIK. Perhaps on the infrastructure side the backgrounds are different.

As much as I'd wish to say "you're wrong, people care about intelligent, passionate people who do great work, not PhDs" you're right about much of the work out there.

We've tried many time to work with CSIRO (the NSF of Australia) and it's fallen flat. They love impressive resumes and nothing else. I'm having a chat with their "Director of ML" who's never heard of the words "word2vec" or "pytorch" before. (And I'm a UX designer!)

I think at most corporate firms you'll end up running into more resume stuffers than people who actually know how to use ML tools.

> Director of ML" who's never heard of the words "word2vec" or "pytorch" before

Reminds me of Computer Security academic and professional pedigree where these people aren't even programmers

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