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Comment by jchallis | Hacker News Reader

There is a far more obvious problem than the cost of computing - the cost of labor. If you are a deep learning researcher, you can join any of these companies and multiply your salary 5x.

Computer constraints are relatively straightforward engineering and science problems to solve. The lack of talent, that seems like the bigger story.

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As an ML researcher without a masters or PhD that’s not necessarily true. Companies reach out to me and say things like “we are really excited to talk to you about opportunities, especially given your 4 years of production ML model work and research”

Then right before interviews I hear, “well we like you, but didn’t realize you didn’t have a PhD. We have a really awesome software engineering / machine learning engineer opening that’d be great for for you. That’s what we’ll do the interviews for”

Cool. Probably happens 2/3 the time, honestly.

Just ask me about my parents, research, and projects...

Anyway, point being you can’t 5x with a B.S. in C.S. That’s why there’s a “lack” of talent.

A lot of the PhD requirements actually comes from the grants and R&D tax benefits these guys are after. For a lot of those you have to justify you have the right workforce.

If you are talking about the R&D tax credits there is nothing that says you have to have a PhD. We get a bunch of these every year and we only have one person with a PhD that is part of the research.

If you have a reputation in your field for good research and top-tier publications, like a PhD would, then I'm really surprised that you are being treated any differently.

There are some famous computer science researchers with no PhD, but they do have the papers to back themselves up.

I think they normally would, but I’m pretty young (27) so they don’t assume I have that. The HR folks who set this up are typically checking boxes and don’t realize equivalencies.

My usual tactic is to mention the patents and send some of my work and they reconsider.

In either case, my general point was that the 5x salary bump is pretty much reserved for an artificially set subset of people.

> sseveran 5 hours ago | favorite[-] [collapse root]

If you are talking about the R&D tax credits there is nothing that says you have to have a PhD. We get a bunch of these every year and we only have one person with a PhD that is part of the research.

How did you get 4 years of experience in ML at 27? care to share?

> How did you get 4 years of experience in ML at 27? care to share?

Presumably they've been working in ML since they were 23? What's so crazy about that?

Probably technically it’s been longer, but I built neural networks in OpenCL in 2014 (22 years old). That was for a research project at university.

In 2015, I started building ML models full-time at two large companies. First while I school, as a contractor, then joined full-time after school

This depends on the company but my understanding is that in most tech companies, there's not much distinction between being an ML engineer and being a research scientist/engineer with a Ph.D, in terms of the type of work or compensation, provided that you aren't in a pure research role (i.e. you were hired because your research is highly regarded by academic peers in a narrow field and get to do something close to original research with fellow academics). Most Ph.Ds aren't qualified for the latter type of positions either and the number of non Ph.Ds that are qualified would be entirely negligible.

Hasn't that been the case for decades though? Maybe not those exact numbers, but from what I understand, STEM profs could always go into the private sector/government and make more money. AI profs were probably the exception a few decades ago; they're just catching up now that it has gotten good enough. EDIT: By 'good enough' I mean that it's progressed to the point where it can actually be used for economic gain in a company/product, where before it was just a money pit.

Long gone are the days when your patron king or queen would fund you handsomely just for doing math.

Before AI was hot they were like other CS professors, maybe they could double their salary by going into industry. And give up the academic freedom to do whatever the hell you want as far as research is concerned, the joy of teaching for those who care, the unshakable job security of tenure, the option of doing the bare minimum to avoid getting fired once you have tenure, summers off.

Then their field got hot and suddenly people are not offering them double their academic salary in industry. They’re offering dectuple their academic salary.

Real demand calls forth supply.

Yeah, but at university you are more free to pick your project and can be much more nimble - so it balances out.

For me, that would maybe balance out if the salary difference was <2x. 5x? Forget about it.

You might be shocked to find out what the job is like for most professors. Grants, grants, and grants.

and travel, travel, travel, and submit, submit, submit papers.

Our AI lab is mainly experimental at this point, but we're a company so we're looking at how to apply certain models at the wide amount of data in our warehouse and how it could be surfaced in multiple ways.

We've also thrown a lot of money in this endeavor, so there's research and building a foundation with the underlying intent that this will be used to help the company.

I don't think that's too different than the university model of grants and research.

A select few get a chance to truly pick their projects, even then, most need to apply for grants internally and externally to get the funds. Professors may also be tied to teaching, cutting their time from the researches they them valuable. When accounting for this, a lower purchase power and seeing 'the grass is greener' on your private corp peers, you are very likely to think long and hard about jumping ship.

Maybe if you're a big name researcher at a top university. Most people are 'forced' to work on whatever happens to be getting grant money that year. But you are more free with regards to how you approach a problem and what you have to deliver.

Fortunately AI is hot right now so there are lots of projects where you can get funding simply by saying I want to apply machine learning to X.

Sadly we as a country dont invest more into CS research like we used to. Companies used to pay to have teams of developers go through training. Now in some places you are lucky if they even pay for training you do at home.

Same problem in Western Europe. In Eastern Europe companies train you before you start with hands on work or a paid professional training. In the West, companies expect you to somehow be born a senior dev. :)

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