A.I. Researchers Are Making More Than $1M, Even at a Nonprofit
nytimes.com
nytimes.com
Unless you are a top professor at an R1 research university or have a PhD from an R1 university studying under the other top minds in the field or are otherwise in the most elite fraction of the profession, it doesn't sound like this is particularly useful information for the average practicing or aspiring data-tician. What's the typical salary for someone with, say, a two-year professional masters?
I laughed at the joke and upvoted you.
And I frowned at the downvotes for the other comments. Friends, why do we turn into insecure conservative bullies when it comes to femininity? Why accept the implicit normalizing and othering of last century's favorite pronouns?
Let's subvert the status quo!
I've almost done my masters - I finished all the courses, but did not submit my graduate thesis in time - so I have what we call "absolutorium", i.e. finished the program without pursuing a degree. The other day, a friend from my alma mater wanted me to come and do a semester of programming classes as an "industry consultant". It was all arranged properly and I was getting ready with my lectures when, few weeks before I was supposed to give the first one, I became a political point in inter-faculty negotiations. People from one of the other faculties argued it's disrespectful to allow someone with mere BSc to teach students. Ultimately, my faculty had to let me go.
So yeah, that's the kind of bullshit that having an academic title may help you navigate. Whether or not it is worth the effort to get one is arguable, though.
The other three reasons are visa (proof of "ability"), wanting to work in government (if you really, really want that) or early-stage startups that need Ivy-League credentials for their investors, because they don't have anything else to offer.
Other than that, as a software engineer in the private industry, nobody will care about any of your degrees.
Your actual contributions to the field.
of which, comp sci is one of the few fields where you don’t need an academic degree to have significant contributions to the field
But I read the other day about a biologist who just recently made a major contribution to graph theory, as an amateur mathematician?
He can come speak at all of my cookouts.
I don't think he's published a paper as a first author. He's about to now, but his advisor is not happy with it and has made it clear his work on that paper is not sufficient. His advisor is sending all sorts of signals that he has given up on him - he often ignores his emails for requests to meet, etc. And he's expressed displeasure about his progress several times. The University will require his advisor's signature every semester because he has gone beyond the university's deadlines for a PhD.
He's a very smart guy and is good at software (his PhD is not CS, though). He can get a good job with a decent (or even top) company. He is almost 40 years old. Oh, and on top of all that, given how his research is going, there's a good chance no one will hire him for his research work even if he gets a PhD.
I asked him "Why not quit and get a job?"
Him: "Man, I put so much time into it and I'll feel all that time would have gone to waste. And please don't talk to me about sunk cost!"
Me: "Did you every consider that you will finish the PhD, and then end up asking yourself why you wasted so much time pursuing it?"
I've been in his shoes, and I quit my PhD as well (although spent a few years less than he did in school). Then I went to industry and saw the types of jobs I would have gotten if I had completed. Although they paid better, I would have hated those jobs. As for the time I spent in grad school? How could it possibly be a waste? I went because I had a passion for the topic, and I learned a lot while there. You don't lose all that knowledge and experience when you quit.
Why?
Furthermore, it's a fairly specialized field - only a few companies in the US (or heck, the world) hire such people. So you can't just up and leave.
Or, more accurately, non-profits are businesses.
True talent is extremely rare as well as lucrative. That said, I'm glad I turned down openAI's offer of only $200K. Yikes. Ilya is better than I am, but not 10x better.
You can look this up for just about any non-profit, including most if not all ivy leagues.
PhD adds about 25k-40k to the figure, depending on industry.
There, fixed it. These are celebrities within the field and there’s nothing surprising about their pay. YouTube celebrity pays more but I suppose you take what you can get.
Key quote:
““When you hire a star, you are not just hiring a star,” Mr. Nicholson of the start-up Skymind said. “You are hiring everyone they attract. And you are paying for all the publicity they will attract.”
It’s not that author doesn’t know this. He even tries to protect himself by making sure facts are included:
At DeepMind, a London A.I. lab now owned by Google, costs for 400 employees totaled $138 million in 2016, according to the company’s annual financial filings in Britain. That translates to $345,000 per employee, including researchers and other staff.
I used to work at a world leading Rnd Organisation and they where trying to get the OH rate under 500%.
- "This is only a few people, so not something that could affect the rest of us"
- "Those people had a specific skill, so the rest of us couldn't possibly make that much"
- "This is only happening in the Bay Area, so anybody living anywhere else shouldn't expect to make that much"
It's maddening. How about, instead, when we see another datapoint showing how ridiculously valuable we developers are to employers and our potential earning power, we talk about how to get one of those million dollar salaries. Because that is in fact doable, and this article highlights just one of the many ways a smart guy in his 20s could position himself so as to be making that million dollar salary within the next few years.
That would be much more productive than trying to prove to everybody that the market does in fact only ever pay $70k/year because that's what you see on that Java job ad on Monster.com.
This is a reality that developers need to take on board. And sadly, most people here are actively fighting against doing so. To their detriment.
If an article said “supermarket checkout clerks are earning over $1m” and the article explained that a couple of retired property investors decided to work at a supermarket, would you respond the same?
The harsh reality is, very few people will have the tenacity, intelligence, and opportunities to become a top 20 AI researcher, especially in their 20s.
And it's to your detriment, because there are a lot more than 20 people making that kind of money in software today.
Note further that AI is in fact a subset of software. There is nothing magic about it that somebody like yourself or anybody else here can't pick up.
You seem to grudgingly accept that doing so can make you a lot more money. But still you go out of your way to insist that you, personally can't do that.
Why?
You might as well say that Michael Schumacher just drives a car pretty well, so most of us could spend a bit of time in training and win F1
The reason I go out of my way to insist I can’t do that is that I’m not in my 20s, and it would be delusional of me to think AI is “nothing magic” and I can watch some YouTube videos to become the worlds best.
In the case of the original article, those million dollar salaries go to researchers, not to developers. I haven't heard about developers making a million a year.
Source: work in AI.
Did you see the laundry list of "Current Customer Use Cases" under https://ramm.science/x/signalbox/?
I respect fake-it-till-you-make to some degree, but I find too much "spinning" actually hurts the ML industry (though no doubt profitable for the spin artists). Communication style straight from the ICO world.
And he singlehandedly built one of Europes largest online betting system backends (and scaled it to 800 transactions a second using Deep Learning, Kafka and a Hadoop cluster)
The frontends (where the casino / betting games run) do a lot more than this, but they work against the balance in the account, it's the payment gateway that's doing 800/s
They do do fraud detection on the deposits, because of anti-money-laundering. The DL models also monitor various other types of abuse (bonus abuse and in-game abusers). They also are experimenting with Deep Reenforcement Learning to actually play some of the games
But good work making something and becoming successful!
I really don't see the point of these articles. If you're the expert corporate lawyer in mergers and acquisitions with an expertise in EU-US mergers in the agrochemical sector you'll make bank when you hit that one huge multinational client. And then that combo of skills goes back to being not particularly valuable.
-How is the performance of $1M AI researchers measured? Like, at their six month review, what do you look at to tell if your money is being well spent?
-How do you tell if it makes more sense to hire 1 famous AI researcher vs 10 additional junior data scientists? What sort of products are suited to different hiring strategies?
-How much of $1M AI researcher hiring is long-term/speculative (i.e., based on hopes of future products/revenue) vs short-term/measurable (immediately measurable as soon as a code commit hits the product)? Are there examples of products where profit went up by millions after a famous AI researcher was hired?
It sounds like quite a few companies are grappling with these questions today. I'm really curious to know how their thinking is evolving on this topic.
If I had the money I'd hire hundreds, and that's just on my subfield (A.I. for software development).
Like they say in the article “When you hire a star, you are not just hiring a star, You are hiring everyone they attract. And you are paying for all the publicity they will attract.”
That said, I'm reminded the champion of the effective altruism movement made his name arguing that charities ought to rigorously audit their efficiency in turning donations into results before recommending $30m of foundation cash went to this particular speculative nonprofit research project his roommate and future brother in law happened to work at...
I am not surprised that an AI researcher can make that much.
Consider the AI researchers behind the optical recognition systems for handwritten text used in ATMs. Now you can massively deploy ATMs that read checks 24/7 without supervision... Paying $1m 2018 dollars to those guys is not a proportional compensation with respect to the amount of wealth they brought in.
I’m going to be the one to say it. Ian Goodfellow is underpaid at 800K.
For example, the life-like faces NVidia research created in Progressive Growing of GANs for Improved Quality, Stability, and Variation[1] are just an incredible achievement (it's worth looking at the pics in that paper if you haven't seen them).
Approaches like that have real-life impact in things like games, architecture, design, film, etc (and that is just the obvious implications for images). GANs of course can be used outside images and they have just as much potential there.
Anywhere an auto-encoder is used now can probably be done better with a GAN.
Good AI easily can carry a $1B valuation, so a few million for you talent makes sense.
Previous employer seems to be a factor here.
By definition, if he was offered some amount then his market value is at least that amount. That's literally what the term "market" is doing. Maybe he was offered above his value, but not above "market" value, by definition.
lexicon ripped from: https://waitbutwhy.com/2015/11/the-cook-and-the-chef-musks-s...
How is this news?
Seems this fellow knows a whole lot more about computers than he does markets hahaha
This is the pattern that goes on all the time inside companies: constant attempts to hire talent from the outside, coupled with complete ignorance of the skills (perhaps developed post-hire) of their existing employees. I know more people than I can count who know everything required to be doing "data science" for a company, but—since they were hired to be maintenance dev-ops people—they will never be considered for "promotion into" a data-science role when there's a vacancy.
1) did you pass linear algebra?
2) show me some code you have written and deployed
3) here's Learn Python the Hard Way, start reading and come back with questions.
In the modern era, I have not found anyone who can pass test 1 and doesn't know code (except for 1 mathematician who's like 70 and no one cares if he can write code, his ideas are so insanely good that other people write his code for him, despite their day jobs). These people execute and come back with challenging problems for me.
Anyone who passes test 2 seems to be able to pick up whatever task I give them. I'm not entirely sure they understand what they're doing all the way down, so there's more review, but they can generally execute. Questions go back and forth.
If I get to test 3, well, no one has come back with a question yet.
What this tells me is there is plenty of signal in the culture that math and programming ability are valued. The people don't, ain't never gunna.
At that point it’s just experience to learn the rules of thumb that guide practical implementations.
I also learned real quick that I don't want to do ML. It's all about data generation/sanitation/management which just doesn't click for me.
However, you can simply think of it as syntactic sugar that manages the encoding annotations in the default case. Where is it creating problems for you? Is it some performance hit or something else?
Not harnessing these untapped assets you already possess is a failure for a company, in much the same way that not shaving cost centers or negotiating purchases would be.
+ Yes, I’m trying to hint here that one would be crazy to allow oneself to be exploited in this manner. If you truly have the ability to do these additional jobs, then you should be applying for roles that explicitly, rather than implicitly, use those skills, and offer compensation for them. Sadly—for the same reason many people find it hard to negotiate salary—many people won’t try for jobs that no one has told them they’re “allowed” to apply for.
The overpaid genius, at the very least, has a preponderance of evidence that he understood all the math he’s using and has evidence he can innovate with it rather than regurgitating code (maybe that’s slightly too lenient)
This assumes a separating equilibrium. There is one, of course, but it's biased against genius. Businesses don't want to overpay. Not should they. People don't negotiate their salaries. They should.
https://en.m.wikipedia.org/wiki/Separating_equilibrium
https://en.m.wikipedia.org/wiki/Signaling_game
The difference between the two you mention could be qualitative, as you imply, or it could be that some of the DS folks send the wrong signal. Not choosing a top-tier AI university would be a poor signal, and fail to differentiate quality candidates of equal ability.
It comes down to whether the mental model of meritocracy is actually practiced by the business world, which also includes whether screening performed by employers is accurate. It's not, ergo it stands to reason there are poor AI geniuses too.
This is some real crazy talk here. The idea that you should limit yourself to the particular set of knowledge you wish to make money off of is insane.
There's almost no such thing as wasted learning, even if you're not interested in pursuing it for a career. Maybe it's a hobby, maybe you touch it in a tangential way for your normal work where a basic understanding brings value but is not necessary.
This is the same attitude that undervalues previous experience and builds in favor of specific lingual or stack competence.
> This is some real crazy talk here. The idea that you should limit yourself to the particular set of knowledge you wish to make money off of is insane.
Crazy talk? That's what you get when you blatantly ignore half of what I said. In the previous sentence I quite literally asked if these want to be doing data science to begin with. If they don't have any interest in it, then it doesn't matter whether or not they know how to do it as far as the company is concerned; they're not going to be doing it either way. Nowhere did I ever suggest that it's somehow a good idea for people to be limiting themselves to a particular knowledge set.
Plus, if Bill Gates buys a hours in your neighborhood, it's more or less guaranteed that the prices go up on name recognition alone.
Once the owner accepts that bid, the liquidity in the market has been completely taken, and the likely future market value reverts to ~100,000.
If X is willing to pay Y the value is Y.
(I can prove it if I find 2 of them running their own business successfully)