"Mr. Zaremba said big tech companies were offering him two or three times what he believed his real market value was"
Seems this fellow knows a whole lot more about computers than he does markets hahaha
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.
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.
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.
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?
If X is willing to pay Y the value is Y.
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.