Quote: "many top hedge funds and trading shops came to NIPS to run career booths, but there was a surprising lack of interest from attendees compared to the likes of Apple, Facebook, Deepmind, Google, etc."
Maybe Apple, Facebook, and Google think they can develop their talent in house from their programmers?
Dunno why that's surprising to the author. Those tech companies will pay for a good data scientist about 2x what a hedge fund would pay the same person initially. Hedge funds and finance company pay will increase more over time though, once they have a good track record.
Most of the tech people I know working in finance are very disillusioned.
The common complaint is that regardless of performance, their pay increases and bonuses end up being shit YOY and that the only way to make serious money is to hop from company to company every year.
One friend who builds trading infrastructure has increased his pay from $180k to north of $480k just by doing this and is about to go to his 5th or 6th fund (in roughly as many years). He kills it, ends up getting the token bonus they give tech people, waits out his non-compete and leaves.
They treat tech talent as commodity just as much as anywhere else. The other downsides being you usually can't trade your own account while employed.
Said friend would prefer to start a fund soon rather than keep this up as he'd rather be able to trade.
Hedge funds and quant finance pays way way way more than Google/Facebook/etc.
To whatever extent they have trouble attracting researchers it's because you will never be able to publish anything or talk about your work in a finance firm. Academia is status/reputation/ego driven (not that there's anything wrong with this).
It is heavily bonus centric. In theory it is unlimited, based on how much you make the firm, which is why you could theoretically make millions any given year (rare).
My friends in quant finance made more than 200k guaranteed their first years (not including performance bonus), and they did not have graduate degrees. It went up to more than half a million in a few years (including bonus).
Are they in more research oriented roles? A basic good Quant Analyst at Google or Data Scientist, Analytics at Facebook will easily clear 200k but these aren't machine learning research experts.
Are those masters/PhD's? My data points were people with no graduate degrees.
The ramp up and ceiling is much higher in finance. First year salary for someone with a bachelors in a finance company may be comparable to someone with a masters/PhD in a tech company, sure.
However, they haven't created significant commercial value in industry yet, in ways that couldn't plausibly be substituted with traditional supervised learning.
Transfer learning, domain adaptation and semi-supervised learning alleviate the data-hungry requirements of deep learning, and are starting to work really well.
Transfer learning is a term of art, but it also represents how supervised learning works from human>machine.
I think we need to embed more ML systems into our daily lives to "teach" networks how/when/why to do things. IMO the best way to do this is through AR as it's a great input output tool for recommending actions to the user and transmitting the type of data that we are making great progress in (vision).
As someone who is not a deep learning wizard, I could barely follow the notes and jargon.
A part of me feels very scared. What if the big companies do end up replacing millions of jobs.
Better than human driving, speech recognition, image recognition, translation, learning to code, knowledge systems.
Once you learn how to create a better human brain and make billions of them at scale. The big companies become monoliths who can get as many AI slaves as they need.
This is a great overview. Have been very keen on emulating biological neurons, where axons and dendrites may span beyond a single layer. ResNets seem like a step in that direction.
"many top hedge funds and trading shops came to NIPS to run career booths, but there was a surprising lack of interest from attendees (attendees were more interested in the likes of Apple, Facebook, Deepmind, Google, etc).
At a regular college career fair in the East Coast, these roles are reversed. The talent pool at NIPS seems to be far more interested in tech and open-ended research than making money."
Also perhaps machine learning experts are paid well enough in industry research labs in tech companies that the difference isn't perceived as that large given the diminishing marginal utility of money.
Maybe Apple, Facebook, and Google think they can develop their talent in house from their programmers?