
Summary of the Neural Information Processing Systems Conference 2016 - ericjang
http://blog.evjang.com/2017/01/nips2016.html
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jostmey
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?

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eanzenberg
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.

~~~
argonaut
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).

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Teodolfo
How much do they pay? And how is the pay structured?

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argonaut
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).

~~~
eanzenberg
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.

~~~
argonaut
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.

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AndrewKemendo
Great writeup.

He makes a great point I think inadvertently:

 _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).

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nojvek
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.

Do we even need 10 billion people anymore?

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musesum
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.

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hardmaru
My favorite 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 (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."

~~~
Teodolfo
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.

