
How I Transitioned from Physics Academia to the ML Industry - datasciencer
https://dluo.me/academiatoindustry
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Tehchops
I worry there are long-term implications to the exodus of intellectual capital
from academia -> industry.

 _However_ , I don't blame anyone who makes this obvious choice:

A)Years of underpaid, thankless, exhausting work, for a... fractional
probability of getting even a _livable_ wage.

Contrast this with:

B)$techCo, where even a decent undergrad CV will get you a six-figure job,
advancement opportunities, and maybe even a modicum of respect.

What sane(or not blindly idealistic) individual would willingly choose option
A?

If there isn't some kind of concerted effort by higher-ed orgs to reduce the
administrative and bureaucratic overhead of post-grad education, it's going to
have long-lasting consequences.

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petermcneeley
My favorite though is the all the masters and phds that end up teaching k-12.

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abdullahkhalids
I know a couple of people who went into a masters/phd programs with the
intention of teaching in schools later after finishing. They wanted to learn
some mental skills and they wanted to dedicate their lives to teaching.
Nothing wrong with it.

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petermcneeley
I just dont think its economically efficient. It would seem like a huge waste
of talent and potential.

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IggleSniggle
That’s funny, seems like reinvesting to me. Better long term returns.

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petermcneeley
Let me put it in worker placement board game terms.

If you have invested in making a worker scientist you probably want to place
them on the location where you get a chance of getting a Tech upgrade. You
likely dont want to place them on the location where you have a chance of
upgrading another worker to a scientist worker when a much less specialized
worker will do. And if you have too many tech upgrades then it makes no sense
to try to upgrade normal workers to scientist workers.

Anyone that plays games actually has an intuitive sense of economic
calculation.

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IggleSniggle
I think there’s societal level value in having normal workers with a better
appreciation for and understanding of science. Something like at a particular
saturation level: +10% bonus to research lab output, 25% reduction of policy
update costs with presence of overwhelming scientific evidence (stacks),
bonuses to population health improvement with Medical Lifestyle Research
unlocks, 25% reduction of knowledge rust, 5% reduction in knowledge related
production costs, faster to upgrade to scientist, 2% chance that a normal
worker will automatically upgrade to a scientist worker, etc. Obviously there
are opportunity costs.

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fmajid
When I was last interviewing candidates for a data scientist position, I can't
count how many physicists applied for the job when they didn't even understand
what overfitting was.

There is a definite sense of "physics is hard, therefore everything else must
be easy" groupthink, combined with Dunning-Kruger syndrome.

~~~
acdc4life
If you are hiring physicist to do Machine Learning, then you are doing it all
wrong. Physicist are excellent at mathematical modeling, especially those in
their 50s+. Data science from my experience is vastly inferior to mathematical
modeling. The OP that wrote the article seems young, and doesn't seem like
they've seen much. In the world of quantitative finance, physicists and
mathematicians reign supreme. These guys get paid millions in salary and
bonuses, because they earn 10x that for the firms. Their entire jobs are to
mathematically model things. There are very successful hedge funds that only
hire physicist and mathematicians. The current data science and machine
learning is child's play compared to the stuff these quant funds do.
Physicist, especially ones that have mathematical maturity are incredible
assets, they have a lethal skillset that isn't really there in silicon valley.
The majority of data science posts I see on HN are mediocre at best. I think
age and experience are highly under valued. The physicists and mathematicians
that Draper, Raytheon, DARPA, Renaissance Tech, Black Rock or Bridge Water (I
would include SpaceX and Tesla in this list) have are superior to any data
scientist that you will find in Silicon Valley. Mathematical modeling is
incredibly hard, seasoned physicists are very good at it (I can't emphasize
how difficult and long this process can be, nor can I articulate the reward
for doing things this way).

> There is a definite sense of "physics is hard, therefore everything else
> must be easy" groupthink, combined with Dunning-Kruger syndrome.

You are severely misinformed, and you severely lack a breadth of industry
experience. Physicists (especially the experienced ones)have something called
mathematical maturity. Having mathematical maturity is a skillset, you are
able to make precise formulations of some phenomenon you want to understand
things about, and prove with rigor that your ideas are correct, or that they
converge to what you want. This is a lethal mind to have, it's far superior to
machine learning or data science. You aren't relying on heuristics like
"overfitting". These concepts aren't well defined, and are very subjective.

I can tell you this with 100% confidence, AlphaGo, Duplex, Siri etc. are all
gimmicks, no different from Watson or Deep Blue. They are just applying
statistical brute force to narrow domains. I'd love to see a machine learning
system that consistently matches the profits that the successful quantitative
funds do. I know no tech company can do this, they don't have a fundamental
understanding of mathematical analysis, they don't hire the right people. The
right people are at DARPA, Draper, Tesla, SpaceX, any organization working on
the hard sciences. Go figure; pretty much all of the major important
discoveries came from either military or laborites investigating the hard
science.

Data science/ML, is great for simple data sets (there is a mathematical
definition of this, not heuristic). For many industrial problems like what
SAP, or McKinsey does it's perfect. The available cloud compute power and a
basic Statistics + CS degree will disrupt these businesses. But do know this:
they are severely limited. Machine learning will very quickly start showing
its cracks.

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whatshisface
It's worth knowing that computer vision originally worked with manually
programmed feature identification using more-or-less standard signal
processing techniques, but those techniques were wiped out by neural networks.
In some domains ML performs far, far better than any manual model could,
simply because in those cases first-principles understanding is impractical to
obtain. In fact, most textbooks on optimization start out with some comment to
the effect of, "such and such a technique is only good to use when you have no
knowledge of your problem domain." That speaks for a lot of problem domains.

~~~
acdc4life
This not 100% true. I am not advocating first principles. I am advocating
mathematical modeling. Mathematically modeling vision seems pretty much
impossible. I do think there are many theoretical parts of topology that might
be useful like homotopy. It could be the case that the "next calculus" needs
to be discovered. By that I mean a totally new branch of math that will change
mathematics for the next several hundred years (the same way Newton and
Leibniz did with the invention of calculus). It took a very long time for us
to go from algebra to calculus, in the end calculus ended up being simple. The
same may be true here.

Or another scenario, instead of modeling vision, model the brain,
mathematically (this is the path I favor)

There are mathematicians trying to model the brain. Unfortunately the field is
very new, around 50 years or so. Deep learning and neural networks (in their
current form) are temporary. There is a tremendous amount of experimental and
quantitative work that was done in the last 50 years that (I believe) will
solve vision far better than deep learning.

My major question to you is, why do people use back propagation?

[https://www.axios.com/artificial-intelligence-pioneer-
says-w...](https://www.axios.com/artificial-intelligence-pioneer-says-we-need-
to-start-over-1513305524-f619efbd-9db0-4947-a9b2-7a4c310a28fe.html)

This is the smartest thing Hinton has said in his career. Back propagation is
a pseudoscience, sort of like ether theory was.

Deep learning is a pseudo science. Why back propagation? Why sigmoid function?
I know the intuitions behind why these decisions were made. It's all
questionable, there is no rigor to it, nor is there experimental evidence for
any of this. Pseudoscience.

What will replace back propagation and the sigmoid or the relu function?

The guys modeling the brain have a good idea. Right now is a wonderful time,
there are institutes all over the world that have done tremendous amount of
work in this domain. There's a wide amount of competing ideas and models.
These ideas haven't trickled their way into computer science yet, and remain
esoteric. Which one is the right one? I have my "team" already picked, and it
will solve audio and vision, and basic problems in language. But the proof is
in the pudding.

Will it give us AGI? (Hint: no it won't. Not in our life time at least, the
math isn't there yet).

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consz
Can you expand more on what direction your "team" is researching towards?

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tinkerteller
Main paragraph:

 _I grew increasingly disillusioned with academia. It seemed that many STEM
undergraduates would rush into PhD programs out of sheer inertia, without
exploration of other options, and spend five, six years of their prime youth
down the rabbit hole of academic research. This resulted in frequent burnouts.
Many PhD graduates turned, in the end, to industry after acquiring a distaste
for academia in grad school. Those who continued in academia were met with
fierce competition for limited amounts of tenure-track positions, and must go
through several rounds of postdoctoral research stints before being accepted
for a faculty position, if they were accepted at all._

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braindongle
The academic ponzi scheme thing is overplayed. I'm a Phd Research Scientist at
a good school. I'm not faculty, but I have a lot of freedom and I get to do
research that is fulfilling. Yes, it's a pyramid (aren't all org charts?) and
I'm not at the top, but I'm happy.

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whatshisface
The unspoken aspect of the pyramid scheme theory is that academia is great for
the top one percent of Einsteins, and bearable for the top one percent of the
most tenacious.

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braindongle
I'm interested to see what the future of the Research Scientist role is. It's
basically a place for people who didn't do a post-doc and aren't bad-ass
enough to not need one. And, one could argue, people who just don't want to
play the faculty game. I hope we see that space grow in academia. I think we
will.

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irpapakons
Doing an undergraduate degree is not being in "physics academia". The author
transitioned from graduation to getting a job. But good post regardless.

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ada1981
I initially read this as the MLM Industry!

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ada1981
Downvoted because you don’t like that I misread it? Or don’t like that I
shared that I misread it and found it funny?

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throwaway5250
A lot of HNers don't have much of a sense of humor. (I thought it was funny.)

