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Dear Academia, I loved you, but I’m leaving you (2014)

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Re: Dear Academia, I loved you, but I’m leaving you (2014)

#41

Post by joefourier »

pasabagi wrote: post #23
anonymous5133 wrote: post #18

Earlier quoted context omitted.

Academia lives in a world where people are willing to spend hundreds of thousands to make themselves more knowledgeable in subjects which might have no economic value in the world. Just look at how much of our undergrad education consists "general education" type courses. While these courses are interesting, most of them are pretty useless. I took Chinese history, Asian american film, music history, art history and s…

I think the problem is, that leads to a society that's a bit like a sports car being driven by a nine-year-old. STEM and vocational stuff is great for getting to places fast. It's awful for making a society that can talk critically about where it's going. I say talk, and not 'think', because I don't think STEM people are bad at thinking about problems critically. I just think a lot of humanities is about developing a…

Do the humanities really succeed at accomplishing those goals? In my own college experience, the humanities courses I took mostly taught me how to parrot the professor's opinions, how to write long-winded dissertations with overly complicated language with the goal of appearing intelligent rather than being understood, and how to insert meaning into literary works that author likely never intended.

And while I was very successful in those courses, I fail to see how they prepared me to be a better citizen, or to think critically about the direction society is going towards. For my daily life, I would probably have been better off taking a course in plumbing than literary analysis.

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#42

Post by account2 »

PAClearner wrote: post #37
account2 wrote: post #33

Earlier quoted context omitted.

I am primarily interested in the experience of ML post-docs that forgo going into industry. Can you elaborate a bit more about their experience? Salaries look a bit lower for post docs vs industry work. Some I've seen start in the mid-40k to 70k range. Could be different based on geography.

are you already a grad student or are you considering it? in my department [we are an outlier probably] post doc wages are actually pretty comparable to industry i.e. >= 100k. 40k to 70k seems low to me. I would expect the range to be more like 60-100k. I haven't been on the real job market yet-but the key to getting a TT or postdoc position seems to be a) collaborators who want to hire you b) people who have heard y…

I am considering going into a CS PhD focusing in ML. The mid 40k-70k range was from quick google search I did for CS post docs in ML in lower cost of living areas where the cost of living is much lower than on the West Coast. I am trying to look at career prospects and weigh whether it makes sense to stay in academia or jump to industry (after I complete a PhD). If wages are closer to 60k-100k for post docs, then I may consider staying in academia for some time after completing a PhD depending on whether my career interest shift.

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#43

Post by laurentl »

v_lisivka wrote: post #19
stretchwithme wrote: post #12

Tracking what actually happens in scientific studies and how many don't get published would be a good idea. And a blockchain would be a way to do it. Knowing the whole truth could make science better.

Can you explain why it cannot be done with Web2.0 and Object Oriented Programming in ACID Cloud?

Well, it’s well-known [citation needed] that to be taken seriously, any vague business idea proposal must exhibit Proof of Buzzword. Just like in the Bitcoin protocol, when a Buzzword has successfully been mined enough times its reward value halves and one must add new, previously unexploited buzzwords to the proposal to maintain its credibility.

By this token web2.0, ACID, Cloud et al. have almost no buzzword value left. Even Bitcoin is very 2017. Which is why the only logical solution is nano-engineered quantum genetic algorithms with dark matter entanglement in P-space.

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#44

Post by Pica_soO »

Old_Thrashbarg wrote: post #36
aluren wrote: post #3

Regarding "literally everyone does it" (fishing for results, removing negative ones, and so on): it's very true, and much more prevalent than what people think. Even at top-level universities with big shots. Especially at top-level universities with big shots. I know someone, upon entering a lab to do electronic microscopy, who found out that the image data her lab had written multiple papers on very high-impact jour…

I had a similar experience. I worked in a neuroimaging lab during grad school. I found a mistake in our code that basically meant we had being overestimating the p-value from MRI data. It potentially invalidated many of the previous papers (and there were a lot, this was a "paper mill"). Being naive, I just assumed they'd be retracting them, but my labmates explained there was no way our PI would let that happen. It…

The knowledge market should reward "sniping" false results down- even if you just do it by giving a anonymous tip.

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#45

Post by pasabagi »

joefourier wrote: post #41
pasabagi wrote: post #23

Earlier quoted context omitted.

I think the problem is, that leads to a society that's a bit like a sports car being driven by a nine-year-old. STEM and vocational stuff is great for getting to places fast. It's awful for making a society that can talk critically about where it's going. I say talk, and not 'think', because I don't think STEM people are bad at thinking about problems critically. I just think a lot of humanities is about developing a…

Do the humanities really succeed at accomplishing those goals? In my own college experience, the humanities courses I took mostly taught me how to parrot the professor's opinions, how to write long-winded dissertations with overly complicated language with the goal of appearing intelligent rather than being understood, and how to insert meaning into literary works that author likely never intended. And while I was ve…

I don't think the humanities departments are particularly successful - but they've been underfunded and horribly hamstrung by political and administrative interference for about half a century now.

It's also not that Shakespeare's going to make anybody a better thinker. It's more that stuff like this sets the tone for the culture in general. If you don't really teach hard, interesting culture, then all the people who might have been interested just get the feeling that culture is kinda garbage and go on to engage with hard interesting ideas in other fields. Which leads to a public culture that's not in any way healthy - one that's really just people passing the time, that doesn't really push us or make us grow.

History shows this kind of thing is just incredibly dangerous. If everybody likes culture that's enjoyable and easy, easy, self-satisfied thinking predominates. Then suddenly everybody is marching waving big flags and wearing funny hats, because it's a lot of fun, and makes you feel great to be part of something.

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#46

Post by twblalock »

lgessler wrote: post #7

Looks like this poster was in STEM. The situation's even more bleak in the humanities--see for instance this Reddit thread[1]: > The humanities PhD is still a vocational degree to prepare students for a career teaching in academia, and there are no jobs. Do not get a PhD in history. [1]: https://www.reddit.com/r/AskHistorians/comments/96yf9h/monda...

I got a master's degree in history by bailing on a PhD program after the second year. The Reddit post you linked is spot on.

I left the degree program long enough ago that some of the people who started with me have finished their PhDs, although many of them still have not, after nearly a decade. Of the ones with PhDs, the only ones with decent jobs are the ones who work outside of academia. The others are adjuncts, and some of them qualify for food stamps.

The thing about getting a PhD is that you defer all of the income and career opportunities many people have in their 20s. By the time you graduate, other people your age have already paid their dues in entry level jobs, got promoted, bought cars and homes, been able to afford travel, and saved money for retirement. When you graduate you will be a decade behind everyone else in that aspect of life.

The only reason any rational-thinking person would make such a sacrifice is if the payoff at the end could make up for it.

A STEM PhD opens doors to a lot of jobs in the private sector. The only door opened by a humanities PhD is a chance to compete in one of the worst job markets in the world: academia, where there are several times as many new PhD graduates as jobs every year.

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#47

Post by PAClearner »

account2 wrote: post #42
PAClearner wrote: post #37

Earlier quoted context omitted.

are you already a grad student or are you considering it? in my department [we are an outlier probably] post doc wages are actually pretty comparable to industry i.e. >= 100k. 40k to 70k seems low to me. I would expect the range to be more like 60-100k. I haven't been on the real job market yet-but the key to getting a TT or postdoc position seems to be a) collaborators who want to hire you b) people who have heard y…

I am considering going into a CS PhD focusing in ML. The mid 40k-70k range was from quick google search I did for CS post docs in ML in lower cost of living areas where the cost of living is much lower than on the West Coast. I am trying to look at career prospects and weigh whether it makes sense to stay in academia or jump to industry (after I complete a PhD). If wages are closer to 60k-100k for post docs, then I m…

Well I would be happy to provide some context. I just finished my first year of CS Phd in ML (more on the theory side) and I really like it. I think most of the places you would want to do a post doc in CS are probably going to be moderately high CoL. My phd is in a place with pretty low CoL (but a still a top 10-top 20 school (depending on who you ask) ) so the graduate stipend goes reasonably far.

The other thing to note in ML is that it seems like a few people go to industry research labs for a few years i.e MSR/FAiR/google brain and then come back to the academy since there are industry roles that involve research and publication. for instance moritz hardt.

my personal plan for the first 3 years of grad school is to work really hard and try to keep both academia and industry open and after year 3 evaluate the number of publications I have and my current skill set to see if I can make it in the academy or shift more towards industry.

I think the biggest factor I would comment on is look very closely about what jobs the graduated students from the department you matriculate at AND more importantly the professor you want to work with go on to do post Phd. There are a lot of naysayers in this thread about the risks of an academic career and I share those concerns but I felt a lot more comfortable taking the plunge after I looked at the career record of the graduated students of my advisor. They were all either tenure track or had good industry positions.

edit: if your advisor has collaborators in industry groups I think it is pretty straight forward to get an industry gig.

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#48

Post by account2 »

PAClearner wrote: post #47
account2 wrote: post #42

Earlier quoted context omitted.

I am considering going into a CS PhD focusing in ML. The mid 40k-70k range was from quick google search I did for CS post docs in ML in lower cost of living areas where the cost of living is much lower than on the West Coast. I am trying to look at career prospects and weigh whether it makes sense to stay in academia or jump to industry (after I complete a PhD). If wages are closer to 60k-100k for post docs, then I m…

Well I would be happy to provide some context. I just finished my first year of CS Phd in ML (more on the theory side) and I really like it. I think most of the places you would want to do a post doc in CS are probably going to be moderately high CoL. My phd is in a place with pretty low CoL (but a still a top 10-top 20 school (depending on who you ask) ) so the graduate stipend goes reasonably far. The other thing t…

Thanks for the context. That sounds like a good plan to me regarding post-doc locations. I am also interested in theory side of ML. What areas of mathematics should one learn really well that apply to the theory side? What blogs, papers, books would you point one to to learn the theory side more? To your knowledge are their applications of abstract algebra to ML? If so, what areas of algebra apply & what problems do they solve?

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#49

Post by [deleted] »

PAClearner wrote: post #47
account2 wrote: post #42

Earlier quoted context omitted.

I am considering going into a CS PhD focusing in ML. The mid 40k-70k range was from quick google search I did for CS post docs in ML in lower cost of living areas where the cost of living is much lower than on the West Coast. I am trying to look at career prospects and weigh whether it makes sense to stay in academia or jump to industry (after I complete a PhD). If wages are closer to 60k-100k for post docs, then I m…

Well I would be happy to provide some context. I just finished my first year of CS Phd in ML (more on the theory side) and I really like it. I think most of the places you would want to do a post doc in CS are probably going to be moderately high CoL. My phd is in a place with pretty low CoL (but a still a top 10-top 20 school (depending on who you ask) ) so the graduate stipend goes reasonably far. The other thing t…

[deleted]

Re: Dear Academia, I loved you, but I’m leaving you (2014)

#50

Post by PAClearner »

account2 wrote: post #48
PAClearner wrote: post #47

Earlier quoted context omitted.

Well I would be happy to provide some context. I just finished my first year of CS Phd in ML (more on the theory side) and I really like it. I think most of the places you would want to do a post doc in CS are probably going to be moderately high CoL. My phd is in a place with pretty low CoL (but a still a top 10-top 20 school (depending on who you ask) ) so the graduate stipend goes reasonably far. The other thing t…

Thanks for the context. That sounds like a good plan to me regarding post-doc locations. I am also interested in theory side of ML. What areas of mathematics should one learn really well that apply to the theory side? What blogs, papers, books would you point one to to learn the theory side more? To your knowledge are their applications of abstract algebra to ML? If so, what areas of algebra apply & what problems do…

I could rant about this for hours. I actually just went to a defense for a deep learning paper that had a ton of abstract algebra. I am honestly not really a fan of deep learning and algebra because all the papers to me like- seem to stop at describing some really basic feedforward network as some really specific mathematical structure but these theories a) provide very little explanation of empirical phenomena b) provide no new directions of research in terms of like useful network architectures.

I haven't really come across algebra in machine learning other than people applying it to deep learning.

i.e. https://arxiv.org/pdf/1802.03690.pdf

ie. https://icml.cc/Conferences/2018/Schedule?showEvent=2048

I don't personally find papers like this valuable but idk I have never really enjoyed abstract algebra.

For areas of mathematics to do theory in ML (and to do ML more generally!)

-probability/concentration/hoeffding bounds [the PAC model] [Key]

-linear algebra [key]

-optimization [key]

for books

-understanding machine learning by shai ben david

This book is nice since it really balances theory with a more practical understanding.

-An Introduction to Computational Learning Theory by kearns is a classic [low priority]. this is fun since the proofs are simple and deep but is very very far away from practical algorithms.

-convex optimization by boyd

Course Notes:

[I think a good alternative to blogs is stalking course notes for other schools-they are very often public.]

- http://ttic.uchicago.edu/~avrim/MLT18/index.html

good learning theory course by avrim blum who is a big deal in learning theory and theory.

- tim roughgardens notes are a blessing for algorithms and theory [seriously he should have a patreon or something]

https://theory.stanford.edu/~tim/notes.html

Blogs:

-http://www.argmin.net/

this is ben recht's blog and is filled with ML wisdom.

-https://blogs.princeton.edu/imabandit/ not quite learning theory but a lot of ML adjacent stuff

I don't read many blogs as I should tbh so other people can give better advice

VIDEOS https://www.youtube.com/channel/UCW1C2xOfXsIzPgjXyuhkw9g

This is the simons institute youtube channel. probably the best single location for recordings of TALKS in computer science-good amount of ML talks.

https://simons.berkeley.edu/videos

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