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Comment by fock | Hacker News Reader

An undergrad to his supervisor in our office talking about publishing a paper: I've fixed the data, now the plots look ok. I (undergrad too) am sitting there thinking - well, you are using ML as a regression blackbox to plot a line, I can do that too w/o ML if I'm fixing the data. Supervisor: ok, that's really great. Me cringing...

I'm not hammering the ML-keyword above my work (and thus am getting considerably less academic attention), but it's nice to hear from people who made it in academia that they support my theory. 50% off the people are just showoffs throwing buzzwords and positivity around while they produce a load of sh...

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Yup. As someone who is in a junior position in the field - I'm torn between, on one hand, riding the wave so to speak and taking advantage of all the buzzwords that I can put on my resume (which is fine with me because I can back them up), and on the other hand avoiding association with a certain type of person/career path that might turn out be just hot air in a couple of years.

So if I can actually write decent code, have solid understanding of software development principles, have studied math and statistics from the ground up to an advanced level, am familiar with relevant research - then do I really want to call myself a data scientist (or ML-something) just because it might improve my job/salary prospects, or do I want to stay away from it because everyone who takes a one-week course on Udemy calls themselves a data scientist without being able to back it up with actual skills?

Having been subject to a lot of buzzword-blarers over the last term I'd say that you just tell everybody that you are working the foundations of what all the other people are doing if they ask, what makes you special. If you can back it up, why not ride the wave. I would refrain from putting the sticker on everything though. I als made a turnaround and now spin everything I do which takes time (basically automating my research and data-vis) as building blocks for (gradschool) ML-based research.

> 50% off the people are just showoffs throwing buzzwords and positivity around while they produce a load of sh...

Better get on this train because 10 years after undergraduate I can say it seems that’s all there is out there. Once you make it then maybe you don’t have to be full of sh but I don’t see people making it on merit unless they’re Albert Einstein.

That supervisor is someone who "made it in academia" so it might be good to not sneer and cringe at them and your peers.

I know. But basically at this point I'd say that a large portion of the academic output is crap even in "hard" science.

Also when looking at experiments (did that too in a lab course, where I got data from an existing apparatus): "Interpolating" data with a spline. "No, your result is not good, I get different result". "Maybe you should get more data then". "No, data's good, we need different result, also colormap is bad, use same like Matlab". "Well, the matlab colormap is colorful but not true to reality". "No, I see interesting things in plot with Matlab colormap". Stopped arguing, went back home, used jet instead of viridis and smoothed the spline. Got an A+. What a great day!

And while I might be not the brightest guy around and thus might not be able to just run around spitting out solutions for hard problems I have certain standards on integrity. Basically faking results is something I won't do to create (optional, published) work (which a paper is for undergrad work and also was for a PhD until not too long ago here...).

Luckily the academic system will typically weed out unethical behaviour (e.g. faking results).

If you think the current process does, you either are in a very good environment or are plainly lying to yourself. I could just add some bits here and there, get excellent results and publish. Probably nobody will notice as it won't be a "big issue" and seems all fine with existing experimental data.

If you've faked your data to get these results then it could mean the end of your career, hence must be a very uncommon practice. This is a different from cherrypicking data, or putting a positive spin on your findings. But again, it's expected now that data and code are open, so you wont get away with much these days in high-impact conferences/journals.

Not talking about high impact journals (where the majority of papers does not end up, when everyone is expected to publish twice a year...). Also I think cherrypicking your ML training data to achieve a certain result is akin to faking results?! And if you think that all data and code are open today you should certainly take a better look around you. Half of our lab is running on proprietary (simulation) software, good luck with auditing the code there (and evaluating the data because most of the people don't really document there workflow...). And both these examples take/took place on upper-mid-tiere european universities which consistently rank in the global upper 100/european upper 20-citation rankings...

Besides that, most of the people do a PhD for getting better paid jobs, not academic tenure (where such things could backfire).

regression is not a black box.

no, but using ML for what is basically regression and then telling everyone who asks: "I don't know, how it works, it's advanced ML" is "using it like a black box"

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