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AAAS: Machine learning 'causing science crisis'

bbc.co.uk

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Re: AAAS: Machine learning 'causing science crisis'

#41

Post by fock »

anjc wrote: post #33
fock wrote: post #7

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…

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...).

Re: AAAS: Machine learning 'causing science crisis'

#42

Post by fock »

ppod wrote: post #37
fock wrote: post #7

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…

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"

Re: AAAS: Machine learning 'causing science crisis'

#43

Post by anjc »

fock wrote: post #41
anjc wrote: post #33

Earlier quoted context omitted.

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…

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

Re: AAAS: Machine learning 'causing science crisis'

#44

Post by fock »

ppod wrote: post #36
fock wrote: post #28

Earlier quoted context omitted.

I think you got the point. A lot of people don't seem to realize that ML might be great for finding patterns but will never yield scientific knowledge in the sense of cause-reaction sense. Unfortunately everyone thinks he can use it for finding "new stuff" and so in my field they "predict material properties", etc. using ML fed with data where every review about the physics tells you that the algorithms they use for…

What method would you use to yield scientific knowledge in the sense of cause-reaction? Many important processes really do have large numbers of causal factors that interact non-linearly. If we want to try to learn about that, some statistical method that deals with many parameters will be needed. Such models are generally referred to as "Machine Learning". Their generalisation or causal inference properties are part…

A tool called Mathematics which can exactly describe this interactions. And if those processes have a lot of variables, a ML-model might certainly be useful, but it will never be generally applicable! This probably also contributes to "scientific knowledge" but it's not the same as scientific facts (or whatever you call universally transferable results).

Re: AAAS: Machine learning 'causing science crisis'

#45

Post by thomasz »

konschubert wrote: post #20
harry8 wrote: post #6

Fails to touch on the perverse incentives in academia, "publish or perish" etc. Torturing a dataset to find a p value that a journal will like (or equivalent stat measure) is better for your career than not publishing a paper that will be discredited in time. You have no incentive at all to decide "my results are unconvincing at this point, I'm not going to submit them" and every reason to write them up as a useful c…

Some days ago there was a post here on HN about a post doc who failed to get tenure. And every other comment was like: “What did he expect, he had much less than the usual two papers a year.” So it seems that even here on HN, the mindset of quantity over quality still persists.

Those were not normative statements.

Re: AAAS: Machine learning 'causing science crisis'

#46

Post by fock »

anjc wrote: post #43
fock wrote: post #41

Earlier quoted context omitted.

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…

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.

Re: AAAS: Machine learning 'causing science crisis'

#47

Post by baxtr »

konschubert wrote: post #20
harry8 wrote: post #6

Fails to touch on the perverse incentives in academia, "publish or perish" etc. Torturing a dataset to find a p value that a journal will like (or equivalent stat measure) is better for your career than not publishing a paper that will be discredited in time. You have no incentive at all to decide "my results are unconvincing at this point, I'm not going to submit them" and every reason to write them up as a useful c…

Some days ago there was a post here on HN about a post doc who failed to get tenure. And every other comment was like: “What did he expect, he had much less than the usual two papers a year.” So it seems that even here on HN, the mindset of quantity over quality still persists.

The problem really is: there is no alternative (which I know of). Having a number of papers in well known journals is everything we have to gauge the quality of people that look for a life time position. It’s sad but true

Re: AAAS: Machine learning 'causing science crisis'

#48

Post by parmegv »

Waterluvian wrote: post #30
harry8 wrote: post #6

Fails to touch on the perverse incentives in academia, "publish or perish" etc. Torturing a dataset to find a p value that a journal will like (or equivalent stat measure) is better for your career than not publishing a paper that will be discredited in time. You have no incentive at all to decide "my results are unconvincing at this point, I'm not going to submit them" and every reason to write them up as a useful c…

We need to find resources to fund "Failures in Science" journals that exclusively seek to publish interesting research that went nowhere. Personally I'd find these far more interesting to study. "Here's some background. Here's a pretty logical, plausible hypothesis we came up with and how, here's our experiment, here's our results, here's our thoughts as to why we were wildly wrong."

There is a new conference in cryptology that promotes exactly this: CFAIL.

From their 2019 website[0]: "Do you have insightful and exciting work sitting in a drawer somewhere because it never quite panned out? Are you willing to share your failed approaches so that others can learn from them without having to re-travel the same road? Are you tired of reading papers that pretend the incremental result they happened to achieve was well-motivated and was their goal all along?

We are! That’s why we are founding a new conference: a place for papers that describe instructive failures or not-yet-successes, as they may prefer to be called."

[0] https://www.cfail2019.com/

Re: AAAS: Machine learning 'causing science crisis'

#49

Post by anjc »

fock wrote: post #46
anjc wrote: post #43

Earlier quoted context omitted.

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.

Re: AAAS: Machine learning 'causing science crisis'

#50

Post by hannob »

kevcampb wrote: post #4

Is machine learning really to blame for the reproducibility crisis? I'm not in academia, but it seemed to me that the problem was entirely present without machine learning being involed. For example, Amgen reporting that of landmark cancer papers they reviewed, 47 of the 53 could not be replicated [1]. I would have assumed that most of them didn't involve 'machine learning' [1] https://www.reuters.com/article/us-scie…

The problem was there before, but there are reasons why Machine Learning is amplifying bad practices.

In the past people were manually fishing for results in available datasets. Now they have algorithms to do it for them.

In medicine a popular way to use ML is to improve diagnosis. Now there's already a problem in medicine that the benefits of early diagnosis are overrated and the downsides (overtreatment etc.) usually ignored. You get more of that.

And TBH computer scientists aren't exactly at the forefront when it comes to scientific quality standards. (E.g. practically noone is doing preregistration in CS, which in other fields is considered a prime tool to counter bad scientific practices.)

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