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AAAS: Machine learning 'causing science crisis'

bbc.co.uk

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Re: AAAS: Machine learning 'causing science crisis'

#21

Post by willj »

Curious (possibly naive) question: isn't there a fundamental difference between the goals behind creating models with ML vs the "old-fashioned" way? That is, in modern ML applications, you're creating a model with dozens/hundreds of potential variables, without a hypothesis of how they relate or contribute to the target (other than that they might, hence your including them in the modeling process). You're using the model for predictions more than for explainability (though there is work ongoing into improving explainability, but it seems kind of post hoc to me). And there's an expectation that you will retrain, or at least tune, the model as its predictive accuracy decays over time.

By contrast, traditionally in science you're coming in with a hypothesis ahead of time about what variables predict what target. The goal is to come up with a model that is consistent with your hypothesis (and possibly some existing theory), and which can be applied generally, and which should need no tuning. For example, the very simple model for Beer's Law-- absorbance vs concentration. That is a law that will apply in every other circumstance, but if modern ML methods had been applied, the scientist might have chosen the model with a slightly better score but which includes nonsense variables in addition to concentration.

All that to say, it seems to me the problem stems from scientists' lack of hypotheses at the outset of a project, and/or the understandable desire to get the best bang for their buck out of an experiment by measuring dozens of variables at once and hoping the magic of ML can find a hypothesis for them.

Hope that made sense.

Re: AAAS: Machine learning 'causing science crisis'

#22

Post by rocqua »

hobofan wrote: post #3

ML is not causing a reproducibility crisis, it just exposes one that is already there. > If we had an additional dataset would we see the same scientific discovery or principle on the same dataset? The same holds true for traditional science based on traditional statistics. It just seems that traditional datasets are under less scrutiny of reproducibility and are taken more easily at face value.

A specific issue with machine learning is overfitting and non-interpretability.

The first means it is possible to get results that don't generalize (even if they survive cross validation). The second means it is a lot harder to detect use of correlations that cannot possibly be causal.

Re: AAAS: Machine learning 'causing science crisis'

#23

Post by alexgmcm »

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…

This is the real issue.

Getting data is always expensive in any field so it's much easier to analyse data that already exists as you don't need a large grant application.

Furthermore, I think the use of certain ML techniques may be akin to resume-driven development particularly for PhD students given that the career prospects in Data Science in industry (and using the HR buzzwords like AI, ML, Deep Learning etc.) are much better than the thin pickings that remain in academia in many fields.

Re: AAAS: Machine learning 'causing science crisis'

#24

Post by rocqua »

harry8 wrote: post #12
boomskats wrote: post #2

Good read. It's also refreshing to see a mainstream article that talks about ML without once mentioning 'AI'.

ML is statistics with a different name using a computer, that should always be mentioned in articles for the general public. On the other hand AI is fantasy BS hype boosting off the fact that ML sounds similar to AI to people who aren't aware Machine Learning is stats. Maybe AI one day but today it is utterly ridiculous. No really. Every single article should mention both of those things at least in passing. Downvote…

I'd say currently ML is heuristics done by a computer. The rigour of statistics isn't quite present in ML yet. At least, not in the basic courses yet.

Re: AAAS: Machine learning 'causing science crisis'

#25

Post by fock »

e_carra wrote: post #5

I wonder: don't machine learning frameworks' results come with a level of confidence? Ps: I have no experience with anything regarding ML.

Yeah, as most people here wrote already, it's statistics. And your models will yield those statistics...

Unfortunately if you're an engineer/physicist/chemist/biologist/social-scientist your background in statistics is neither fresh nor deep. So your professor comes to you: can you do something with ML, it's such a hot topic (your boss has also no background in statistics nor do his peers (which review your stuff...)) you say: yes (because a no I don't know about it won't be good for you). Then you go to some google or blockchain sponsored-tutorial where some self-taught-Indian-CS-Bachelor is telling you how to use ML with Python and Tensorflow. You might wonder about some things but in the end you need to get things done and feed your data (which is often garbage, but verifying that it's not is not hot) into an algorithm you don't understand. Then you find some other guys, doing this, cite them and publish. 0 scientific value generated, but a great step for your academic carrer nonetheless.

Re: AAAS: Machine learning 'causing science crisis'

#26

Post by altairiumblue »

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…

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?

Re: AAAS: Machine learning 'causing science crisis'

#27

Post by SuoDuanDao »

e_carra wrote: post #5

I wonder: don't machine learning frameworks' results come with a level of confidence? Ps: I have no experience with anything regarding ML.

They should, it shouldn't be difficult. I think the issue is more that ML gives more tools that a reasearcher could employ to get any arbitrary result if they knew what they are looking for.

Re: AAAS: Machine learning 'causing science crisis'

#28

Post by fock »

willj wrote: post #21

Curious (possibly naive) question: isn't there a fundamental difference between the goals behind creating models with ML vs the "old-fashioned" way? That is, in modern ML applications, you're creating a model with dozens/hundreds of potential variables, without a hypothesis of how they relate or contribute to the target (other than that they might, hence your including them in the modeling process). You're using the…

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 extracting that data are domain-specific and might yield results different on the order of magnitudes. But nobody cares; take some SW off the net, which claims to be able to extract what you want, run it, train your ML, publish your results.

Re: AAAS: Machine learning 'causing science crisis'

#29

Post by paraschopra »

Science works because it posits models first, and then data is sought to confirm or disconfirm it. The benefit of having a model first is that it is much more likely to be general (and hence reproducible).

ML does completely opposite. Data first, and then the model is discovered using data. It's pretty easy to see why it would lead to non-reproducible models.

Re: AAAS: Machine learning 'causing science crisis'

#30

Post by Waterluvian »

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."
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