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Comment by Q6T46nT668w6i3m | Hacker News Reader

How would you explain the rise (and success) of machine learning in science? A lab that uses some learning-based method will likely be limited to just one or two people (responsible for data acquisition, feature engineering, evaluation, etc.) and extremely finite data.

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It's not clear there has been any deep impact actually, but there has been a lot of discussion (and grant proposals)

I've seen a lot of cross pollination of ML and AI techniques into various disciplines. A large percentage just didn't work at all, most of the rest were more "kind of interesting, but". Nothing earthshaking happened although pop sci press likes to talk about it a lot.

If you have more digital data than you used to, using modern free frameworks and toolkits to do basic (i.e. older, boring, but understood) ML stuff to understand it seems to have a reasonable return. Mostly I think this is because it becomes accessible to someone without much background in the area, and you can do reasonable things without having to put 6 months of reading and implementing together before starting.

How do you define success? Adoption? Because right now, writing "we will use machine learning to solve X" in a grant proposal is an easy way to increase chances of getting funding.

I'm not sure there is a rise. 'Science' is a huge domain. Machine learning if I had to guess maybe plays a role in < 1% of them, and that may be overstating it.

Also it's doubtful to even categorize machine-learning as science. The goal of science is to generate insight and knowledge, ML solves particular engineering problems or searches problem spaces, it doesn't build fundamental scientific models.

Can you elaborate on what you mean by "A lab that uses some learning-based method will likely be limited to just one or two people (responsible for data acquisition, feature engineering, evaluation, etc.)" ? I know a bunch of labs that apply machine learning to specific tasks, and the parts you list each can easily take up multiple people for years for a single task - not counting data acquisition, because data is definitely not "extremely finite", you need lots of quality data, and improving data is something that always gets improvements and can easily eat up more manpower than you can have budget, no matter what that budget is.

...because previously, the academics would use an army of undergrads to do the same data labeling that ML accomplishes.

(The dis-economy of scale hurts less if you're already starting from a point with the manual labor.)

Its now a lot cheaper in the 1980's than when I worked at the worlds leading hydrodynamics orgs.

I briefly looked at using neural nets to analyse data from an experiment - analysing the efficacy of toilet bowl designs.

The entry level hardware was £250k in 1981 - it was much cheaper to take photo's and have a research assistant count squares.

Now you could use fairly cheap commodity hardware to do it.

It would have been an amazing cutting edge project if we could have got some government funding we did have an in-house knowledge engineer.

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