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This is more about ML than CS. ML is fundamentally about developing general-purpose algorithms applicable to a wide range of problems. If your job is using ML t

This is more about ML than CS. ML is fundamentally about developing general-purpose algorithms applicable to a wide range of problems. If your job is using ML to solve problems in chemistry, it's more about chemistry than ML, and a chemistry background is more important than an ML background. It's unlikely that you have to develop novel ML methods for the problems you are facing. I've seen the opposite in bioinformatics. While dedicated bioinformatics programs are now common, you still see many CS / mathematics / statistics / physics / EE people moving to bioinformatics after bachelor's / masters's / PhD / postdoc. In some bioinformatics jobs, you often have to solve new computational problems, and it's easier to teach enough biology to people with a methodological background than the other way around.

a_bonobo 4y ago
I've seen both in bioinformatics, because the field is so wide now. 1) Bioinformatics as tool-building, algorithm-dev: you're right, you don't need to know much biology there if the problem is defined well. 2) Bioinformatics as a tool to answer biological questions: here I've seen ML-background people really struggle, either developing stuff that's not useful or reinventing-the-wheel-but-now-it's-deep-learning. I've seen ML people present their fancy plant disease image detector which turned out to be pretty good at spotting 'yellow' - very good at training accuracy and benchmarks, does not add anything to what people in the field are doing.
- qumpis 4y ago
  In regards to 2), it sounds a bit directionless to be proposing stuff people don't need? Isn't that more of a problem of selecting relevant problems to solve, and getting supervision on your ideas?
  - a_bonobo 4y ago
    Yeah that's the problem! The ML people and the biology or agriculture people don't talk, they're not in the same building. A biologist might see the ML-person's work only after it's published.

a_bonobo 4y ago

I've seen both in bioinformatics, because the field is so wide now. 1) Bioinformatics as tool-building, algorithm-dev: you're right, you don't need to know much biology there if the problem is defined well. 2) Bioinformatics as a tool to answer biological questions: here I've seen ML-background people really struggle, either developing stuff that's not useful or reinventing-the-wheel-but-now-it's-deep-learning. I've seen ML people present their fancy plant disease image detector which turned out to be pretty good at spotting 'yellow' - very good at training accuracy and benchmarks, does not add anything to what people in the field are doing.

qumpis 4y ago
In regards to 2), it sounds a bit directionless to be proposing stuff people don't need? Isn't that more of a problem of selecting relevant problems to solve, and getting supervision on your ideas?
- a_bonobo 4y ago
  Yeah that's the problem! The ML people and the biology or agriculture people don't talk, they're not in the same building. A biologist might see the ML-person's work only after it's published.