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Once you've plugged your data into an ML system and gives you a classifier, are you done? What if the results are not good enough, do you just move on to anothe
by Eridrus 10y ago
Once you've plugged your data into an ML system and gives you a classifier, are you done? What if the results are not good enough, do you just move on to another problem?
If you don't just move on to another problem you will need people who know how to push these systems further.
- Namrog84 10y agoBut at some point, there is a difference between turning dials of the systems vs implementing new novel ways of these systems. Obviously we always need new novel approaches, but how much of that should stay in phd/academic/research world and how much spreads out into engineering day to day activities? I am rather newbish in the whole space, but most everything I've seen is quite often just knowing enough to turn the right dials the right way. And I do wonder how long will it be before some of these dials can start turning themselves with more iterations with a simple, "yes that's what I want, or no that's what I don't want" Sure, it might take some more cycles for it to find optimal in this example.
- Eridrus 10y agoIt all comes down to ROI of going further; if the ROI is small, people won't go much further than twiddling some knobs. I think there's a reason you see people throwing a lot of analysis at finance. Understanding what models are doing and adding new features/data transformations is standard work for ML practitioners now. If you believe the deep learning hype, architecture engineering is the new feature engineering, and to the extent that that is true, industry will essentially resemble academia to some extent. Maybe not making anything groundbreaking, but mixing ideas together.