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That's because application and research are quite different. If one does a PhD in ML they learn how to research ML. Someone with a PhD in chemistry learns how t
by kevviiinn 3y ago
That's because application and research are quite different. If one does a PhD in ML they learn how to research ML. Someone with a PhD in chemistry learns how to research chemistry, they only need to apply ML to that research
- ghaff 3y agoBack when O'Reilly was still hosting events (sigh), at one of their AI conferences, someone from Google gave a talk about differences between research/academic AI and applied AI. I think she had a PhD in the field herself but basically she made the argument that someone who is just looking to more or less apply existing tools to business or other problems mostly doesn't need a lot of the math-heavy theory you'll get in a PhD program. You do need to understand limitations etc. of tools and techniques. But that's different from the kind of novel investigation that's needed to get a PhD.
- frozenport 3y ago>>math-heavy theory you'll get in a PhD program Lol. With the exception of niche groups in compressed sensing, math doesn't get too hard. Furthermore, ML isn't math driven in the sense people are trying things and somebody tries to come up with the explanation after the fact.
- selimthegrim 3y agoWell I think the issue is more of if you’re Genentech and you need ML people and can’t afford to pay them you’re probably better off retraining chemistry PhDs.
- kevviiinn 3y agoI think you missed my point. Genentech, AFAIK, was not doing research on machine learning as in the principles of how machine learning works and how to make it better. They do biotech research which uses applied machine learning. You don't need a PhD in ML to apply things that are already known
- cmavvv 3y agoAs a PhD student working on core ML methods with applications in chemistry, I second this. During my PhD, I read very few papers by chemists that were exciting from a ML perspective. Some work very well, but the chemists don't even seem to always understand why they made the right choice for a specific problem. I don't claim that the opposite is easy either. Chemistry is really difficult, and I understand very little.
- dekhn 3y agoGenentech has several ML groups that do mostly applied work, but some do fairly deep research into the model design itself, rather than just applying off-the-shelf systems. For example, they acquired Prescient Design which builds fairly sophisticated protein models (https://nips.cc/Conferences/2022/ScheduleMultitrack?event=59064 https://nips.cc/Conferences/2022/ScheduleMultitrack?event=59...) and one of the coauthors is the head of Genentech Research (which itself is very similar to Google Research/Brain/DeepMind), and came from the Broad Institute having done ML for decades ('before it was cool'). They have a few other groups as well (https://nips.cc/Conferences/2022/ScheduleMultitrack?event=60268 https://nips.cc/Conferences/2022/ScheduleMultitrack?event=60... and https://neurips.cc/Conferences/2022/ScheduleMultitrack?event=60041 https://neurips.cc/Conferences/2022/ScheduleMultitrack?event... and https://neurips.cc/Conferences/2022/ScheduleMultitrack?event=57996 https://neurips.cc/Conferences/2022/ScheduleMultitrack?event...). I can't say I know anybody there who is doing what I would describe as truly pure research into ML; it's not in the DNA of the company (so to speak) to do that.
- gowld 3y agoYou can get an ML PhD doing applied ML.
- sdenton4 3y agoSadly a lot of foundational ML research works for single-label image classification and not much else. ImageNet is a niche problem and way too much ML research is over-indexed on it. If you can make your problem look like ImageNet, you're going to do OK, but if not you effectively need to re-invent the wheel...
- kgwgk 3y agoWhat if they don’t need “ML people”? Computational biology has been a thing for a while.
- selimthegrim 3y agoWell they had a whole suite of presentations at NeurIPS that suggests they hired a bunch.