A lot of ML research is done by PhDs in other fields. I did research that focused on developing compact group invariant features (for neural networks) for predicting local atomic energies in materials science, and a few mathematicians I follow did work on developing convolutional neural networks that utilize Clebsch–Gordan coefficients to generalize the translational invariance to other symmetries.
On the contrary, a lot of CS machine learning research is heavily application focused (generate such-and-such new thing using a GAN). If anything, mathematicians are the ones who understand machine learning at the very deepest level of theory. This isn't to say there aren't many theory-focused CS presentations/publications; I'm just refuting your point that highly theoretical machine learning research is purely the domain of CS PhDs.
Sure, but most problems aren't life-or-death. They're mundane problems related to improving a business process for a widget manufacturer, etc.
I rather have people have a PhD in logic or ethics, so they hopefully wont make for example racist programs without even thinking about it. Unfortunately, so far i dont have a lot of faith in computer science as a field when it comes to ethics, as it's all about the technical challenge. That something can be build does not mean it should be build.
Interesting. I don't see anything in the story above that supports that position. There's nothing about the scenario you described that would be affected by having, or not having, a Phd in CS. If anything, as somebody else pointed out, this is more of question of philosophy or ethics.
It's also a pretty niche example, which is not representative of the kinds of things that ML can be used for. If you want somebody who work on pricing optimization or a recommender system for your e-commerce site, you really don't need somebody who's doing cutting edge ML research.
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I had someone contact me for a "consulting" position, to replace their rule-based insurance claim denial system, with "ML". Contract-work, nobody in-house to review its performance, no insight to the problems of the domain and they wanted to throw ML at it to "solve their problem".
When I refused, and pointed out that it would be highly irresponsible, to "contract-out" this type of work, particularly given its life-or-death implications; the CEO got angry. Told me; its "post-claims processing" and it isn't life or death. To which I told him bullshit; your denial of claims is going to directly influence how doctors practice their treatment. There is a direct feedback cycle. The fact that you don't see it, makes it even more dangerous.
They simply, didn't understand, how HIGHLY inappropriate it was to just throw random ML at a problem, particularly as a one-off consulting project (and no in-house expertise).
^ thats real-world.
Personally I don't think you should be allowed anywhere near ML, UNLESS you have that PHD in computer science. I don't even think you should be allowed to HIRE people for ML until you fully understand the hazards with letting a computer control critical decisions.
So yeah, with respect to your distinction of "ML research" and "applied ML".
"NO."