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> Personally I don't think you should be allowed anywhere near ML, UNLESS you have that PHD in computer science A lot of ML research is done by PhDs in other f

> Personally I don't think you should be allowed anywhere near ML, UNLESS you have that PHD in computer science 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.

jacobolus 8y ago
This seems like it misses the point. Obviously there are professional numerical analysts, statisticians, mathematical physicists, etc. who have sufficient background and interest to keep up with cutting edge research and do solid work in machine learning. The argument is not that everyone needs a CS degree per se, but rather that you shouldn’t have your excel guy who just went through a machine learning MOOC but has no further training or deeper understanding try to apply machine learning to life-or-death problems.
- mindcrime 8y ago
  shouldn’t have your excel guy who just went through a machine learning MOOC but has no further training or deeper understanding try to apply machine learning to life-or-death problems. Sure, but most problems aren't life-or-death. They're mundane problems related to improving a business process for a widget manufacturer, etc.
paulgrant999 8y ago
well logically, I can't make the statement a PHD is required; because a PHD, doesn't mean you know ML. So you can consider the comp sci a "fuzzy" field denominator indicating a heavy theoretical familiarity with the math behind computer science. > I'm just refuting your point that highly theoretical machine learning research is purely the domain of CS PhDs. actually I don't think ML is enough in and of itself; I also prefer a heavy domain-specific expertise. ;) but that was outside the scope of the original comment. in truth, the PHD doesn't guarantee intelligence; it gives you a track record to see what the person has been up to i.e. it is necessary, but not sufficient. I talked to a PHD doing superconductivity research where I could put together stuff he hadn't even thought of (in seven years of study!). Then I talked to a post-doc doing CFD simulation of certain physical systems, who had discovered a very very neat trick to speeding up computation (by orders of magnitude). The two could not be compared in quality. I would trust the latter (with one-sentence (or for particularly subtle shit, two sentences)) to immediately understand the implications; the former? You could spoon-feed him all night long and he would barely be able to decipher the sentence, much less the implications. Overall arching point: if you think you know ML, and haven't used it/studied it extensively, you don't know ML. You simply don't have the critical knowledge base of edge/corner cases to detect when your ML is giving you crappy results (and why; or more importantly, how to OR IF YOU CAN, remediate). > On the contrary, a lot of CS research on neural networks is heavily application focused Grad-level? its crap. I spent a couple of months refreshing the knowledge base on computing (reading papers), and lord it was like wading through a river of shit. And just when I was thinking this, about two days later I read a paper from some grad who basically said the same exact shit ("enough with the nature-anologies"). Couple of gems every now and then, but few and far between. Mostly derivative shit (where the person who wrote the paper was either desperate to get published; or didn't understand the problem domain sufficiently to see why there solution was computationally-equivalent to a prior solution). PHD's theres a slight higher increase in quality; but where they generally shine, is in the breadth of their knowledge-base. The extra time contemplating shit, makes a vast difference. To be frank, I find the masters from a top school, to be equivalent to a sophomore-junior year bachelors. or possibly a remedial (senior-year). PHD depends on the aptitude of the student, and the selection-quality of their research. You can either rock it, or coast. > If anything, mathematicians are the people who understand machine learning at the very deepest level. Good ones, yeah. Don't know the frequency of'em, because I never took a math degree. -- now compare and contrast this, with the positions held by the "applied ML" crowd. - sorry I couldn't reply. "posting too fast" <rolls eyes>

jacobolus 8y ago

This seems like it misses the point. Obviously there are professional numerical analysts, statisticians, mathematical physicists, etc. who have sufficient background and interest to keep up with cutting edge research and do solid work in machine learning. The argument is not that everyone needs a CS degree per se, but rather that you shouldn’t have your excel guy who just went through a machine learning MOOC but has no further training or deeper understanding try to apply machine learning to life-or-death problems.

mindcrime 8y ago
shouldn’t have your excel guy who just went through a machine learning MOOC but has no further training or deeper understanding try to apply machine learning to life-or-death problems. Sure, but most problems aren't life-or-death. They're mundane problems related to improving a business process for a widget manufacturer, etc.

paulgrant999 8y ago

well logically, I can't make the statement a PHD is required; because a PHD, doesn't mean you know ML. So you can consider the comp sci a "fuzzy" field denominator indicating a heavy theoretical familiarity with the math behind computer science. > I'm just refuting your point that highly theoretical machine learning research is purely the domain of CS PhDs. actually I don't think ML is enough in and of itself; I also prefer a heavy domain-specific expertise. ;) but that was outside the scope of the original comment. in truth, the PHD doesn't guarantee intelligence; it gives you a track record to see what the person has been up to i.e. it is necessary, but not sufficient. I talked to a PHD doing superconductivity research where I could put together stuff he hadn't even thought of (in seven years of study!). Then I talked to a post-doc doing CFD simulation of certain physical systems, who had discovered a very very neat trick to speeding up computation (by orders of magnitude). The two could not be compared in quality. I would trust the latter (with one-sentence (or for particularly subtle shit, two sentences)) to immediately understand the implications; the former? You could spoon-feed him all night long and he would barely be able to decipher the sentence, much less the implications. Overall arching point: if you think you know ML, and haven't used it/studied it extensively, you don't know ML. You simply don't have the critical knowledge base of edge/corner cases to detect when your ML is giving you crappy results (and why; or more importantly, how to OR IF YOU CAN, remediate). > On the contrary, a lot of CS research on neural networks is heavily application focused Grad-level? its crap. I spent a couple of months refreshing the knowledge base on computing (reading papers), and lord it was like wading through a river of shit. And just when I was thinking this, about two days later I read a paper from some grad who basically said the same exact shit ("enough with the nature-anologies"). Couple of gems every now and then, but few and far between. Mostly derivative shit (where the person who wrote the paper was either desperate to get published; or didn't understand the problem domain sufficiently to see why there solution was computationally-equivalent to a prior solution). PHD's theres a slight higher increase in quality; but where they generally shine, is in the breadth of their knowledge-base. The extra time contemplating shit, makes a vast difference. To be frank, I find the masters from a top school, to be equivalent to a sophomore-junior year bachelors. or possibly a remedial (senior-year). PHD depends on the aptitude of the student, and the selection-quality of their research. You can either rock it, or coast. > If anything, mathematicians are the people who understand machine learning at the very deepest level. Good ones, yeah. Don't know the frequency of'em, because I never took a math degree. -- now compare and contrast this, with the positions held by the "applied ML" crowd. - sorry I couldn't reply. "posting too fast" <rolls eyes>