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My frank opinion as someone ~2 years into grad school in something to do with computers and statistical inference (taken a bunch of numerical classes, ML course

My frank opinion as someone ~2 years into grad school in something to do with computers and statistical inference (taken a bunch of numerical classes, ML courses, maybe ~1 year of grad level stats coursework): if you actually want to do statistical analysis for a living, you need get a statistics degree. CS people are irrelevant except for computational issues. As a CS guy probably you can be useful for scaling a system to do automated inference, or as an advanced database guy, or writing shell scripts or something. If you go to grad school in CS and work hard in the right subfield you might develop the understanding of someone with part of a stats BS. Again, the part you would actually know something about is computation, which as far as I can tell isn’t a real bottleneck for anything except very large automated systems. Otherwise some guy can just use R and his laptop. I mentioned my status above because this is solely based on me evaluating things in an academic setting and not job experience, but this seems very clear to me once laid out like above. ML is “hot” but it seems to me like an extraordinarily specialized area of expertise with little general applicability.

mistrial9 7y ago
agree but -- previous eras of statistics used important and non-obvious ways to infer from subsets of data.. now, whole sets are used, and the subset part is more like a representation problem. Second, the things that are made to be important in school, are not always important outside of school. You do acknowledge that tacitly there.. The point of view in the post here is from an individual, regarding the work of another (hypothetical) individual, while the paper linked spends quite a lot of effort to characterize in the real world, real activity by large and very large, organizations, mainly business, and mainly the business of money. You conclude that the field has "little general applicability" and that might be true in some sense.. but large organizations, and particularly large finance organizations, are not general at all, but rather composed of a very large number of specialized and repetitive operations.. exactly what ML and AI perform, no?

mistrial9 7y ago

agree but -- previous eras of statistics used important and non-obvious ways to infer from subsets of data.. now, whole sets are used, and the subset part is more like a representation problem. Second, the things that are made to be important in school, are not always important outside of school. You do acknowledge that tacitly there.. The point of view in the post here is from an individual, regarding the work of another (hypothetical) individual, while the paper linked spends quite a lot of effort to characterize in the real world, real activity by large and very large, organizations, mainly business, and mainly the business of money. You conclude that the field has "little general applicability" and that might be true in some sense.. but large organizations, and particularly large finance organizations, are not general at all, but rather composed of a very large number of specialized and repetitive operations.. exactly what ML and AI perform, no?