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by ssivark
2883 days ago
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The impression I get is that many people want to be system designers stringing together pieces to create systems to solve problems (part of the motivation might be that it is easier to extract economic value from such integrated solutions, rather than better functioning pieces). The problem is that in an immature field that's still evolving, the components are not yet well-understood or well-designed, so available abstractions are all leaky. However, modern software engineering is mostly built on the ability to abstract away enormous complexity behind libraries, so that a developer who is plumbing/composing them together can ignore a lot of details [1]. People with that background now expect similarly effective abstractions for machine learning, but the truth is that machine learning is simply NOT at that level of maturity, and might take decades to get there. It is the price you pay for the thrill of working in a nascent field doing something genuinely uncharted. "Math in machine learning" is a bit of a red herring. We hear the same complaints about putting in effort to grok ideas in functional programming, thinking about hardware/physics details, understanding the effects of software on human systems [2], etc. Fundamentally, I think a lot of people have not developed the skill to fluidly move between different levels of abstraction, and a variety of approximately correct models. And to be fair, it seems like most of software engineering is basically blind to this, so one can't shift all the blame on individuals. [1] Why the MIT CS curriculum moved away from Scheme towards Python -- https://www.wisdomandwonder.com/link/2110/why-mit-switched-f... [2] Building software through REPL-it-till-it-works leads to implicitly ignoring important factors (such as ethics) -- https://news.ycombinator.com/item?id=16431008 |
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Deep learning, in particular, is a trade today. If we want to be generous we can call it an "experimental science"... but my perception is that only a minority of papers in the field actually deserve that moniker.
(Speaking as a deep learning practitioner with expertise in a narrow domain.)