Sure, our models have close to no intelligence. They're just automating what we've trained them to do. But that power makes them extremely useful in the real world. Just not a panacea.
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I see too many people taking the stance "ML does not magically solve every problem, therefore we shouldn't use it". Instead, engineers in every industry should be asking, "what can I do with ML?", while keeping in mind its limitations. Turns out, there's *a lot* you can do.
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I don't think that's ever been the source of (reasonable) criticism. The argument is moral: if machine learning researchers overhype their findings, they contribute to a (resource assignment) dynamic that maximizes short-term returns at the expense of long-run scientific progress
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Sure it's useful. But you should remember that all is reactionary as we have seen by ML researchers that "AI is electricity" and "DL is Software 2.0" claims from **far-sighted** ML researchers.
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Even here super-human accuracy is an ill-defined term. ML means/promotes mediocracy. Yeah, that has some uses for sure.
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I literally work on replacing human led audits in retail using relatively simple neural networks. The only reason we survive is because our system is significantly more accurate than humans at the same task (and because we can do it about 21x more often).
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My problem isnt with ML it is with the lack of sufficient GI models that people who use ML have. when you model GI you'll see ML currently doesnt build 4d memory schema that mirror our own. It doesn't attend, observe, memorise, generalise, bootstrap, recall like we do.
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I've been building an OCR system lately and it routinely generalizes to fonts and lighting conditions it hasn't encountered before. As far as I can tell this is only possible for me to do using deep learning.
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Agreed! It's pretty much the same insanity as saying "we shouldn't build any machine because they are only capable of doing what they were built for..."
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One heck of a label
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Čini se da učitavanje traje već neko vrijeme.
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