When we take formal rules and put them in a database (say the CA immigration system: it adds points. Doesn't matter by hand or computation) or even when we automate a fairly well-understood system (flying) we have ways of debugging/troubleshooting that we don't have for ML. +
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One (1) counter is that we'll eventually crack this, and have interpretable ML (See: https://twitter.com/andrewthesmart/status/1064341779816767488 …). Other counter (2) is that we have used black-box technologies before (that it produced behavior we wanted but we did not know how). (1) maybe. (2) not too many.
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But thanks, it's clearer to me what the disagreement is. I'll see if anyone has a long-form version of what I'm asserting (obviously, I think correctly) and what other historical black-box examples could compare (and also set out their limits).
End of conversation
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It's just not the same as say, adding more variables like heart rate, blood pressure, this and that measurement and running a regression or applying a formula.
