Once we solve AutoML, the automated creation of machine learning pipelines, we can move on to focus on the art of loss function engineering
i.e. how to quantify a business goal as a scalar that can be readily computed from a model's output. Requires extensive domain knowledge
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Also requires experimentation - often only a successful A/B test tells if your loss function is appropriate
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Problems occur when businesses naively assign $ as that scalar and hit the optimize button.
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and they have not needed fancy ML to do that..
End of conversation
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Closer to the intelligence of corporations with fixed mandates vs people with lots of different and contradictory goals.
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why a scalar?
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