I'm as enthusiastic about the future of AI as (almost) anyone, but I would estimate I've created 1000X more value from careful manual analysis of a few high quality data sets than I have from all the fancy ML models I've trained combined.
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It's worth reflecting on why this might be the case. I think often when we generate value from data, it's a result of a *dialog* we have with it. As opposed to knowing the exact question in advance and setting some precise process in motion to answer it.
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I think that's the key: we don't often know the right question to ask when we start a line of empirical inquiry. We refine some initial vague set of question as we interact with data, answer some of them, and generate new ones.
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I'm sure AI systems will eventually be capable of working interactively with humans to mimic and streamline this process. For now, mucking about with simple tools in a curiosity-driven dialog with our data is the best we've got.
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You know who was remarkably prescient about this exact problem of thinking computers could solve all our epistemological problems? Deep Thought: 42 Earth: "What do you get if you multiply six by nine?"
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I guess in some ways you're saying preregistration isn't applicable to computational/statistical modelling?
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