there are a number of features in machine learning that seem to have counterparts in statistics - stuff like m-like estimators as kernels (I think - correct me if wrong), information criteria for feature selection, that have existed a long time ago.<p>maybe it&#x27;s the combination of sheer computing power and availability of data that allows better models. maybe we&#x27;ve never looked at algorithmically generated models because we also want a narrative (commonsensical explanation) to the model, not just a matter of algorithmically finding correlations between jelly beans and acne, say.<p>part of me thinks, ok the machines found something. now can we actually use that to understand the world, rather than build more recommendation algorithms? (haha). I&#x27;m not sure what an advisor will say about a doc student who says, let&#x27;s just throw reams of data at a machine until we find a meaningful correlation, and then let&#x27;s reason from the correlations (my guess is &#x27;no&#x27;, that&#x27;s not really the scientific method is it).<p>I&#x27;m hopeful, and I don&#x27;t think the answer to the question will come from academia.