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Machine learning is too often perceived as a kind of oracle-like power capable of predicting the unknowable and working out of distribution.
Lots of casual proposals that amount to: "what if we trained a text classifier on past biology research papers to predict those that went on to win awards, then we used it to select the next award winners for our prize in physics?"
when it is actually like a regular ETL job with some extra effort on the transformations
yes sure man
And many practitioners are not helping to dispel this myth.
Want to tell us what the results were?
It's the Great Interpolator
Sounds like a description of Bayesianism. :-)
Almost all other scientific fields use a definition of generalizability based on external validation results (out of distribution). If it's not intended, ML (and even mostly DL) should properly align this definition to receive a realistic perception from other fields.
AI will be helpful in accessing the vast knowledge database in a more direct and efficient way. That's how I see it.
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