I feel returning to three models for thinking about machine learning: automation, relational databases and interns. 1/
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If steam power said ‘what could we do with millions more horsepower?’ maybe ML is ‘what could you do with a million interns?’ </>
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ML is far less adaptable, robust and versatile than any number of interns. But when it works, it's a lot faster.
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But with time, it will become everything and faster
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Just like horses and steam engines
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For thought, the steam engine made such a difference: no steam engine, no train, no train no theory of relativity: what will ML enable?
End of conversation
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