For example, in a complex world one should give up explain-ability (the main goal in classical science) to gain a better predict-ability.
This sounds a lot like True vs. Useful, again.
(Of course it’s a bit redundant to call it “machine” learning, since we are learning machines, and there’s little reason to assume that we don’t learn using mechanical processes optimized for multi-factor matching. And that would tend to explain why learning and skills don’t always transfer well between Theory and Practice.)
This sounds a lot like True vs. Useful, again.
(Of course it’s a bit redundant to call it “machine” learning, since we are learning machines, and there’s little reason to assume that we don’t learn using mechanical processes optimized for multi-factor matching. And that would tend to explain why learning and skills don’t always transfer well between Theory and Practice.)