It depends whether the value of science is human understanding or pure prediction. In some realms (for drug discovery, and other situations where we just need an answer and know what works and what doesn’t), pure prediction is all we really need. But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics?
Maybe there’ll be an intermediate era for a while where ML models outperform traditional analytical science, but then eventually we’ll still be able to find the (hopefully limited in number) principles from which it can all be derived. I don’t think we’ll ever find that Occam’s razor is no use to us.
> But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics?
At that point I wonder if it would be possible to feed that uninterpretable model back into another model that makes sense of it all and outputs sets of equations that humans could understand.
The success of these ML models has me wondering if this is what Quantum Mechanics is. QM is notoriously difficult to interpret yet makes amazing predictions. Maybe wave functions are just really good at predicting system behavior but don't reflect the underlying way things work.
OTOH, Newtonian mechanics is great at predicting things under certain circumstances yet, in the same way, doesn't necessarily reflect the underlying mechanism of the system.
So maybe philosophers will eventually tell us the distinction we are trying to draw, although intuitive, isn't real
That’s what thermodynamics is - we initially only had laws about energy/heat flow, and only later we figured out how statistical particle movements cause these effects.
Pure prediction is only all we need if the total end-to-end process is predicted correctly - otherwise there could be pretty nasty traps (e.g., drug works perfectly for the target disease but does something unexpected elsewhere etc.).
> e.g., drug works perfectly for the target disease but does something unexpected elsewhere etc.
That's very common. It's the reason to test the new drug in petri dish, then rats, then dogs, then humans and if all test passed send it to the pharmacy.
Maybe there’ll be an intermediate era for a while where ML models outperform traditional analytical science, but then eventually we’ll still be able to find the (hopefully limited in number) principles from which it can all be derived. I don’t think we’ll ever find that Occam’s razor is no use to us.