Here is a question: do you think ML models will ever replace scientific theory, especially those areas of science where the human brain may be inherently limited to explore? In other words, instead of descriptive equations showing how dark energy works, could ML take its place without the need for us to explain how dark energy exactly works, especially if we can make accurate predictions using that ML model about dark energy?
Replace theory -> No, at least not completely in the near future. But it many areas we might acknowledge more and more that phenomenons can't be explained by interpretable equations (like a linear model).
I see science as becoming more accepting that due to interactions and non-linearities we won't be able to "understand" all relations. But machine learning models can often approximate such relations really well and become part of the science toolkit.
Christoph - thank you for your thoughtful response.
Would you say that there is a resistance in the scientific community for Replace Theory? Also, do you think that the physics of the cosmos is statistical in itself or determined by laws with perfect mechanistic relations?
I'm not familiar with state of the art of modeling physics of the cosmos, so can't say.
About resistance: when people argue not to use ML because "it's a black box" and "it's not interpretable", then I would see that as a resistance against ML, so yes you can observe that. But mostly I think it's a tendency to sticking to the scientific methods that have always been used in a field. change always happens slowly
Predictive models teach us a lot about the system we are observing. My fair value bitcoin model does a poor job of predicting price, but it teaches us a lot about the human emotions behind market prices.
Great project! I think one of the central questions is, what are scientific theories for? Is science "just" about prediction, or is it also about explanation, and how are these related? And how is "explaining the ML model" related to "explaining the world" (ie, what science is supposed to strive for)? Cf. the discussion by Henk de Regt on eplanation, and the use Mario Krenn et al make of this concerning the role of AI in science (https://doi.org/10.1038/s42254-022-00518-3).
That paper wasn't on my radar. That's a perfect reference, thanks a lot for sharing Rainer! Will dive right in. If you have more references like this, don't hold back 😉
Great take!
Here is a question: do you think ML models will ever replace scientific theory, especially those areas of science where the human brain may be inherently limited to explore? In other words, instead of descriptive equations showing how dark energy works, could ML take its place without the need for us to explain how dark energy exactly works, especially if we can make accurate predictions using that ML model about dark energy?
Great question!
Replace theory -> No, at least not completely in the near future. But it many areas we might acknowledge more and more that phenomenons can't be explained by interpretable equations (like a linear model).
I see science as becoming more accepting that due to interactions and non-linearities we won't be able to "understand" all relations. But machine learning models can often approximate such relations really well and become part of the science toolkit.
Christoph - thank you for your thoughtful response.
Would you say that there is a resistance in the scientific community for Replace Theory? Also, do you think that the physics of the cosmos is statistical in itself or determined by laws with perfect mechanistic relations?
I'm not familiar with state of the art of modeling physics of the cosmos, so can't say.
About resistance: when people argue not to use ML because "it's a black box" and "it's not interpretable", then I would see that as a resistance against ML, so yes you can observe that. But mostly I think it's a tendency to sticking to the scientific methods that have always been used in a field. change always happens slowly
It will be exciting to watch for sure.
Looking forward to your book and future newsletters!
Predictive models teach us a lot about the system we are observing. My fair value bitcoin model does a poor job of predicting price, but it teaches us a lot about the human emotions behind market prices.
Great project! I think one of the central questions is, what are scientific theories for? Is science "just" about prediction, or is it also about explanation, and how are these related? And how is "explaining the ML model" related to "explaining the world" (ie, what science is supposed to strive for)? Cf. the discussion by Henk de Regt on eplanation, and the use Mario Krenn et al make of this concerning the role of AI in science (https://doi.org/10.1038/s42254-022-00518-3).
That paper wasn't on my radar. That's a perfect reference, thanks a lot for sharing Rainer! Will dive right in. If you have more references like this, don't hold back 😉