great to read your blog, quite a pretty job you are doing!
The issue you raise about machine learning is really true. When I first heard about machine learning and physics, I thought that by definition it is the very contrary of science. Science is about using our intelligence to nail down some specific observations to a simple model that starts from a given fact and gets to the observation. For instance: I could train my machine-learning algorithm by observing the tides of several see locations around the world and probably at the end it would be able to predict the tides of a given uncharted location. This might be great from an engineering viewpoint: as a scientist, I think that we understand more about tides once we make the connection with the Moon, the Sun, the Newtonian gravity and the laws of water flow. And we will not be significantly wiser, without understanding that!
However, the more I think about it, the more I have the impression that machine learning is just another numerical tool. We can use it to guide our intuition, but at the end we need to prove the results it gives us, at least partially. I do my research activity in condensed-matter, and I’m fine if machine learning tells to my colleague (I don’t do that) that in a condensed-matter model there are three very exotic quantum phases of matter, but then he (we?) should be able to understand those phases and link them with pen and paper to the original model. I feel that only at that stage we have produced knowledge. I feel that only at that stage we have identified the key properties of the model, and that somebody could use them to see what happens when I perturb the model by adding, for instance, an electromagnetic field. Many people might be happy with the machine-learning result, I feel it is just a hint.
I have an observation concerning the point where you say: “At its core, science has always been about convincing people. The rigorous web of logical deductions that constitutes the backbone of math and of all exact sciences, is –ultimately- a rhetorical device.” I have the impression that here you are biased by your research on black holes in complex astrophysical objects. I think that science is about producing models that are able to predict the behavior of nature under certain conditions. It’s not just about rhetoric, it’s also about numbers and data!
Hi Mario,
great to read your blog, quite a pretty job you are doing!
The issue you raise about machine learning is really true. When I first heard about machine learning and physics, I thought that by definition it is the very contrary of science. Science is about using our intelligence to nail down some specific observations to a simple model that starts from a given fact and gets to the observation. For instance: I could train my machine-learning algorithm by observing the tides of several see locations around the world and probably at the end it would be able to predict the tides of a given uncharted location. This might be great from an engineering viewpoint: as a scientist, I think that we understand more about tides once we make the connection with the Moon, the Sun, the Newtonian gravity and the laws of water flow. And we will not be significantly wiser, without understanding that!
However, the more I think about it, the more I have the impression that machine learning is just another numerical tool. We can use it to guide our intuition, but at the end we need to prove the results it gives us, at least partially. I do my research activity in condensed-matter, and I’m fine if machine learning tells to my colleague (I don’t do that) that in a condensed-matter model there are three very exotic quantum phases of matter, but then he (we?) should be able to understand those phases and link them with pen and paper to the original model. I feel that only at that stage we have produced knowledge. I feel that only at that stage we have identified the key properties of the model, and that somebody could use them to see what happens when I perturb the model by adding, for instance, an electromagnetic field. Many people might be happy with the machine-learning result, I feel it is just a hint.
I have an observation concerning the point where you say: “At its core, science has always been about convincing people. The rigorous web of logical deductions that constitutes the backbone of math and of all exact sciences, is –ultimately- a rhetorical device.” I have the impression that here you are biased by your research on black holes in complex astrophysical objects. I think that science is about producing models that are able to predict the behavior of nature under certain conditions. It’s not just about rhetoric, it’s also about numbers and data!
Leonardo