Miles Cranmer


"Rather than a prediction being hidden away in a neural network, this gives you a way of translating the neural network’s behavior into a symbolic equation in a more familiar language to scientists. 

"Forcing the machine learning model to use symbolic mathematics is basically a way of giving it a bias toward the existing ideas that we’ve constructed physics out of.

"There’s multiple benefits from this. The equations you get are very interpretable, and they tend to generalize and give you much better out-of-distribution predictions. The downside is that these algorithms are really, really computationally expensive. If you had infinite resources, it would be perfect.

"With symbolic regression, you are giving a neural network the symbols that scientists use as a library that it can build things with. 

"Another way is much more data-driven: giving a library of examples. Our approach in Polymathic AI is to take a model and train it on all the science data you can get. 

"You’re still starting from scratch, but you’ve given it so much data that you’re sort of anchoring its predictions."

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