The biggest success of AI in science so far is the AlphaFold 2 system. This is a deep learning system which has made large strides on a fundamental scientific problem: how to predict the 3-dimensional structure of a protein from the sequence of amino acids making up that protein. This breakthrough has helped set off an ongoing deep learning revolution in molecular biology. While obviously of interest to molecular biologists, I believe this is of much broader interest for science as a whole, as a concrete prototype for how artificial intelligence may impact discovery. In this short survey talk I briefly discuss questions including: how can such systems be validated? Can they be used to identify general principles that human scientists can learn from? And what should we expect a good theory or explanation to provide anyway? The focus of the talk is extant results and the near term, not the longer-term future. However, I hope it may help in grounding an understanding of that longer-term future, and of the benefits and risks of AI systems.
Here's the abstract of the talk: