Machine Learning Shaking Up Hard Sciences Too

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"... Over the past decade, in tandem with the broader deep-learning revolution, particle physicists have trained algorithms to solve previously intractable problems and tackle completely new challenges. ...
Machine learning is also allowing particle physicists to think differently about the data they use. Instead of focusing on a single event—say, a Higgs boson decaying to two photons—they are learning to consider the dozens of other events that happen during a collision. Although there’s no causal relationship between any two events, researchers ... are now embracing a more holistic view of the data, not just the piecemeal point of view that comes from analyzing events interaction by interaction. ...
More dramatically, machine learning has also forced physicists to reassess basic concepts. ... symmetry ... Symmetries require a reference frame—in other words, is the image of a distorted sphere in a mirror actually symmetrical? There’s no way of knowing without knowing if the mirror itself is distorted. ...
As some particle physicists delve deeper into machine learning, an uncomfortable question rears its head: Are they doing physics, or computer science? Stigma against coding—sometimes not considered to be “real physics”—already exists; similar concerns swirl around machine learning. ..."

Machine Learning Shaking Up Hard Sciences, Too - IEEE Spectrum Heard of graph neural networks? Particle physicists have