Modern machine learning has had an outsized impact on many scientific fields, and fundamental physics is no exception. What is special about fundamental physics, though, is the vast amount of theoretical, experimental, and observational knowledge that we already have about many problems in the field. Is it possible to teach a machine to “think like a physicist” and thereby advance physics knowledge from the smallest building blocks of nature to the largest structures in the universe? In this talk, I argue that the answer is “yes”, using the example of particle physics at the Large Hadron Collider to highlight the fascinating synergy between theoretical principles and machine learning architectures. I also argue that by fusing the “deep learning” revolution with the time-tested strategies of “deep thinking” in physics, we can galvanize research innovation in artificial intelligence more broadly.