Why theory is not dead

Famous founders of social media services or internet search engines repeatedly explain in the media that theory is dead, and the future consists of labelled data and machine learning algorithms. The mentioned machine learning algorithms are increasingly used in astronomy as well. In my goodbye-talk, I will therefore describe why and how machine learning cannot replace theoretical physics. The different points of view can of course be debated, but it is surprisingly difficult to really show with equations where machine learning is limited, and where theoretical physics must take over. To demonstrate it, I will fit all cosmological data sets simultaneously, with a single parameter, and derive the best-fitting solution analytically. I will show that this fit achieves arbitrary precision. Most statistical tests therefore prefer this universal parameter over any theoretical model. The universal parameter can only be discarded, once randomness is fully incorporated as a feature into conventional theoretical physics.