On Particle Physics, Information, and Machines That Learn

Description

Recent advances in machine learning have not only innovated much of the hi-tech industry, they also change how modern science is pursued. By giving a few subjectively selected highlights from the field of particle physics, I wish to describe the data challenges that particle physics is currently facing, the solutions that have worked in the past, and possible ideas for the future. I shall however argue that not only does machine learning affect particle physics, physics might also help elucidate on machine learning, similar to how biology or neuroscience inspired many machine learning algorithms. "Why is deep learning so cheap? Can we apply the mathematics of curved spacetimes to information spaces? Could the notion of quantum mechanical superpositions help in developing efficient algorithms? Can machine learning benefit from quantum computing?" These are questions that physicists are currently debating. I shall briefly (and only superficially) touch upon these topics and some of their possible answers.

Period	25 Jan 2018
Event type	Guest talk
Location	AustriaShow on map