Biological systems rely on an influx of energy from the environment to build and maintain complex spatio-temporal structures. Striking examples of the non-equilibrium nature of biological systems are the self-organisation of cells during embryonic development, regeneration and tumour genesis. Recent technological breakthroughs in genomics and machine learning now for the first time allow probing these systems with unprecedented detail. In our group we believe that these new developments give rise to exciting questions at the frontier of theoretical physics. We use methods from non-equilibrium statistical physics and machine learning to understand the processes underlying the behaviour of active biosystems and the inner workings of artificial intelligence. We also study ensuing questions on fundamental physical principles underlying non-equilibrium matter. In collaboration with experimental collaborators we apply our work in the context of embryonic development, ageing and artificial intelligence.