Our universe is of astonishing simplicity: almost all physical observations can in principle be described by a few theories that have short mathematical descriptions. But there is a field of computer science which quantifies simplicity, namely algorithmic information theory (AIT). In this workshop, we will discuss emerging connections between AIT and physics, some of which have recently shown up in fields like quantum information theory and thermodynamics. In particular, AIT and physics share one goal: namely, to predict future observations given previous data. In fact, there exists a gold standard of prediction in AIT called Solomonoff induction which is also applied in artificial intelligence. This motivates us to look at a broader question: what is the role of induction in physics? For example, can quantum states be understood as Bayesian states of belief? Can physics be understood as a computation in some sense? What is the role of the observer, i.e. the agent that is supposed to perform the predictions? These and related topics will be discussed by a diverse group of researchers from different disciplines.