Reservoir Computing (RC) is an umbrella term for adaptive computational paradigms that rely on an excitable dynamical system, also called the "reservoir." The paradigms have been shown to be particularly promising for temporal signal processing. RC was also explored as a potential candidate for emerging nanoscale architectures. In this article we reflect on the current state of RC and muse about its future. In particular, we propose a set of open problems that we think need to be addressed in order to make RC more mainstream.