One of the key choices we have as scientists is to design informative experiments. Using examples from astronomy, we discuss a machine learning perspective of the scientific method. With AI promising accurate predictions, we revisit the question of adaptively designing new measurements that take previous data into account. Following this scientific method framing, we illustrate some recent ideas on using machine learning to recommend experiments in genomics. The goal of this talk is to start a conversation about what it means to recommend experiments in astronomy.