on adaptivity and learning in artificial and biological systems has been shown to be mutually fruitful (e.g. [@neftci_reinforcement_2019; @hassabis_neuroscience-inspired_2017; @cully_robots_2015]). In this paper, we want to argue that it might be valuable to consider how the problems solved by current AI approaches and biological systems differ fundamentally. Initially, AI started from static problems that allow for a crisp characterization and showed how learning algorithms can produce solutions by optimizing a cost function. Usually, these cost functions characterize desirable performance criteria for a particular task, such as playing a particular game, such as Go or some of the Atari games, and the learning algorithm is conceptualized as a specific form of adapting to a given task. Typically, this adaptivity unfolds within a huge number of passes through different instances of the given task, and one celebrated breakthrough of AI was the possibility to demonstrate the feasibility of this approach for quite an impressive number of different tasks. However, it has been turned out very difficult to extend this “crystallized” adaptivity to also respond quickly to unpredicted variability that occurs on the time scale of a single action: such changes may occur due to unpredicted events during execution of a single task 