the time scales that we see in development and in acquisition of new skills through repetitive training with the fast and reactive fluid adaptivity that we see in reactions to unexpected task or context changes.

Thus, AI and biological intelligent systems foster our understanding of adaptivity from complementary ends: AI allows us to understand adaptivity by starting out at technically feasible forms of crystallized adaptivity to synthesize solutions to specific tasks, thereby allowing us to work towards increased levels of fluid adaptivity. Biological systems offer us the opportunity to study how entire hierarchies of fluid adaptivity interact in coherent architectures while, however, it is extremely difficult to observe the underlying levels of crystallized adaptivity that have produced these systems.

We want to argue that biological systems indeed provide inspiration on how to overcome the current focus on crystallized adaptivity, i.e. the creation of solutions to given and largely fixed tasks towards finding learning algorithms that can produce an organized hierarchy of fluid adaptivities that support flexible action in a wide range of varying conditions.

Previous work by us and other groups has demonstrated that the described type of flexibility can be studied in several biological systems ranging from insects and 