Researchers are increasingly using small, autonomous underwater robots to collect data in the world's oceans. Rescuers spotted debris from the tourist submarine Titan on the ocean floor near the wreck of the Titanic on June 22, 2023, indicating that the vessel suffered a catastrophic failure and the five people aboard were killed. Bringing people to the bottom of the deep ocean is inherently dangerous. At the same time, climate change means collecting data from the world's oceans is more vital than ever. Purdue University mechanical engineer Nina Mahmoudian explains how researchers reduce the risks and costs associated with deep-sea exploration: Send down subs, but keep people on the surface.

Self-adaptive systems continuously adapt to changes in their execution environment. Capturing all possible changes to define suitable behaviour beforehand is unfeasible, or even impossible in the case of unknown changes, hence human intervention may be required. We argue that adapting to unknown situations is the ultimate challenge for self-adaptive systems. Learning-based approaches are used to learn the suitable behaviour to exhibit in the case of unknown situations, to minimize or fully remove human intervention. While such approaches can, to a certain extent, generalize existing adaptations to new situations, there is a number of breakthroughs that need to be achieved before systems can adapt to general unknown and unforeseen situations. We posit the research directions that need to be explored to achieve unanticipated adaptation from the perspective of learning-based self-adaptive systems. At minimum, systems need to define internal representations of previously unseen situations on-the-fly, extrapolate the relationship to the previously encountered situations to evolve existing adaptations, and reason about the feasibility of achieving their intrinsic goals in the new set of conditions. We close discussing whether, even when we can, we should indeed build systems that define their own behaviour and adapt their goals, without involving a human supervisor.