In recent years, there has been a paradigm shift in the design of cyber-physical systems (CPS) towards using learning-enabled components to perform challenging tasks in perception, prediction, planning, and control. This transformation has created a gap between the implementation of this emerging class of learning-enabled cyber-physical systems and the guarantees that one can provide on their safety and reliability. To close this gap, a fundamental revision is required of how a combination of formal methods and machine learning theory can be applied in the analysis of such systems. The goal of this AISoLA track is to drive a discussion on shaping this fundamental revision and to foster the exchange of ideas on assured autonomy among researchers from the fields of formal methods, control, and AI.