A preprint published by Stanford University and Google researchers proposes an AI technique that predicts how goals were achieved, effectively learning to reverse-engineer tasks. They say it enables autonomous agents to learn through self-supervision, which some experts believe is a critical step toward truly intelligent systems. Learning general policies for complex tasks often requires dealing with unfamiliar objects and scenes, and many methods rely on forms of supervision like expert demonstrations. But these entail significant tuning; demonstrations, for example, must be completed by experts many times over and recorded by special infrastructure. That's unlike the researchers' proposed approach -- time reversal as self-superivision (TRASS) -- which predicts "reversed trajectories" to create sources of supervision that lead to a goal or goals.