Active learning can dramatically accelerate scientific discovery by automatically designing experiments that test competing mechanistic theories, reducing the data needed to understand complex systems.
ATLAS is an automated system that discovers interpretable behavioral models by iteratively generating competing hypotheses and designing experiments to distinguish between them. It uses sparse neural networks and active learning to efficiently recover how agents make decisions, achieving 5-10x better sample efficiency than random experimentation.