My research aims to understand the principles underlying biological and artificial intelligence. Humans and animals produce amazingly complex behaviors in the face of changing environments, often with little to no supervision. This has led me to study the structure of neural representations in perceptual and physiological experiments, asking how our visual system might enable such behaviors. At DeepMind, I have been investigating self-supervised algorithms that extract this structure from raw data, enabling data-efficient image recognition, behaviorally-relevant scene understanding, and unsupervised object discovery.