An independent study
Foundations of
Efficient Intelligence
Why is biological intelligence so computationally and sample efficient — and how do we build AI systems with those properties? An interactive graduate textbook built one paper, one lecture at a time, drawing on computational cognitive science, neuroscience, ML, and systems.
Samuel Gershman Lab
Strategy reuse, reinforcement learning, memory, Bayesian cognition, human decision-making.
Josh Tenenbaum Lab
One-shot learning, program induction, concept learning, causal reasoning, intuitive physics.
Karl Friston
Predictive coding, active inference, the free energy principle.
Danijar Hafner
Latent world models, Dreamer, planning.
Eric Schulz Lab
Meta-learned models of cognition; neural networks and LLMs as models of human learning.
Matthew Botvinick Lab
Deep reinforcement learning as a theory of brain and cognition; meta-RL, distributional value coding.