Uncovering Causal Structures

Yoshua discusses how GFlownet can help uncover causal structures in the world, bridging the gap between machine learning and causality research. The ability to represent distributions and sample pieces of them as thoughts is fundamental to how we think, leading to potential breakthroughs in understanding causal relationships.