The Future Of Life

Jan Leike is a senior research scientist who leads the agent alignment team at DeepMind. His is one of three teams within their technical AGI group; each team focuses on different aspects of ensuring advanced AI systems are aligned and beneficial. Jan's journey in the field of AI has taken him from a PhD on a theoretical reinforcement learning agent called AIXI to empirical AI safety research focused on recursive reward modeling. This conversation explores his movement from theoretical to empirical AI safety research — why empirical safety research is important and how this has lead him to his work on recursive reward modeling. We also discuss research directions he's optimistic will lead to safely scalable systems, more facets of his own thinking, and other work being done at DeepMind.  Topics discussed in this episode include: -Theoretical and empirical AI safety research -Jan's and DeepMind's approaches to AI safety -Jan's work and thoughts on recursive reward modeling -AI safety benchmarking at DeepMind