According to @demishassabis, the newest Google DeepMind Podcast episode focuses on AlphaGo and is available on YouTube, and as reported by Google DeepMind’s official podcast channel, the discussion revisits how reinforcement learning and Monte Carlo Tree Search advanced from AlphaGo to policy and value networks used in later systems. According to the Google DeepMind podcast episode page, the show highlights how self play and search efficiency translated into practical pipelines for enterprise decision making, including operations research, logistics, and game theoretic simulations. As reported by Google DeepMind, lessons from AlphaGo’s training curriculum—data-efficient self play, policy iteration, and evaluation—inform current large model agents and planning-enhanced models, creating opportunities for businesses to apply RL-driven optimization to routing, pricing, and resource allocation. According to the YouTube episode linked by @demishassabis, the episode also examines evaluation frameworks and governance takeaways from AlphaGo’s human-AI match deployments, which companies can adapt for AI risk management and human-in-the-loop oversight.