Explore the role of multi-agent learning in artificial intelligence research at DeepMind in this comprehensive lecture from the Alan Turing Institute. Delve into the concept of intelligence as an agent's ability to achieve goals in diverse environments, with a focus on evolving collections of agents. Examine two key projects: the study of cooperation among self-interested agents using Sequential Social Dilemmas, and the groundbreaking AlphaGo project that utilized Learning from Self-Play to defeat top professional Go players. Gain insights into the challenges and advancements in multi-agent learning, including temporal dynamics, coordination problems, and the complexities of the game of Go. Discover the innovative approaches used in AlphaGo, such as value networks, policy networks, and supervised learning techniques. Analyze the lessons learned from these projects and their implications for the future of AI research.