Rich Sutton

Rich Sutton (Richard S. Sutton) is a Canadian computer scientist and a foundational figure in the field of reinforcement learning. He is a professor at the University of Alberta and a distinguished research scientist at DeepMind. Sutton is best known as the co-author, with Andrew Barto, of the definitive textbook *Reinforcement Learning: An Introduction*, and as the originator of temporal-difference learning — a method that allows an agent to learn predictions by comparing its current predictions with later, more informed predictions.

In 2019, Sutton published an influential essay titled "The Bitter Lesson," arguing that the most effective advances in artificial intelligence have come from leveraging general methods and massive computation rather than from encoding human knowledge into systems. The essay has become a touchstone in debates about the future direction of AI research, with defenders citing it as a call to scale and critics arguing that it undervalues the role of inductive bias, data efficiency, and human expertise in building safe and interpretable systems. Sutton's work connects the engineering of learning algorithms to the broader systems question of how intelligence can be built from interaction rather than from instruction.