Richard Sutton

Richard Sutton, a leading researcher in reinforcement learning and 2024 Turing Award winner, argues that large language models (LLMs) are a dead end. He believes LLMs can't learn on-the-job and emphasizes the need for a new architecture enabling continual learning like animals do. The discussion touches on how LLMs perform imitation instead of genuine experiential learning, and why instilling goals is vital for intelligence. Sutton critiques the predictive nature of LLMs, advocating for a future where AI learns from real-world interactions rather than fixed datasets.

Join Richard Sutton, a renowned Professor from the University of Alberta and a Research Scientist at Keen Technologies, as he delves into the complexities of artificial general intelligence. He discusses who controls AGI and the moral dilemmas it brings. Sutton envisions a decentralized, cooperative future while questioning what true prosperity means in a tech-driven reality. He unpacks humanity's fragile relationship with AI and the necessity for collaboration amid geopolitical challenges, emphasizing the unpredictable journey that lies ahead.

In this engaging discussion, Richard Sutton, a pioneer in reinforcement learning, and Andrew Barto, an influential figure in the same field, share insights on their groundbreaking work. They delve into the origins of reinforcement learning, discussing its ties to neuroscience and psychology. The duo reflects on their notable contributions, like temporal difference learning, and its applications in AI systems such as AlphaGo. They also explore the future of human-RL relationships, emphasizing the importance of safety and ethical considerations in AI development.

Join Richard Sutton, a pioneer in computational reinforcement learning, as he discusses the transformative potential of AI, the importance of computational power, and the vision for AI that interacts with the world through goals and learning from its environment. Explore challenges and opportunities in making AI more embodied and goal-oriented, revolutionizing our interaction with technology.

Richard Sutton, a pioneer in AI research best known for his work on reinforcement learning, dives deep into the limitations of large language models (LLMs). He argues that LLMs mimic human behavior without true understanding and emphasizes the importance of goal-directed learning. The conversation explores themes such as continual learning, imitation versus experience, and the implications of Sutton's Bitter Lesson for future AI developments. Sutton also discusses the potential risks of merging experiences between AI copies and the need for steering AI towards positive outcomes.