Join host Craig Smith on episode #170 of Eye on AI, for a riveting conversation with Richard Sutton, currently serving as a professor of computing science at the University of Alberta and a research scientist at Keen Technologies. Sutton is considered one of the founders of modern computational reinforcement learning, having several significant contributions to the field, including temporal difference learning and policy gradient methods. In this episode, we go through the Alberta Plan for AI development, the transformative potential of reinforcement learning, and the future of AI in augmenting human intelligence. Richard Sutton shares insights on the importance of computational power, the impact of large language models, and the vision for AI that interacts with the world through goals and learning from its environment. We also explore the challenges and opportunities in making AI more embodied and goal-oriented, and how this approach could revolutionize our interaction with technology. A must-listen for anyone interested in the cutting-edge advancements in AI and its societal implications. Don't forget to rate us on Apple Podcast and Spotify if you enjoyed this episode! This episode is sponsored by Netsuite by Oracle, the number one cloud financial system, streamlining accounting, financial management, inventory, HR, and more. Download NetSuite's popular KPI Checklist, designed to give you consistently excellent performance - absolutely free at https://netsuite.com/EYEONAI Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Preview and Introduction (02:15) AI's Evolution: Insights from Richard Sutton (07:08) Breaking Down AI: From Algorithms to AGI (10:50) The Alberta Experiment: A New Approach to AI Learning (18:27) The Horde Architecture Explained (21:23) Power Collaboration: Carmack, Keen, and the Future of AI (25:04) Expanding AI's Learning Capabilities (31:34) Is AI the Future of Technology? (35:29) The Next Step in AI: Experiential Learning and Embodiment (40:00) AI's Building Blocks: Algorithms for a Smarter Tomorrow (45:59) The Strategy of AI: Planning and Representation (49:27) Learning Methods Face-Off: Reinforcement vs. Supervised (52:53) The 2030 Vision: Aiming for True AI Intelligence?

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