results in episodes
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Tagged “Reinforcement Learning”
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Mastering Strategy: From Poker to Diplomacy AI
Noam Brown, a leading research scientist at Meta AI, discusses his pioneering work in developing systems that achieve superhuman performance in complex games. The episode covers his journey from creating poker-playing AIs like Libratus and…
2h 33m -
Oriol Vinyals: DeepMind AI, Gato, and the Future of AGI
In this episode, DeepMind research director Oriol Vinyals discusses the frontier of artificial intelligence, focusing on generalist models like Gato and the importance of modularity in scaling capabilities. Vinyals highlights the shift fro…
2h 15m -
Wojciech Zaremba: OpenAI, AGI, Consciousness, and Love
In this expansive dialogue, Wojciech Zaremba, co-founder of OpenAI, provides a deep dive into the engineering and philosophical foundations of Artificial General Intelligence. Exploring the intersection of mathematics and consciousness, Za…
2h 58m -
Machine Learning, Reinforcement Learning, and AI Parody
This episode features a lively discussion with computer science professor Michael Littman on the fundamentals and future of Machine Learning and Reinforcement Learning. Littman shares his expert take on the AGI debate, arguing that existen…
2h 01m -
Ilya Sutskever: Deep Learning and the Future of AI
In this extensive conversation, Ilya Sutskever discusses the transformative journey of deep learning from its early, underestimated days to its current state of radical success. He explores the empirical necessity of combining massive comp…
1h 37m -
Unlocking Human-Level AI with David Silver
This episode features David Silver, a pioneering force at DeepMind, discussing the profound journey of mastering complex games through reinforcement learning. The core narrative follows the evolution from AlphaGo to the generalized, superh…
1h 48m -
Human-Robot Interaction: Alignment and Modeling
In this deep dive into human-robot interaction, Anka Dragan discusses the fundamental algorithmic challenges of building robots that can effectively understand and coordinate with humans. Moving away from rigid, rule-based systems, Dragan …
1h 39m -
Exploring Artificial General Intelligence with Marcus Hutter
Marcus Hutter, a senior research scientist at Google DeepMind, joins the show to discuss the theoretical architecture of Artificial General Intelligence. Central to his outlook is the concept that intelligence is essentially compression—th…
1h 40m -
Deep Learning and the Future of AI with Oriol Vinales
In this insightful conversation, Google DeepMind research scientist Oriol Vinales explores the intersection of deep learning and human-competitive gaming. The episode provides a comprehensive look at the architecture of AlphaStar, the AI t…
1h 46m -
Greg Brockman: The Path to AGI at OpenAI
Greg Brockman, CTO and co-founder of OpenAI, discusses the organization's mission to develop safe and beneficial Artificial General Intelligence. He explores the concept of intelligence as an information-processing phenomenon and emphasize…
1h 25m -
Robotics, AI Planning, and Intelligence with Leslie Kaelbling
In this extensive discussion, roboticist Leslie Kaelbling explores the technical and philosophical foundations of artificial intelligence. She critiques the historical reliance on expert systems, favoring a more balanced approach that comb…
1h 01m -
Jürgen Schmidhuber on AGI, Creativity, and AI History
In this insightful conversation, Jürgen Schmidhuber explores the fundamental pillars of intelligence, focusing on the role of recursive self-improvement and meta-learning. He posits that the history of science is essentially a pursuit of d…
1h 20m -
Robotics and AI: Learning, Control, and Human Interaction
In this insightful conversation, robotics expert Peter Abiel explores the frontiers of artificial intelligence and physical robotics. The discussion delves into the technical challenges of hierarchical reinforcement learning, the importanc…
42m 56s -
Yoshua Bengio on the Future of Deep Learning and AI
In this engaging episode, Yoshua Bengio discusses the limitations of current deep learning architectures, emphasizing that simple scaling is insufficient for human-level artificial intelligence. He argues for a shift towards active agents …
42m 54s
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