learning

Dwarkesh Patel interviews Andy Matuschak on how he learns, why memorization matters for understanding, and why most learning tools fail. The conversation also covers handling confusion and the role of tutoring.

A 2023 University of Toronto conversation between Geoffrey Hinton and Fei-Fei Li on responsible AI development. They look back at the history of deep learning and ImageNet and discuss the ethics and risks of AI.

Dwarkesh Patel interviews Richard Sutton, reinforcement learning pioneer, Turing Award winner and author of The Bitter Lesson. Sutton argues LLMs are a dead end because they cannot learn on the job, and that continual learning from experience will require a new architecture.

Dwarkesh Patel interviews Andrej Karpathy on why reinforcement learning is flawed, why AGI may blend into steady GDP growth, why self-driving took so long, and the future of education. The conversation runs about two and a half hours.

A long Machine Learning Street Talk interview with Kenneth Stanley, creator of NEAT and novelty search, about his book on why greatness cannot be planned. They discuss open-endedness, the tyranny of objectives, serendipity and the evolution of ideas.

A 42-minute CBS interview with Geoffrey Hinton at the Vector Institute in 2023, covering the history of neural networks and the present and future of AI. He discusses AGI timelines and whether language models truly understand.

Journalist Vincent Woo discusses his New York Magazine piece on Lambda School's misleading promises, including its income share agreements and placement claims, in an interview on This Week in Startups. The host questions him pointedly.

A Ray Summit 2022 keynote fireside chat between OpenAI's Greg Brockman and Anyscale's Robert Nishihara. They discuss how OpenAI's training infrastructure evolved and its use of Ray.