Podcasts

Deep technical conversations with alignment researchers on interpretability, governance, superalignment, and the specific open problems in reducing existential risk from AI.

FLI's dedicated alignment series covers recursive reward modeling, RLHF, scalable oversight, and long-form interviews with leading safety researchers.

Aimed at computer scientists: deep dives into alignment papers with the authors, covering formal methods, reward modeling, and mechanistic interpretability.

Focused on career paths into AI safety: fellowship applications, research programs, and practical advice on transitioning into the field.

Long-form interviews on the world's most pressing problems, with extensive coverage of AI risk, governance, alignment research, and how to build a career that reduces existential threats.

ML research interviews with recurring coverage of interpretability, robustness, provably safe AI, and the intersection of capabilities and safety research.

Covers the intersection of AI governance, legislation, and safety, with expert guests on regulatory frameworks, international coordination, and policy strategies for advanced AI.

A four-hour conversation on AI existential risk, the difficulty of alignment, intelligence versus optimization, and why Yudkowsky believes the default outcome is catastrophic.

OpenAI's CEO discusses the company's safety philosophy, AGI governance, compute scaling, and the tension between moving fast and getting alignment right.

In-depth technical interviews with AI leaders including Dario Amodei on Anthropic's safety philosophy, Paul Christiano on iterated amplification, and others on scaling and alignment.

Episodes on AI risk, timelines, and decision-making under deep uncertainty, with a rationalist focus on calibrating beliefs about transformative AI.

Technical ML interviews with regular deep dives into interpretability, scaling laws, emergent capabilities, and the safety implications of frontier model development.

Applied ML and engineering, with episodes on responsible deployment, bias mitigation, red teaming, and the safety challenges that emerge when AI systems meet real-world constraints.

Industry and research perspectives with occasional safety and ethics episodes, useful for understanding how capability-focused organizations think about risk.