Ezra Karger
Showing 1–2 of 2 transcripts.
- 80,000 Hours2h 50m
The best of The 80,000 Hours Podcast in 2024
Luisa, Rob, Randy Nesse, Hugo Mercier, Meghan Barrett, Sébastien Moro, Sella Nevo, Zvi Mowshowitz, Zach Weinersmith, Rachel Glennerster, Emily Oster, Carl Shulman, Nathan Labenz, Nathan Calvin, Rose Chan Loui, Nick Joseph, Sihao Huang, Ezra Karger, Matt Clancy, Vitalik Buterin, Annie Jacobsen, Nate Silver, Kevin Esvelt, Lewis Bollard, Bob Fischer, Elizabeth Cox, Anil Seth, Eric Schwitzgebel, Jonathan Birch, Peter Godfrey-Smith, Laura Deming, Venki Ramakrishnan, Ken Goldberg, Sarah Eustis-Guthrie, Dean Spears, Cameron Meyer Shorb, Spencer Greenberg
Randy Nessie and Hugo Mercier establish that morality and skepticism are evolutionary adaptations driven by social selection and the need for honest signaling. In parallel, experts like Megan Barrett and Lewis Bollard advocate for expanding ethical consideration to insect sentience and the eradication of wild animal suffering, while AI safety researchers demonstrate emerging risks such as sleeper agents and instrumental convergence. The discussion further critiques the economic viability of space resources and analyzes social dynamics ranging from the gender wage gap to the limitations of cryonics, concluding with philosophical arguments about consciousness and strategies for effective altruism.
- 80,000 Hours2h 49m
What superforecasters and experts think about existential risks | Ezra Karger
The Forecasting Research Institute conducted a multi-stage tournament and adversarial collaboration involving over 200 experts, superforecasters, and skeptics to quantify existential risks, revealing that while both groups agree advanced AI will emerge by the mid-21st century, they diverge sharply on extinction probabilities with domain experts estimating a 3% risk compared to 0.38% for superforecasters. Follow-up studies on AI risk perception determined that long-term worldview differences and expectations regarding human well-being rather than short-term capabilities drive these disagreements, resulting in minimal belief convergence during eight weeks of structured debate. The findings suggest that public risk estimates are highly sensitive to reference class framing and highlight independent verification of AI's ability to evade deactivation as the critical crux needed for consensus on catastrophic outcomes.