Princeton Comp Sci Ph.D. candidate Sayash Kapoor co-authored a blog post last week with his professor Arvind Narayanan called "AI Existential Risk Probabilities Are Too Unreliable To Inform Policy".

While some non-doomers embraced the arguments, I see it as contributing nothing to the discourse besides demonstrating a popular failure mode: a simple misunderstanding of the basics of Bayesian epistemology.

I break down Sayash's recent episode of Machine Learning Street Talk point-by-point to analyze his claims from the perspective of the one true epistemology: Bayesian epistemology.

00:00 Introduction

03:40 Bayesian Reasoning

04:33 Inductive vs. Deductive Probability

05:49 Frequentism vs Bayesianism

16:14 Asteroid Impact and AI Risk Comparison

28:06 Quantification Bias

31:50 The Extinction Prediction Tournament

36:14 Pascal's Wager and AI Risk

40:50 Scaling Laws and AI Progress

45:12 Final Thoughts

My source material is Sayash's episode of Machine Learning Street Talk: https://www.youtube.com/watch?v=BGvQmHd4QPE

I also recommend reading Scott Alexander’s related post: https://www.astralcodexten.com/p/in-continued-defense-of-non-frequentist

Sayash's blogpost that he was being interviewed about is called "AI existential risk probabilities are too unreliable to inform policy": https://www.aisnakeoil.com/p/ai-existential-risk-probabilities

Follow Sayash: https://x.com/sayashk

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