AXRP - the AI X-risk Research Podcast
Un podcast de Daniel Filan
59 Épisodes
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35 - Peter Hase on LLM Beliefs and Easy-to-Hard Generalization
Publié: 24/08/2024 -
34 - AI Evaluations with Beth Barnes
Publié: 28/07/2024 -
33 - RLHF Problems with Scott Emmons
Publié: 12/06/2024 -
32 - Understanding Agency with Jan Kulveit
Publié: 30/05/2024 -
31 - Singular Learning Theory with Daniel Murfet
Publié: 07/05/2024 -
30 - AI Security with Jeffrey Ladish
Publié: 30/04/2024 -
29 - Science of Deep Learning with Vikrant Varma
Publié: 25/04/2024 -
28 - Suing Labs for AI Risk with Gabriel Weil
Publié: 17/04/2024 -
27 - AI Control with Buck Shlegeris and Ryan Greenblatt
Publié: 11/04/2024 -
26 - AI Governance with Elizabeth Seger
Publié: 26/11/2023 -
25 - Cooperative AI with Caspar Oesterheld
Publié: 03/10/2023 -
24 - Superalignment with Jan Leike
Publié: 27/07/2023 -
23 - Mechanistic Anomaly Detection with Mark Xu
Publié: 27/07/2023 -
Survey, store closing, Patreon
Publié: 28/06/2023 -
22 - Shard Theory with Quintin Pope
Publié: 15/06/2023 -
21 - Interpretability for Engineers with Stephen Casper
Publié: 02/05/2023 -
20 - 'Reform' AI Alignment with Scott Aaronson
Publié: 12/04/2023 -
Store, Patreon, Video
Publié: 07/02/2023 -
19 - Mechanistic Interpretability with Neel Nanda
Publié: 04/02/2023 -
New podcast - The Filan Cabinet
Publié: 13/10/2022
AXRP (pronounced axe-urp) is the AI X-risk Research Podcast where I, Daniel Filan, have conversations with researchers about their papers. We discuss the paper, and hopefully get a sense of why it's been written and how it might reduce the risk of AI causing an existential catastrophe: that is, permanently and drastically curtailing humanity's future potential. You can visit the website and read transcripts at axrp.net.
