Скачать или смотреть Is RL + LLMs enough for AGI? — Sholto Douglas & Trenton Bricken

New episode with my good friends Sholto Douglas & Trenton Bricken. Sholto focuses on scaling RL and Trenton researches mechanistic interpretability, both at Anthropic. We talk through what’s changed in the last year of AI research; the new RL regime and how far it can scale; how to trace a model’s thoughts; and how countries, workers, and students should prepare for AGI. See you next year for v3. Enjoy!

𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒
Transcript: https://www.dwarkesh.com/p/sholto-tre...
Apple Podcasts: https://podcasts.apple.com/us/podcast...
Spotify: https://open.spotify.com/episode/3H46...
Last year's episode:    • Sholto Douglas & Trenton Bricken — How LLM...  

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
00:00:00 – How far can RL scale?
00:16:27 – Is continual learning a key bottleneck?
00:31:59 – Model self-awareness
00:50:32 – Taste and slop
01:00:51 – How soon to fully autonomous agents?
01:15:17 – Neuralese
01:18:55 – Inference compute will bottleneck AGI
01:23:01 – DeepSeek algorithmic improvements
01:37:42 – Why are LLMs ‘baby AGI’ but not AlphaZero?
01:45:38 – Mech interp
01:56:15 – How countries should prepare for AGI
02:10:26 – Automating white collar work
02:15:35 – Advice for students