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Nov 2024
5h 22m

#452 – Dario Amodei: Anthropic CEO on Cl...

LEX FRIDMAN
About this episode

Dario Amodei is the CEO of Anthropic, the company that created Claude. Amanda Askell is an AI researcher working on Claude’s character and personality. Chris Olah is an AI researcher working on mechanistic interpretability.
Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep452-sc
See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.

Transcript:
https://lexfridman.com/dario-amodei-transcript

CONTACT LEX:
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EPISODE LINKS:
Claude: https://claude.ai
Anthropic’s X: https://x.com/AnthropicAI
Anthropic’s Website: https://anthropic.com
Dario’s X: https://x.com/DarioAmodei
Dario’s Website: https://darioamodei.com
Machines of Loving Grace (Essay): https://darioamodei.com/machines-of-loving-grace
Chris’s X: https://x.com/ch402
Chris’s Blog: https://colah.github.io
Amanda’s X: https://x.com/AmandaAskell
Amanda’s Website: https://askell.io

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OUTLINE:
(00:00) – Introduction
(10:19) – Scaling laws
(19:25) – Limits of LLM scaling
(27:51) – Competition with OpenAI, Google, xAI, Meta
(33:14) – Claude
(36:50) – Opus 3.5
(41:36) – Sonnet 3.5
(44:56) – Claude 4.0
(49:07) – Criticism of Claude
(1:01:54) – AI Safety Levels
(1:12:42) – ASL-3 and ASL-4
(1:16:46) – Computer use
(1:26:41) – Government regulation of AI
(1:45:30) – Hiring a great team
(1:54:19) – Post-training
(1:59:45) – Constitutional AI
(2:05:11) – Machines of Loving Grace
(2:24:17) – AGI timeline
(2:36:52) – Programming
(2:43:52) – Meaning of life
(2:49:58) – Amanda Askell – Philosophy
(2:52:26) – Programming advice for non-technical people
(2:56:15) – Talking to Claude
(3:12:47) – Prompt engineering
(3:21:21) – Post-training
(3:26:00) – Constitutional AI
(3:30:53) – System prompts
(3:37:00) – Is Claude getting dumber?
(3:49:02) – Character training
(3:50:01) – Nature of truth
(3:54:38) – Optimal rate of failure
(4:01:49) – AI consciousness
(4:16:20) – AGI
(4:24:58) – Chris Olah – Mechanistic Interpretability
(4:29:49) – Features, Circuits, Universality
(4:47:23) – Superposition
(4:58:22) – Monosemanticity
(5:05:14) – Scaling Monosemanticity
(5:14:02) – Macroscopic behavior of neural networks
(5:18:56) – Beauty of neural networks

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