In 'The Secret of Our Success', Joseph Henrich presents a provocative alternative to the standard narrative about human evolution. He argues that the key to human success is not our individual intelligence, but our ability to form collective brains that store and transmit vast amounts of cultural knowledge. Henrich draws on findings from anthropology, linguistics, behavioral economics, psychology, and evolutionary biology to demonstrate how culture and genes interact in a unique evolutionary process that drives human innovation and progress. The book challenges traditional views of evolution and highlights the importance of social and cultural factors in shaping human behavior and achievements.
Moby-Dick is a novel that tells the story of Captain Ahab's maniacal quest for revenge against Moby Dick, the giant white sperm whale that bit off his leg. Narrated by Ishmael, the story follows the crew of the whaling ship Pequod as they embark on a journey that explores themes of obsession, man vs. nature, and the existential questions of good and evil. The novel is rich in detailed descriptions of whale hunting and life aboard a culturally diverse crew, and it has become a cornerstone of American and world literature[2][3][5].
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. Here’s last year’s episode, btw. Enjoy!
Watch on YouTube; listen on Apple Podcasts or Spotify.
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SPONSORS
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* Scale is building the infrastructure for safer, smarter AI. Scale’s Data Foundry gives major AI labs access to high-quality data to fuel post-training, while their public leaderboards help assess model capabilities. They also just released Scale Evaluation, a new tool that diagnoses model limitations. If you’re an AI researcher or engineer, learn how Scale can help you push the frontier at scale.com/dwarkesh.
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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
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