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6 件
並び順
5:15:00EN
Lex Fridman· 2024年11月11日ポッドキャスト · 2 人Dario Amodei Has Seen This Scaling Movie Enough Timesゲスト Dario Amodei · ホスト Lex FridmanAnthropic’s CEO puts numbers on frontier training costs and AGI, then hands the mic to the people who shape Claude’s character and look inside its weights.モデルと学習安全性とガバナンスScaling Laws+3
2:30:28EN
Lex Fridman· 2023年4月21日ポッドキャスト · 2 人AI Only Has a Neocortex: Manolis Kellis on Evolutionary Baggage and Superintelligent Partnersゲスト Manolis Kellis · ホスト Lex FridmanOn his fifth Lex Fridman podcast, the MIT computational biologist argues that layered evolutionary history is what makes humans irreplaceable — and that alignment with AI has to run both ways.PhilosophyAGI(汎用人工知能)Model Interpretability+3
3:17:51EN
Lex Fridman· 2023年3月30日ポッドキャスト · 2 人Whoops is the sound of getting less wrong: Yudkowsky, two weeks after GPT-4ゲスト Eliezer Yudkowsky · ホスト Lex FridmanTwo weeks after GPT-4 launched, Eliezer Yudkowsky sat down with Lex Fridman and started by admitting the transformer stack had already gone further than he expected.セキュリティと安全性安全性とガバナンスExistential Risk+3
3:02:43EN
Lex Fridman· 2021年1月18日ポッドキャスト · 2 人Humanity Cannot Survive on the Right Person Showing Up at the Right Timeゲスト Max Tegmark · ホスト Lex FridmanMIT physicist Max Tegmark tells Lex Fridman why AI should be as well understood as rockets, why 2020 was a warning on weapons and news, and why luck is a terrible long-term plan.セキュリティと安全性安全性とガバナンスAI research+3
1:18:41EN
Lex Fridman· 2020年1月14日ポッドキャスト · 2 人The SS Soldier Who Hugged a Jewish Boy, and the Systems That Still Fool Usゲスト Daniel Kahneman · ホスト Lex FridmanDaniel Kahneman on System 1, deep learning that does not know what it is saying, and why a machine that must call a human is almost as hard as a machine that never needs one.機械学習の基礎Deep LearningHuman-AI Collaboration+3
2:24:31EN
Lex Fridman· 2019年10月11日ポッドキャスト · 2 人Watson Won Jeopardy Without Understanding. Ferrucci Wants AI That Can Explain Itselfゲスト David Ferrucci · ホスト Lex FridmanLex Fridman talks with David Ferrucci about IBM Watson, why prediction is not understanding, and why high-stakes decisions need machines that can reason with us.RAG とナレッジ検索拡張生成(RAG)AI research+3