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12:49EN
OpenAI· 2026년 10월 7일Alex Mashrabov: Higgsfield agents that choose models and launch their own GPUsAlex Mashrabov · HiggsfieldAt OpenAI DevDay 2026, Higgsfield's CEO walks through why a continuous media experience needs an agent that picks models, scores its own output, and provisions GPUs — including a live 10-hour AI stream small teams can now ship.
13:12ENOpenAI· 2026년 10월 7일Alexandra Lato on Building Stack Overflow for Agents and Stack Internal with CodexAarti Bagul, Alexandra Lato · OpenAIStack Overflow’s VP of Product explains how a small team used Codex with opposite constraints on two products—and why agent-native design means letting agents contribute knowledge, not just read it.
13:30EN
3Blue1Brown· 2025년 8월 17일How AlphaGeometry Combines Logical Proof with Creative ConstructionAleph0Aleph0 explains how a symbolic geometry solver gets surprisingly far without AI, and how a language model supplies the auxiliary constructions that take it to Olympiad level.
11:26EN
Lex Fridman· 2020년 4월 11일팟캐스트 · 2명Richard Dawkins on Memes, the Internet and Why Wrong Ideas Spread게스트 Richard Dawkins · 진행자 Lex FridmanThe man who coined the word explains to Lex Fridman what makes a meme more than a metaphor, why the internet is a step change for ideas, and why an idea, like a peacock's tail, doesn't need to be true or useful to spread.
12:42ENLex Fridman· 2020년 3월 28일팟캐스트 · 2명The Experience Machine: What Our Refusal to Plug In Says About What We Value게스트 Nick Bostrom · 진행자 Lex FridmanNick Bostrom unpacks Robert Nozick's experience machine, the status quo effect behind our answers, and whether a simulation convincing enough to feel real would have to contain real minds.
14:55ENLex Fridman· 2019년 10월 29일팟캐스트 · 2명Kasparov on Deep Blue, AlphaZero, and Why Machines Win Closed Systems but Humans Still Choose the Questions게스트 Garry Kasparov · 진행자 Lex FridmanGarry Kasparov revisits the 1997 loss to Deep Blue, explains why machines beat humans in closed systems, and argues that the human role is to pick the right questions and not interfere with superior machine knowledge.
10:20ENLex Fridman팟캐스트 · 2명How C++ Is Standardized: Votes, Consensus and the Move to a Three-Year Release Cycle게스트 Bjarne Stroustrup · 진행자 Lex FridmanBjarne Stroustrup explains how the ISO committee works, why C++0x slipped to C++11, and how shorter cycles made C++14, C++17 and C++20 arrive on time.
11:11ENLex Fridman팟캐스트 · 2명Yann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not General게스트 Yann Lecun · 진행자 Lex FridmanIn a 2019 clip, LeCun defends benchmarks as a test of AI claims, explains why interactive environments break the usual train-test protocol, and argues that "general intelligence" is a misnomer.
14:41ENLex Fridman팟캐스트 · 2명Talking for 20 Minutes: How the Alexa Prize Turns Real Customers into a Testbed for Conversational AI게스트 Rohit Prasad · 진행자 Lex FridmanRohit Prasad, head scientist of Amazon Alexa, explains the Alexa Prize grand challenge, how it is judged and why true conversation still needs reasoning.
11:41ENLex Fridman팟캐스트 · 2명Teaching Machines Humility: Stuart Russell on Escaping the King Midas Problem게스트 Stuart Russell · 진행자 Lex FridmanIn a short clip from a 2018 conversation, Berkeley's Stuart Russell explains why fixed objectives make superintelligent AI dangerous and why uncertainty about our goals is the fix.
10:16ENLex Fridman팟캐스트 · 2명Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Think게스트 Yann Lecun · 진행자 Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
11:19ENLex Fridman팟캐스트 · 2명Why Donald Knuth Thinks P = NP and Why It Might Not Matter게스트 Donald Knuth · 진행자 Lex FridmanKnuth explains how an algorithm can provably exist without anyone being able to write it down, and why that shapes his view of P versus NP.
12:53ENLex Fridman팟캐스트 · 2명Principles, Not Black Boxes: Ray Dalio on When to Trust AI게스트 Ray Dalio · 진행자 Lex FridmanRay Dalio explains how Bridgewater turns human thinking into algorithms, why machine learning is risky when the future differs from the past, and why writing down principles is the route to better decisions.
12:01ENLex Fridman팟캐스트 · 2명The Big Nap: Why Eric Weinstein Thinks the Pandemic Ends 75 Quiet Years게스트 The Big Nap · 진행자 Lex FridmanIn an April 2020 conversation, Eric Weinstein and Lex Fridman discuss what a global crisis reveals about collective action, fragility, and the institutions we treat as museum pieces.
10:24ENLex Fridman팟캐스트 · 2명Concepts: How C++ Finally Got Compile-Time Constraints on Templates게스트 Bjarne Stroustrup · 진행자 Lex FridmanBjarne Stroustrup explains what C++ concepts are, why they are real and shippable today, and why it took decades to get them right.
11:11ENLex Fridman팟캐스트 · 2명Filling the Empty Quadrants: Jim Gates on Supersymmetry and the Beauty of Broken Symmetry게스트 Jim Gates · 진행자 Lex FridmanTheoretical physicist Jim Gates explains supersymmetry with a four-slice pie, recalls how he chose it for his thesis in the 1970s, and connects it to the way physics moves from mathematics to experiment.
12:52ENLex Fridman팟캐스트 · 2명Roger Penrose on a Universe That Restarts: When the Far Future Becomes the Next Big Bang게스트 Roger Penrose · 진행자 Lex FridmanPenrose explains conformal cyclic cosmology, in which the remote future of one cosmic eon is mathematically the same as the Big Bang of the next.
14:47ENLex Fridman팟캐스트 · 2명Build Your Own Types: What Bjarne Stroustrup Learned from Simula, Fortran and Lisp게스트 Bjarne Stroustrup · 진행자 Lex FridmanThe creator of C++ traces how Simula's user-defined types shaped his language, and why he builds for constrained, long-lived systems.
10:34ENLex Fridman팟캐스트 · 2명Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AI게스트 François Chollet · 진행자 Lex FridmanFrançois Chollet explains why neural networks only interpolate between points they have seen, and why the best AI systems pair them with symbolic rules.
11:59ENLex Fridman팟캐스트 · 2명From a Side Project to TensorFlow's Core: François Chollet on How Keras Came to Be게스트 François Chollet · 진행자 Lex FridmanThe creator of Keras recalls why he built it in 2015 around recurrent networks, how it moved onto TensorFlow, and what TensorFlow 2.0 means for its design.
11:23ENLex Fridman팟캐스트 · 2명Science Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence Explosion게스트 François Chollet · 진행자 Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.