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1:40:44ENGive the Model Time, Memory, and Tools: Nathan Labenz’s 2023 Field Guide to Using GPT-4진행자 Nathan LabenzPart two of the AI Scouting Report walks from H100 clusters and RLHF through chain-of-thought, retrieval, agents, and mixture-of-experts.
59:41ENHumans Are Single-Threaded. MULTI·ON Wants 100 Agents Running Anyway진행자 Nathan LabenzStanford PhD dropout Div Garg put a personal agent in Chrome so it can reuse your logins, ask for help when it stalls, and stop before it hits Buy.
1:40:07ENThirty thousand voltage updates a second: inside Skydio’s autonomy stack진행자 Nathan LabenzHayk Martiros walks Nathan Labenz through a two-pound drone that sees in every direction, runs more than ten neural nets on board, and will not be weaponized.
1:30:29ENGPT-4 Wins Ten Times More Bill Fights: DoNotPay's Robot-Lawyer War진행자 Nathan LabenzGPT-3 thanked Comcast for twenty dollars off. GPT-4 said that was not enough — and the robot lawyer called it an arms race.
51:55ENNine orders of magnitude later, he shipped a companion, not an oracle진행자 Sarah Guo, Elad GilDeepMind co-founder Mustafa Suleyman tells No Priors why Inflection’s first product is a personal AI that sits in your corner, and why the missing piece is a router, not a bigger model.
58:26ENSeventy Percent of the Inbox Is a Pitch: Matt Welsh on the First Wave of AI Agents진행자 Nathan LabenzThe Fixie.ai founder maps copilot versus delegation, the missing appendages into our systems, and why agents will need guardrails, memory hygiene, and probably an earpiece.
1:29:31EN130,000 Teleop Demos: Google's GPT Playbook for Robots진행자 Nathan LabenzKeerthana Gopalakrishnan of Google Robotics tells Nathan Labenz how RT-1, SayCan, and PaLM-E turn language-model scaling into kitchen-ready mobile manipulators.
1:25:46ENDon't Make the Tools Agentic: Flo Crivello on What Production Agents Actually Need진행자 Nathan LabenzThe Lindy CEO tells Nathan Labenz that most working agents are intelligent workflows with human-designed control flow — and that scaffolding may still matter even if AGI arrives.
4:57ENHow Microsoft Research Used ChatGPT to Guide Robots and DronesMatthew Berman · Forward FutureA 2023 research demonstration combines natural-language instructions, robot APIs and human review to generate code for physical and simulated tasks.
1:00:36ENForty games of Diplomacy, and nobody spotted the bot: Noam Brown’s 100,000x lesson진행자 Sarah Guo, Elad GilMeta’s Noam Brown tells Sarah Guo and Elad Gil how Cicero negotiated in English for 40 games on a tiny web community — and why thinking at decision time beat three years of scaling.
2:29:21ENThe Poker Bot That Never Tried to Read a Soul게스트 Noam Brown · 진행자 Lex FridmanNoam Brown tells Lex Fridman how a Nash-equilibrium bot crushed heads-up no-limit Hold'em, how Pluribus did it six-handed, and why Diplomacy required an AI that could earn human trust.
3:12:55ENThe IQ required to destroy the world drops by one point every year게스트 George Hotz · 진행자 Lex FridmanGeorge Hotz on Fermi's paradox, hacking the simulation, and teaching cars to drive with neural nets.
4:08:58ENThe Man Who Named AGI Wants Superintelligence Raised on Compassion게스트 Ben Goertzel · 진행자 Lex FridmanLex Fridman talks with Ben Goertzel for four hours about OpenCog, Sophia, SingularityNET, and why a bigger neural net is not a mind.
1:12:36ENKate Darling on Why We Name the Roomba and Mourn the Bomb Robot게스트 Kate Darling · 진행자 Lex FridmanAn MIT Media Lab researcher argues that our feelings about machines track the history of animal domestication — and that design can steer that instinct for better or worse.
1:38:33ENMaybe people are rational under different assumptions게스트 Anca Dragan · 진행자 Lex FridmanAnca Dragan talks with Lex Fridman about inverse reinforcement learning, autonomous driving, and why a written reward is evidence rather than law.
1:39:56ENWhy Marcus Hutter Treats Compression as Intelligence Itself게스트 Marcus Hutter · 진행자 Lex FridmanThe DeepMind researcher behind AIXI tells Lex Fridman why a Wikipedia compression contest, Solomonoff induction, and an incomputable formula belong in the same conversation.
1:45:58ENNine of ten colleagues said far-field speech was impossible게스트 Rohit Prasad · 진행자 Lex FridmanRohit Prasad, Amazon’s vice president and head scientist for Alexa, talks with Lex Fridman about conversational AI, the Alexa Prize, trust, and why a 20-minute chat is still years away.
1:23:01ENYou Cannot Ask a Question You Have No Words For게스트 Judea Pearl · 진행자 Lex FridmanA teenager spent three days in bed with a fever after learning that geometry could be done in algebra — then spent a career giving cause and effect a mathematics.
44:48ENChris Urmson: The Path to Driverless Cars Splits Away From the One That Lets You Nap게스트 Chris Urmson · 진행자 Lex FridmanLex Fridman talks with Aurora CEO Chris Urmson about the DARPA races, why lidar is a tool rather than a crutch, and why Level 2 economics will not grow into true autonomy.
1:46:01ENThe Night AlphaStar Beat TLO, the Team Went to a Bar게스트 Oriol Vinyals · 진행자 Lex FridmanOriol Vinyals on how DeepMind turned StarCraft II into a sequence problem, built a league of agents, and still refuses to call the game solved.
1:01:24ENLeslie Kaelbling: You Buy the Ticket Before You Can Plan the Airport게스트 Leslie Kaelbling · 진행자 Lex FridmanThe MIT roboticist tells Lex Fridman why abstractions, belief-space control, and built-in bias matter more than picking a tribe of symbols or neural nets.
1:06:17ENLibratus Beat the World's Best Poker Players Without a Neural Network게스트 Tuomas Sandholm · 진행자 Lex FridmanTuomas Sandholm tells Lex Fridman how game theory, abstraction, and search — not deep learning — took heads-up no-limit Texas Hold'em, and why the harder work is deploying that toolkit outside the casino.
1:45:49ENTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the Company진행자 Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.
1:21:44ENFrom Rowing to Steering: Inside OpenAI's Push Toward Persistent AI Coworkers진행자 Lenny RachitskyTara Seshan, who leads product for Codex and ChatGPT Work at OpenAI, explains how PM work changes when agents do the rowing and people do the steering.
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