필터 결과
175개정렬
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.
1:29:10ENAndrew Ng: The Hardest Part of AI Is Rarely the Algorithm게스트 Andrew Ng · 진행자 Lex FridmanIn a conversation with Lex Fridman, Ng traces a path from late-night Coursera recordings to factory floors, arguing that messy data, habits, and starting small matter more than hype.
1:39:57ENPeople Say They Want Perfect Robots. They Trust the Ones That Push Back게스트 Ayanna Howard · 진행자 Lex FridmanAyanna Howard on human-robot interaction, the ethics of safety-critical code, and why a little imperfection is what actually earns trust.
1:18:35ENThe last job Sebastian Thrun wants is one he is already good at게스트 Sebastian Thrun · 진행자 Lex FridmanLex Fridman talks with the roboticist behind Stanley, Google’s self-driving car, Udacity, and Kitty Hawk about learning, safety, and why traffic is a choice.
1:16:58ENFear of Robots Is a Champagne Problem, Says Whitney Cummings게스트 Whitney Cummings · 진행자 Lex FridmanThe comedian talks with Lex Fridman about Bear Claw, sex robots, factory farms, codependency, and terror management.
39:33ENMars is not Plan B: Dava Newman on spacesuits, autonomy, and Spaceship Earth게스트 Dava Newman · 진행자 Lex FridmanThe MIT Apollo professor and former NASA deputy administrator on Magellan’s crew, mechanical counterpressure, and why the next decade belongs to Earth.
37:38ENThe Best-Selling Vacuum in America Is a Robot게스트 Colin Angle · 진행자 Lex FridmaniRobot CEO Colin Angle on why most robotics companies die, why a Roomba is costed by weight, and why printing privacy promises on the box would hurt sales.
56:47ENWhy Vijay Kumar Refuses to Call Them Drones게스트 Vijay Kumar · 진행자 Lex FridmanThe Penn roboticist on swarm design, true autonomy, and why batteries — not algorithms — still limit the sky.
1:13:06ENLockheed Martin's CTO on Autonomy, the Moon, and the Culture of Not Getting It Wrong게스트 Keoki Jackson · 진행자 Lex FridmanKeoki Jackson tells Lex Fridman why going back to the Moon is the easy part—and why proving a learning system will still do the right thing is not.
1:59:37ENLane Centering Is the Only Self-Driving Feature That Already Helps게스트 George Hotz · 진행자 Lex Fridmancomma.ai founder George Hotz tells Lex Fridman why staying in the lane beats Level 5 promises, and why a robotaxi stack still has no network effect.
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.
1:01:24ENLeslie 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.
55:24ENKyle Vogt Built the Highway Kit, Then Scrapped It Before Selling a Single One게스트 Kyle Vogt · 진행자 Lex FridmanThe Cruise president and CTO tells Lex Fridman why a BattleBots kid from Kansas walked away from a working product, partnered with GM, and is spending 2019 grinding from prototype to production.
42:45ENA one-legged robot learned a backflip from "this is better"게스트 Pieter Abbeel · 진행자 Lex FridmanPieter Abbeel tells Lex Fridman why preference comparisons, self-play, ten-minute demos, and messy ensembles of simulators are the practical path through deep reinforcement learning.
1:00:38ENThe Race Car and the Rulebook: Chris Gerdes on Automated Vehicle Safety진행자 Lex FridmanA Stanford engineer and former DOT innovation chief explains how autonomous racing, voluntary safety guidance, and data sharing could shape safer self-driving cars.
24:22ENCraftsmanship, First Principles and the Road to Self-Driving: Jim Keller on Working with Elon Musk게스트 Jim Keller · 진행자 Lex FridmanThe veteran chip architect explains why he thinks autonomous driving is a tractable engineering problem, how Tesla's cost constraints shaped its hardware, and what he took from Musk's way of thinking.
54:33ENVision or Lidar, Human or Machine: Lex Fridman's 2019 Map of the Self-Driving Race게스트 Self-Driving Cars · 진행자 Lex FridmanThe opening lecture of MIT's 6.S094 course surveys 2018's milestones, the industry's optimistic predictions, and the beliefs about driving and humans that will decide who wins.
11:11ENYann 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.
58:46ENCaging the Neural Network: How Aptiv Thinks About Safe Self-Driving진행자 Lex FridmanAptiv Autonomous Mobility's Karl Iagnemma and Oscar Beijbom explain why end-to-end learning is hard to trust in cars, and show how PointPillars and nuScenes push lidar perception forward.
1:23:18ENFrom GPT-2 to GPT-3 Moments in Robotics: Six DeepMind Papers on Bridging Robots and the Internet진행자 Nathan LabenzKeerthana Gopalakrishnan and Ted Xiao walk through RT-2, RT-X, RT-Trajectory, AutoRT, Learning to Learn Faster and PIVOT, and say what is still missing for general-purpose robots.
10:17ENWhy Great Robots Fail as Businesses, and What iRobot Got Right진행자 Lex FridmaniRobot co-founder Colin Angle explains why so many robotics companies collapse and how cost, vision and a clear user need shaped the Roomba.
1:07:22ENCars That Drive Empty: Emilio Frazzoli on Why Self-Driving Needs Rules, Not Just Data진행자 Lex FridmanThe nuTonomy CTO explains why full autonomy and shared fleets are where the value lies, and why the hardest open problem is deciding how vehicles should behave.
1:10:19ENThe Robot That Folded the Laundry: Physical Intelligence’s Three Frontiers진행자 Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.
1:24:24ENIntelligence for Heavy Machines: Qasar Younis on Why Physical AI Will Change Farms, Mines and Roads First진행자 Lenny RachitskyThe Applied Intuition co-founder explains why the biggest near-term AI impact is in vehicles and industrial machines, and shares the values, reading habits and management philosophy behind a quiet $15 billion company.
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