필터 결과
19개정렬
1:29:02ENFrom 50 Designs a Day to 1,500: How AI Changed What Jaguar Land Rover Can Explore진행자 Nathan LabenzNeural Concept’s Thomas von Tschammer on specialist physics models, engineering copilots, and why Formula 1 already runs thousands of designs overnight.
1:50:08ENWaymo's Injury Rate Is Five Times Lower. It Still Loses to a Lyft.진행자 Nathan LabenzHuman drivers average one fatal crash per 100 million miles — and that bar still defines the self-driving debate.
1:16:11ENScooping Food Into Boxes Is 70 Percent of Plant Labor진행자 Nathan LabenzChef Robotics CEO Rajat Bhageria on 20 million plated meals, a foundation model for food, and why the kitchen may leave the house.
1:20:44ENSeeing Like a Mamba: Vision, Rotting States, and a Cell Model in 90 Days진행자 Nathan LabenzNathan Labenz and Jason Meaux survey the vision papers, long-context experiments, and biology work that followed the original Mamba release.
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.
2:45:10ENA Teenager Learns to Drive in Twenty Hours. A Car Still Cannot.게스트 Yann Lecun · 진행자 Lex FridmanYann LeCun tells Lex Fridman why self-supervised world models are the missing dark matter of intelligence, and why machines are not even at the level of a house cat.
2:30:30ENThe Fallacy of the Successful First Step게스트 Ishan Misra · 진행자 Lex FridmanBerkeley's Jitendra Malik explains why computer vision still looks easy, why fully autonomous driving is not near, and what children know that today's networks do not.
1:41:37ENThe Fallacy of the Successful First Step: Jitendra Malik on Why Seeing Still Isn't Solved게스트 Jitendra Malik · 진행자 Lex FridmanBerkeley's Jitendra Malik tells Lex Fridman why computer vision keeps underestimating itself, why full self-driving still needs cognition, and why vision is the parent of language.
1:22:51ENDensity, Not Demos: Sertac Karaman on Robots That Fly and Drive게스트 Sertac Karaman · 진행자 Lex FridmanThe MIT roboticist and Optimus Ride co-founder on why sharing space with people is harder than building the machine, and why flying at transportation scale is still the tougher problem.
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.
1:09:11ENDon't Ship Autopilot: Gavin Miller on AI That Leaves Creatives in Charge게스트 Gavin Miller · 진행자 Lex FridmanAdobe Research's head tells Lex Fridman why smart defaults, fixable errors, and intern-scale bets matter more than a fully automatic Photoshop.
32:45ENElon Musk: Every Driver Input Is an Error Tesla Autopilot Must Eliminate게스트 Elon Musk · 진행자 Lex FridmanAfter MIT published a study of functional vigilance on Autopilot, Lex Fridman sat down with Musk to argue about cameras on drivers, fleet data, and whether humans still belong in the loop.
1:25:16ENFrom Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional Networks진행자 Lex FridmanA 2016 Deep Learning School talk covering how convnets work, how the ImageNet winners evolved, and how to apply them without being a hero.
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.
1:11:50ENData Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the Wheel진행자 Lex FridmanIn an MIT lecture, Lex Fridman shows how deep learning can sense pedestrians, driver posture, gaze, emotion and cognitive load, and why that matters for human-centered autonomy.
1:19:52ENFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End Driving진행자 Lex FridmanLex Fridman's MIT 6.S094 lecture explains how convolutional neural networks work, where deep learning helps a self-driving car, and how to train a network to steer from video.
1:13:22ENThe Second 90%: What It Really Takes to Put Deep Learning on a Driverless Car진행자 Lex FridmanWaymo's Sacha Arnoud walks MIT students through Waymo's history, its perception stack, and the industrial-scale labeling, compute and testing behind it.
15:43ENInside RIDER: The Rugged Black Box Behind MIT's Large-Scale Driving Study게스트 MIT-AVT · 진행자 Lex FridmanA walkthrough of the data-collection device MIT-AVT uses to record drivers and vehicles in semi-autonomous cars month after month.
53:13ENHow Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene Segmentation진행자 Lex FridmanLex Fridman's MIT 6.S094 lecture walks from the basics of image classification through landmark network architectures to pixel-level segmentation, optical flow and the SegFuse competition.






