필터 결과
10개정렬
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.
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.
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.
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.









