筛选结果
14 篇排序
2:45:10EN
Lex Fridman· 2022年1月22日播客 · 2 人对谈A Teenager Learns to Drive in Twenty Hours. A Car Still Cannot.嘉宾 Yann Lecun · 主持 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.
2:30:30ENLex Fridman· 2021年7月31日播客 · 2 人对谈The Fallacy of the Successful First Step嘉宾 Ishan Misra · 主持 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.
1:41:37ENLex Fridman· 2020年7月21日播客 · 2 人对谈The Fallacy of the Successful First Step: Jitendra Malik on Why Seeing Still Isn't Solved嘉宾 Jitendra Malik · 主持 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.
1:22:51ENLex Fridman· 2020年5月19日播客 · 2 人对谈Density, Not Demos: Sertac Karaman on Robots That Fly and Drive嘉宾 Sertac Karaman · 主持 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.
56:47ENLex Fridman· 2019年9月8日播客 · 2 人对谈Why Vijay Kumar Refuses to Call Them Drones嘉宾 Vijay Kumar · 主持 Lex FridmanThe Penn roboticist on swarm design, true autonomy, and why batteries — not algorithms — still limit the sky.
1:09:11ENLex Fridman· 2019年6月10日播客 · 2 人对谈Don't Ship Autopilot: Gavin Miller on AI That Leaves Creatives in Charge嘉宾 Gavin Miller · 主持 Lex FridmanAdobe Research's head tells Lex Fridman why smart defaults, fixable errors, and intern-scale bets matter more than a fully automatic Photoshop.
32:45ENLex Fridman· 2019年4月12日播客 · 2 人对谈Elon Musk: Every Driver Input Is an Error Tesla Autopilot Must Eliminate嘉宾 Elon Musk · 主持 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.
1:25:16ENLex Fridman播客From Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional Networks主持 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.
54:33ENLex Fridman播客 · 2 人对谈Vision or Lidar, Human or Machine: Lex Fridman's 2019 Map of the Self-Driving Race嘉宾 Self-Driving Cars · 主持 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.
1:11:50ENLex Fridman播客Data Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the Wheel主持 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.
1:19:52ENLex Fridman播客From Pixels to Steering Wheels: Convolutional Networks and End-to-End Driving主持 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.
1:13:22ENLex Fridman播客The Second 90%: What It Really Takes to Put Deep Learning on a Driverless Car主持 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.
15:43ENLex Fridman播客 · 2 人对谈Inside RIDER: The Rugged Black Box Behind MIT's Large-Scale Driving Study嘉宾 MIT-AVT · 主持 Lex FridmanA walkthrough of the data-collection device MIT-AVT uses to record drivers and vehicles in semi-autonomous cars month after month.
53:13ENLex Fridman播客How Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene Segmentation主持 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.