필터 결과
14개정렬
2:45:10EN
Lex Fridman· 2022년 1월 22일팟캐스트 · 2명A 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.
2:30:30ENLex Fridman· 2021년 7월 31일팟캐스트 · 2명The 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.
1:41:37ENLex Fridman· 2020년 7월 21일팟캐스트 · 2명The 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.
1:22:51ENLex Fridman· 2020년 5월 19일팟캐스트 · 2명Density, 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.
56:47ENLex Fridman· 2019년 9월 8일팟캐스트 · 2명Why 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.
1:09:11ENLex Fridman· 2019년 6월 10일팟캐스트 · 2명Don'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.
32:45ENLex Fridman· 2019년 4월 12일팟캐스트 · 2명Elon 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.
1:25:16ENLex Fridman팟캐스트From 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.
54:33ENLex Fridman팟캐스트 · 2명Vision 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.
1:11:50ENLex Fridman팟캐스트Data 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.
1:19:52ENLex Fridman팟캐스트From 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.
1:13:22ENLex Fridman팟캐스트The 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.
15:43ENLex Fridman팟캐스트 · 2명Inside 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.
53:13ENLex Fridman팟캐스트How 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.