筛选结果
65 篇排序
15:43ENInside 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.
37:18ENFrom Intelligent Copilots to Aurora: Sterling Anderson on Shared Control and Building a Self-Driving Company主持 Lex FridmanA guest lecture in MIT's 6.S094 course tracing Anderson's path from shared human-machine control research, through Tesla Autopilot, to co-founding Aurora.
1:13:40ENThe Long Tail, the World Model, and the 1995 Problem: Inside Waymo's Scaling Phase and Robotics' Missing Moment主持 Jacob EffronVincent, a distinguished engineer at Waymo, explains how foundation models became teachers for self-driving, why the long tail and causality are the real frontier, and where robotics still lacks its generalist proof point.
53:13ENHow 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.
34:33ENTeaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous Vehicles主持 Lex FridmanLex Fridman's MIT 6.S094 lecture on using driver-facing cameras and neural networks to understand body pose, gaze, emotion and cognitive load.
17:32ENOver-Automating, Googling Doctors and Neural Nets: George Hotz Reviews Programming Memes主持 Lex FridmanLex Fridman and George Hotz rate printed programming memes, and the jokes turn into candid talk about testing, hiring, social media and the power of neural networks.
11:29ENWhy Tesla AI Day Was the Most Impressive Real-World AI Effort Lex Fridman Has Seen主持 Lex FridmanLex Fridman walks through the neural network, data annotation, Dojo and Tesla Bot announcements and what they say about solving real-world robotics.
1:31:29ENIs Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving Cars主持 Lex FridmanThe first lecture of MIT 6.S094 introduces neural networks, reinforcement learning and computer vision through the question of how hard driving really is.
1:02:24ENFrom Looping Forklifts to Self-Driving Cities: Sertac Karaman on Motion Planning主持 Lex FridmanAn MIT professor traces motion planning from the DARPA Urban Challenge to the future of transportation and the cities built around cars.
1:27:41ENDeep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes Next主持 Lex FridmanAn MIT lecture surveys the biggest advances of 2017 to 2019 across language, reinforcement learning, deep learning theory and autonomous vehicles, and lists Fridman's hopes for the year ahead.
1:04:39ENFrom Udacity's Self-Driving Car Course to a Startup for Retirement Communities: Oliver Cameron on Building Voyage主持 Lex FridmanVoyage CEO Oliver Cameron tells the unconventional story of how a self-driving curriculum became a car, then a company that deploys in retirement communities, and what he learned along the way.
1:02:06ENDeep Learning for Self-Driving Cars: Why Edge Cases and Human Nature Are the Real Problem主持 Lex FridmanLex Fridman opens MIT's 6.S094 with a tour of what deep learning is, how it works, what it has achieved and where it still breaks, framed around autonomous vehicles.







