筛选结果
117 篇排序
24:22ENCraftsmanship, First Principles and the Road to Self-Driving: Jim Keller on Working with Elon Musk嘉宾 Jim Keller · 主持 Lex FridmanThe veteran chip architect explains why he thinks autonomous driving is a tractable engineering problem, how Tesla's cost constraints shaped its hardware, and what he took from Musk's way of thinking.
54:33ENVision 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.
11:11ENYann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not General嘉宾 Yann Lecun · 主持 Lex FridmanIn a 2019 clip, LeCun defends benchmarks as a test of AI claims, explains why interactive environments break the usual train-test protocol, and argues that "general intelligence" is a misnomer.
58:46ENCaging the Neural Network: How Aptiv Thinks About Safe Self-Driving主持 Lex FridmanAptiv Autonomous Mobility's Karl Iagnemma and Oscar Beijbom explain why end-to-end learning is hard to trust in cars, and show how PointPillars and nuScenes push lidar perception forward.
1:23:18ENFrom GPT-2 to GPT-3 Moments in Robotics: Six DeepMind Papers on Bridging Robots and the Internet主持 Nathan LabenzKeerthana Gopalakrishnan and Ted Xiao walk through RT-2, RT-X, RT-Trajectory, AutoRT, Learning to Learn Faster and PIVOT, and say what is still missing for general-purpose robots.
10:17ENWhy Great Robots Fail as Businesses, and What iRobot Got Right主持 Lex FridmaniRobot co-founder Colin Angle explains why so many robotics companies collapse and how cost, vision and a clear user need shaped the Roomba.
1:07:22ENCars That Drive Empty: Emilio Frazzoli on Why Self-Driving Needs Rules, Not Just Data主持 Lex FridmanThe nuTonomy CTO explains why full autonomy and shared fleets are where the value lies, and why the hardest open problem is deciding how vehicles should behave.
1:10:19ENThe Robot That Folded the Laundry: Physical Intelligence’s Three Frontiers主持 Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.
1:24:24EN → 简体中文真正会改写农场、矿场和公路的,是装进重型机器里的智能主持 Lenny RachitskyApplied Intuition 联合创始人 Qasar Younis 认为,未来五到十年 AI 最大的冲击会落在车辆和工业机械上,并讲了这家安静做到 150 亿美元估值的公司如何定价值观、读书、做管理。
1:13:18ENHuman-Centered or Fully Autonomous? Lex Fridman's Case for Collaboration Between Driver and Machine主持 Lex FridmanLecture 2 of MIT's 2018 course on deep learning for self-driving cars covers the two paths to autonomy, how sensors compare, who is building what, and where deep learning can help.
1:18:48ENData Movement, Not Math: How to Make Deep Learning Fit on Robots and Phones主持 Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.
1:05:15ENTaming the Long Tail: How Waymo Builds a Self-Driving Factory主持 Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.
1:27:15ENPolicy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement Learning主持 Lex FridmanOpenAI's John Schulman walks through the core methods of deep RL, from the score function estimator to TRPO and DQN, and says when each one is worth using.
17:35ENHands Off the Wheel in San Diego: A Comma Two Demo Drive and a Tour of comma.ai's Hardware Shop主持 Lex FridmanAfter their podcast conversation, George Hotz takes Lex Fridman on an openpilot drive and then through the comma.ai offices, dashboards, assembly line and compute cluster.
1:13:22ENThe 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.
18:46ENA Brooding Stuffed Hedgehog and Lex Fridman's Case for Owning Less主持 Lex FridmanIn an AMA, Lex Fridman uses his thrift-store hedgehog to talk about minimalism, anthropomorphism in robotics, and why shared experience binds people together.
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.
1:39:10EN → 简体中文键盘之后才是正戏:硬件老将眼里的物理 AI 浪潮主持 Lenny Rachitsky先后在 Apple、Meta、OpenAI 做硬件的 Caitlin Kalinowski 解释机器人热潮从何而来、量产真正卡在哪、以及怎样把硬件做出货。
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.
1:19:34EN → 简体中文李飞飞谈 ImageNet、世界模型,以及为什么每个人都该在 AI 里有位置主持 Lenny Rachitsky常被称为「AI 教母」的科学家,回溯大数据如何点燃深度学习,解释空间智能为何是下一站,并演示 World Labs 的第一款产品 Marble。
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.
1:48:13ENRobotics Hits Its GPT-3.5 Moment: Inside Gemini Robotics, From Embodied Reasoning to Origami主持 Nathan LabenzGoogle DeepMind researchers Keerthana Gopalakrishnan and Ted Xiao explain how Gemini Robotics works, what it can and cannot do, and where the field goes next.
26:51ENA Walking Skeleton in a Beautiful Face: Ben Goertzel on What Sophia Really Is主持 Lex FridmanThe former Hanson Robotics chief scientist explains how Sophia works, why she is not AGI, and why he thinks the illusion can still be put to good use.
45:12ENInside a Robot Data Factory: How Mecka Sensorizes Humans to Train RobotsMecka AI founder Josh Gao explains how a company collects, quality-checks and licenses egocentric human data, and why robotics will arrive as a steady unlock of tasks rather than a single breakthrough.
加载更多已显示 24 篇,共 117 篇








