筛选结果
34 篇排序
1:30:54EN → 简体中文
Cognitive Revolution "How AI Changes Everything"· 2026年10月3日播客人形机器人跑赢了博尔特，DeepMind 仍将机器人评为 GPT-2 水平主持 Nathan LabenzGoogle DeepMind 的 Gemini Robotics 负责人讲述：为什么冲刺很容易、煎蛋很难，以及为什么一个大脑应该能驱动任何身体。
1:36EN → 简体中文为什么人形机器人能超越人类的动作限制Matthew Berman · Forward FutureMatthew Berman 解释，超越人类关节活动范围的旋转能力可能如何帮助机器人在工厂工作。
1:55:02EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2025年8月30日播客每天一百次实验，以及材料领域的“死亡之谷”主持 Nathan LabenzRadical AI 两位联合创始人向 Nathan Labenz 讲述，如何通过前沿模型与自驾实验室组成的闭环，把四十年的材料研发周期压缩到十年以内。
1:07:32EN → 简体中文
Unsupervised Learning: With Jacob Effron· 2024年12月18日播客o1 已是新一代模型，通用人工智能（AGI）到来那天可能平淡无奇主持 Jacob EffronOpenAI 前首席研究官 Bob McGrew 谈：为什么 o1 是 100 倍的跨越，为什么智能体（Agent）会卡在可靠性上，以及为什么 AGI 可能以办公室里的琐碎事务降临，而没有一个惊天动地的时刻。
1:50:08EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2024年8月7日播客Waymo 的受伤率低了五倍，却仍输给一辆 Lyft主持 Nathan Labenz人类司机平均每 1 亿英里才发生一起致命事故，这道门槛至今仍是自动驾驶争论的标尺。
1:16:11EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2024年7月11日播客把食物舀进餐盒，占了工厂人工的 70%主持 Nathan LabenzChef Robotics 首席执行官 Rajat Bhageria 谈 2000 万份装盘餐食、食品领域的基础模型，以及厨房为什么可能搬出住宅。
16:06EN → 简体中文AI 新闻：视频生成、Apple Intelligence、机器人与 DeepSeek Coder V2Matthew Berman · Forward FutureMatthew Berman 回顾一周的发布与研究，从 Luma、Runway 到 NVIDIA、机器人和编程模型。
0:53EN → 简体中文NVIDIA Computex 主题演讲：数字人、天气与机器人Matthew Berman · Forward FutureMatthew Berman 简要回顾黄仁勋关于 AI 数字人、天气建模、Blackwell 硬件和工厂机器人的发布内容。
1:40:07EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2023年7月4日播客每秒 30,000 次电压更新：深入 Skydio 的自主技术栈主持 Nathan LabenzHayk Martiros 带 Nathan Labenz 逐一了解一架约 0.9 公斤重的无人机：它能看向四面八方，机上运行十多个神经网络，而且不会被武器化。
1:01:17EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2023年5月9日播客比尔·盖茨拒绝的 3 亿美元，与流向 OpenAI 的 100 亿美元主持 Nathan LabenzRobert Scoble 和 Ben Tossell 与 Nathan Labenz 聊 AI 侦察、仍在沉睡的苹果神经网络引擎，以及老牌公司与 AI 原生创业公司谁会赢。
1:29:31EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2023年3月30日播客130,000 段遥操作演示：谷歌为机器人复刻的 GPT 打法主持 Nathan Labenz谷歌机器人团队的 Keerthana Gopalakrishnan 向 Nathan Labenz 讲述，RT-1、SayCan 和 PaLM-E 如何把语言模型的规模化，变成能进厨房干活的移动操作机器人。
1:00:38EN
Lex Fridman· 2017年12月6日播客The Race Car and the Rulebook: Chris Gerdes on Automated Vehicle Safety主持 Lex FridmanA Stanford engineer and former DOT innovation chief explains how autonomous racing, voluntary safety guidance, and data sharing could shape safer self-driving cars.
58:46ENLex Fridman播客Caging 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.
1:23:18ENCognitive Revolution "How AI Changes Everything"播客From 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.
10:17ENLex Fridman播客Why 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.
1:07:22ENLex Fridman播客Cars 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.
1:10:19ENUnsupervised Learning: With Jacob Effron播客The 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
Lenny's Podcast播客真正会改写农场、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。
1:13:18ENLex Fridman播客Human-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.
1:18:48ENLex Fridman播客Data 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.
1:05:15ENLex Fridman播客Taming 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.
1:27:15ENLex Fridman播客Policy 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.
17:35ENLex Fridman播客Hands 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.
1:13:22ENLex 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.加载更多已显示 24 篇，共 34 篇