筛选结果
347 篇排序
1:13:22EN
Lex 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.
57:15ENLex Fridman播客Inside Torch and Autograd: How Deep Learning Libraries Compute Gradients主持 Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.
1:12:12EN → 简体中文
Lenny's Podcast播客模型是长出来的：Anthropic 研究员如何看见 Claude 的思考过程主持 Lenny RachitskyAnthropic 可解释性研究员 Emmanuel Ameisen 说明，查看语言模型的中间计算，能看见通用概念、提前规划和奇怪故障，这些发现也关系到对齐。
1:20:58ENLex Fridman播客From Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email Replies主持 Lex FridmanA Deep Learning School talk that builds sequence-to-sequence models step by step around one running example, automatic email reply, and then surveys memory and operation-augmented networks.
1:19:48ENLex Fridman播客Bigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep Learning主持 Lex FridmanIn a 2016 Deep Learning School talk, Andrew Ng distills lessons from leading Baidu's AI team into a workflow for diagnosing models, handling data and building a career.
1:02:06ENLex Fridman播客Learning TensorFlow Hands-On: From a Mystery Line to Handwritten Digits主持 Lex FridmanGoogle Brain's Sherry Moore walks a Deep Learning School audience through how TensorFlow works and two core labs, linear regression and MNIST digit recognition.
20:36ENLex Fridman播客Why Simple Theories Win: Marcus Hutter on Occam's Razor, Compression and Kolmogorov Complexity主持 Lex FridmanDeepMind researcher Marcus Hutter explains Solomonoff induction, why compression equals understanding, and how a simple universe can still look rich.
10:34ENLex Fridman播客 · 2 人对谈Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AI嘉宾 François Chollet · 主持 Lex FridmanFrançois Chollet explains why neural networks only interpolate between points they have seen, and why the best AI systems pair them with symbolic rules.
1:19:34EN → 简体中文Lenny's Podcast播客李飞飞谈 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。
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.
34:33ENLex Fridman播客Teaching 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.
1:16:52EN
Cognitive Revolution "How AI Changes Everything"播客Mamba After 90 Days: What 30 Papers Reveal About State Space Models, Hybrids, and Mixture of Experts主持 Nathan LabenzNathan Labenz and AI scout Jason Meaux survey the first wave of Mamba-inspired research, from mechanistic studies and synthetic-task tradeoffs to hybrid architectures and MoE variants.
10:39ENLex Fridman播客Statistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Them主持 Lex FridmanBerkeley's Michael I. Jordan explains where statistics came from, why it is really about decisions, and how empirical Bayes and false discovery rate bridge two rival philosophies.
11:29ENLex Fridman播客Why 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.
52:28ENLex Fridman播客 · 2 人对谈Ray Kurzweil on Hierarchies, the Neocortex and Merging with AI嘉宾 Ray Kurzweil · 主持 Lex FridmanIn an MIT AGI course lecture, Kurzweil connects the history of neural networks to his hierarchical theory of the neocortex, language understanding at Google, longevity, jobs and existential risk.
1:00:15ENLex Fridman播客 · 2 人对谈Circuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is Going嘉宾 Ilya Sutskever · 主持 Lex FridmanIn an MIT AGI lecture, OpenAI's co-founder explains why deep learning works, then walks through meta-learning, hindsight experience replay, sim-to-real transfer, self-play and learning goals from human feedback.
1:48:13ENCognitive Revolution "How AI Changes Everything"播客Robotics 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.
20:50ENLex Fridman播客Glitches as Features: What John Hopfield Says Artificial Neural Networks Are Missing主持 Lex FridmanIn a short conversation with Lex Fridman, physicist and neural-network pioneer John Hopfield compares evolved brains with today's artificial networks and explains why he expects AI to borrow from biology generation after generation.
1:35:09ENLex Fridman播客 · 2 人对谈Reverse-Engineering the Child: Josh Tenenbaum's Case for Human-Like AI嘉宾 MIT AGI · 主持 Lex FridmanMIT cognitive scientist Josh Tenenbaum argues that real machine intelligence needs models of the world, built by studying how babies think and learn, not just better pattern recognition.
32:41ENLex Fridman播客 · 2 人对谈How IBM Watson Won Jeopardy: A Moonshot Built on Engineering, Not a Breakthrough嘉宾 David Ferrucci · 主持 Lex FridmanDavid Ferrucci tells Lex Fridman how he talked IBM into the Jeopardy challenge and how his team built Watson's search, scoring and confidence machinery in about four years.
1:22:17ENCognitive Revolution "How AI Changes Everything"播客One Set of Weights, Fourteen Medical Tasks: Inside Google's Multimodal Med-PaLM M主持 Nathan LabenzVivek Natarajan and Tao Tu explain how Google built a single generalist biomedical model that reads images, genomics and text, and what it will take to put it in front of patients.
1:03:10ENCognitive Revolution "How AI Changes Everything"播客Pause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next Upgrades主持 Nathan LabenzNathan Labenz and Trey Kollmer walk through five recent papers on pausing, backspacing, prompting, streaming and long-context training, and sketch how they might combine into a more capable model.
26:45ENLex Fridman播客 · 2 人对谈Start Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep Learning嘉宾 Andrew Ng · 主持 Lex FridmanIn a clip from the Lex Fridman Podcast, Andrew Ng explains how to begin in deep learning, how to study effectively, and how to choose a career path.
1:31:29ENLex Fridman播客Is 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.加载更多已显示 24 篇，共 347 篇