絞り込み結果
347 件並び順
57:15EN
Lex FridmanポッドキャストInside Torch and Autograd: How Deep Learning Libraries Compute Gradientsホスト Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.
1:12:12EN
Lenny's PodcastポッドキャストModels Are Grown, Not Built: What Anthropic's Interpretability Research Sees Inside Claudeホスト Lenny RachitskyAnthropic researcher Emmanuel Ameisen explains how studying a language model's intermediate computations reveals concepts, plans and surprising quirks, and why that matters for alignment.
1:20:58ENLex FridmanポッドキャストFrom Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email Repliesホスト 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ポッドキャストBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep Learningホスト 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ポッドキャストLearning TensorFlow Hands-On: From a Mystery Line to Handwritten Digitsホスト 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ポッドキャストWhy Simple Theories Win: Marcus Hutter on Occam's Razor, Compression and Kolmogorov Complexityホスト Lex FridmanDeepMind researcher Marcus Hutter explains Solomonoff induction, why compression equals understanding, and how a simple universe can still look rich.
10:34ENLex Fridmanポッドキャスト · 2 人Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIゲスト François Chollet · ホスト 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.
1:19:34ENLenny's PodcastポッドキャストFei-Fei Li on ImageNet, World Models and Why Everyone Has a Role in AIホスト Lenny RachitskyThe scientist often called the godmother of AI traces how big data sparked deep learning, explains why spatial intelligence is the next frontier, and shows what World Labs' first product, Marble, can do.
53:13ENLex FridmanポッドキャストHow Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene Segmentationホスト 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ポッドキャストTeaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous Vehiclesホスト 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"ポッドキャストMamba After 90 Days: What 30 Papers Reveal About State Space Models, Hybrids, and Mixture of Expertsホスト 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ポッドキャストStatistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Themホスト 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ポッドキャストWhy Tesla AI Day Was the Most Impressive Real-World AI Effort Lex Fridman Has Seenホスト 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ポッドキャスト · 2 人Ray Kurzweil on Hierarchies, the Neocortex and Merging with AIゲスト Ray Kurzweil · ホスト 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ポッドキャスト · 2 人Circuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is Goingゲスト Ilya Sutskever · ホスト 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"ポッドキャストRobotics Hits Its GPT-3.5 Moment: Inside Gemini Robotics, From Embodied Reasoning to Origamiホスト 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ポッドキャストGlitches as Features: What John Hopfield Says Artificial Neural Networks Are Missingホスト 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ポッドキャスト · 2 人Reverse-Engineering the Child: Josh Tenenbaum's Case for Human-Like AIゲスト MIT AGI · ホスト 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ポッドキャスト · 2 人How IBM Watson Won Jeopardy: A Moonshot Built on Engineering, Not a Breakthroughゲスト David Ferrucci · ホスト 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"ポッドキャストOne Set of Weights, Fourteen Medical Tasks: Inside Google's Multimodal Med-PaLM Mホスト 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"ポッドキャストPause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next Upgradesホスト 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ポッドキャスト · 2 人Start Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep Learningゲスト Andrew Ng · ホスト 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ポッドキャストIs Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving Carsホスト 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.
1:12:00ENLex FridmanポッドキャストWhy Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised Learningホスト Lex FridmanIn a 2016 Deep Learning School talk, Yoshua Bengio explains the assumptions that let neural nets beat the curse of dimensionality and lays out the open challenges ahead.もっと見る347 件中 24 件を表示