필터 결과
319개정렬
20:36EN
Lex Fridman팟캐스트Why Simple Theories Win: Marcus Hutter on Occam's Razor, Compression and Kolmogorov Complexity진행자 Lex FridmanDeepMind researcher Marcus Hutter explains Solomonoff induction, why compression equals understanding, and how a simple universe can still look rich.
10:34ENLex Fridman팟캐스트 · 2명Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AI게스트 François Chollet · 진행자 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
Lenny's Podcast팟캐스트Fei-Fei Li on ImageNet, World Models and Why Everyone Has a Role in AI진행자 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 Fridman팟캐스트How Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene Segmentation진행자 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 Fridman팟캐스트Teaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous Vehicles진행자 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"팟캐스트Mamba After 90 Days: What 30 Papers Reveal About State Space Models, Hybrids, and Mixture of Experts진행자 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 Fridman팟캐스트Statistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Them진행자 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 Fridman팟캐스트Why Tesla AI Day Was the Most Impressive Real-World AI Effort Lex Fridman Has Seen진행자 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 Fridman팟캐스트 · 2명Ray Kurzweil on Hierarchies, the Neocortex and Merging with AI게스트 Ray Kurzweil · 진행자 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 Fridman팟캐스트 · 2명Circuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is Going게스트 Ilya Sutskever · 진행자 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"팟캐스트Robotics Hits Its GPT-3.5 Moment: Inside Gemini Robotics, From Embodied Reasoning to Origami진행자 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 Fridman팟캐스트Glitches as Features: What John Hopfield Says Artificial Neural Networks Are Missing진행자 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 Fridman팟캐스트 · 2명Reverse-Engineering the Child: Josh Tenenbaum's Case for Human-Like AI게스트 MIT AGI · 진행자 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 Fridman팟캐스트 · 2명How IBM Watson Won Jeopardy: A Moonshot Built on Engineering, Not a Breakthrough게스트 David Ferrucci · 진행자 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"팟캐스트One Set of Weights, Fourteen Medical Tasks: Inside Google's Multimodal Med-PaLM M진행자 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"팟캐스트Pause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next Upgrades진행자 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 Fridman팟캐스트 · 2명Start Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep Learning게스트 Andrew Ng · 진행자 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 Fridman팟캐스트Is Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving Cars진행자 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 Fridman팟캐스트Why Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised Learning진행자 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.
45:12EN
Sequoia Capital팟캐스트Inside 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.
1:27:41ENLex Fridman팟캐스트Deep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes Next진행자 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.
11:23ENLex Fridman팟캐스트 · 2명Science Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence Explosion게스트 François Chollet · 진행자 Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.
51:18ENLex Fridman팟캐스트 · 2명Engineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI Course게스트 MIT AGI · 진행자 Lex FridmanLex Fridman lays out an engineering-first approach to artificial general intelligence, previews the course's projects and guest speakers, and frames deep learning's limits.
1:02:06ENLex Fridman팟캐스트Deep Learning for Self-Driving Cars: Why Edge Cases and Human Nature Are the Real Problem진행자 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.