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10:34EN
Lex FridmanPodcast · 2 StimmenWhy Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIGast: François Chollet · Moderation: 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.Modelle & TrainingML-GrundlagenDeep Learning+2
1:19:34EN
Lenny's PodcastPodcastFei-Fei Li on ImageNet, World Models and Why Everyone Has a Role in AIModeration: 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.Modelle & TrainingWorld modelsVision & Video+3
53:13EN
Lex FridmanPodcastHow Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene SegmentationModeration: 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.Modelle & TrainingComputer VisionDeep Learning+3
34:33EN
Lex FridmanPodcastTeaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous VehiclesModeration: 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.Modelle & TrainingAutonomous DrivingVision & Video+3
1:16:52EN
Cognitive Revolution "How AI Changes Everything"PodcastMamba After 90 Days: What 30 Papers Reveal About State Space Models, Hybrids, and Mixture of ExpertsModeration: 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.Modelle & TrainingTransformers & attentionAI research
10:39EN
Lex FridmanPodcastStatistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between ThemModeration: 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.Modelle & TrainingML-GrundlagenDecision Making+2
11:29EN
Lex FridmanPodcastWhy Tesla AI Day Was the Most Impressive Real-World AI Effort Lex Fridman Has SeenModeration: Lex FridmanLex Fridman walks through the neural network, data annotation, Dojo and Tesla Bot announcements and what they say about solving real-world robotics.Modelle & TrainingAutonomous DrivingRobotics+3
52:28EN
Lex FridmanPodcast · 2 StimmenRay Kurzweil on Hierarchies, the Neocortex and Merging with AIGast: Ray Kurzweil · Moderation: 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.Modelle & TrainingAGIAI History+3
1:00:15EN
Lex FridmanPodcast · 2 StimmenCircuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is GoingGast: Ilya Sutskever · Moderation: 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.Modelle & TrainingML-GrundlagenDeep Learning+3
1:48:13EN
Cognitive Revolution "How AI Changes Everything"PodcastRobotics Hits Its GPT-3.5 Moment: Inside Gemini Robotics, From Embodied Reasoning to OrigamiModeration: 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.Modelle & TrainingRoboticsMultimodal AI+2
20:50EN
Lex FridmanPodcastGlitches as Features: What John Hopfield Says Artificial Neural Networks Are MissingModeration: 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.Modelle & TrainingNeuroscience & AIML-Grundlagen+3
1:35:09EN
Lex FridmanPodcast · 2 StimmenReverse-Engineering the Child: Josh Tenenbaum's Case for Human-Like AIGast: MIT AGI · Moderation: 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.Modelle & TrainingAGIML-Grundlagen+3
32:41EN
Lex FridmanPodcast · 2 StimmenHow IBM Watson Won Jeopardy: A Moonshot Built on Engineering, Not a BreakthroughGast: David Ferrucci · Moderation: 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.Modelle & TrainingAI HistorySearch & retrieval+1
1:22:17EN
Cognitive Revolution "How AI Changes Everything"PodcastOne Set of Weights, Fourteen Medical Tasks: Inside Google's Multimodal Med-PaLM MModeration: 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.Modelle & TrainingAI in healthcareMultimodal AI+3
1:03:10EN
Cognitive Revolution "How AI Changes Everything"PodcastPause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next UpgradesModeration: 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.Modelle & TrainingTransformers & attentionML-Grundlagen+3
26:45EN
Lex FridmanPodcast · 2 StimmenStart Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep LearningGast: Andrew Ng · Moderation: 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.Modelle & TrainingDeep LearningML-Grundlagen+3
1:31:29EN
Lex FridmanPodcastIs Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving CarsModeration: 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.Modelle & TrainingDeep LearningAutonomous Driving+3
1:12:00EN
Lex FridmanPodcastWhy Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised LearningModeration: 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.Modelle & TrainingML-GrundlagenDeep Learning+2
45:12EN
Sequoia CapitalPodcastInside 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.Modelle & TrainingRoboticsData quality & curation+2
1:27:41EN
Lex FridmanPodcastDeep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes NextModeration: 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.Modelle & TrainingDeep LearningAI research+3
11:23EN
Lex FridmanPodcast · 2 StimmenScience Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence ExplosionGast: François Chollet · Moderation: Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.Modelle & TrainingAGIAI research+3
51:18EN
Lex FridmanPodcast · 2 StimmenEngineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI CourseGast: MIT AGI · Moderation: 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.Modelle & TrainingAGIAI research+3
1:02:06EN
Lex FridmanPodcastDeep Learning for Self-Driving Cars: Why Edge Cases and Human Nature Are the Real ProblemModeration: 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.Modelle & TrainingAutonomous DrivingDeep Learning+3