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37:00EN
Lex FridmanPódcastInside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the CodePresenta: Lex FridmanYouTube's head of search and discovery, Cristos Goodrow, explains to Lex Fridman how search and recommendations work, which signals matter, and how the team measures whether viewers are better off.Modelos y entrenamientoRecommendation systemsSearch & retrieval+3
15:41EN
Lex FridmanPódcastAbolishing Involuntary Death: Ben Goertzel on Immortality, Pain and the Meaning of LifePresenta: Lex FridmanIn a clip from Lex Fridman's podcast, AI researcher Ben Goertzel argues that death and pain should be optional, and sketches what it would take to fund a serious attack on aging.PhilosophyNeuroscience & AIAGI
1:31:52EN
Lex FridmanPódcastFrom Phonemes to CTC: How Deep Learning Rebuilt Speech RecognitionPresenta: Lex FridmanBaidu's Adam Coates walks through the traditional speech pipeline, the CTC-based end-to-end engine that replaced much of it, and what it takes to scale and deploy it.Modelos y entrenamientoDeep LearningMultimodal AI+2
21:52EN
Lex FridmanPódcastThe Secret Seminar: Eric Weinstein on What Happens When Academia Meets Someone It Can't CategorizePresenta: Lex FridmanIn a clip from the Lex Fridman Podcast, mathematician Eric Weinstein recounts discovering a hidden seminar at Harvard and explains why he thinks institutions fail on novel cases.Science CommunicationCareer GrowthPhilosophy+2
46:21EN
Lex FridmanPódcastForty-Eight Miles, Twelve Thank-Yous: Lex Fridman Takes On the Goggins ChallengePresenta: Lex FridmanLex Fridman runs four miles every four hours for 48 hours and records a gratitude reflection after each session.Martial ArtsCombat SportsJiu Jitsu
17:40EN
Lex FridmanPódcastFrom AlphaGo Zero to MuZero: How Self-Play Strips Knowledge Out of AIPresenta: Lex FridmanDeepMind's David Silver explains why removing human knowledge made AlphaZero stronger and more general, and how MuZero extended the idea to worlds with unknown rules.Modelos y entrenamientoReinforcement LearningAI research+3
1:07:22EN
Lex FridmanPódcastCars That Drive Empty: Emilio Frazzoli on Why Self-Driving Needs Rules, Not Just DataPresenta: 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.Autonomous DrivingRoboticsAI startups & small teams+3
1:10:12EN
Lex FridmanPódcastGrandpa Lex Visits Night City: A Rusty Gamer's First Hours in Cyberpunk 2077Presenta: Lex FridmanLex Fridman plays the opening stretch of Cyberpunk 2077 on release day, narrating character creation, shaky driving, tutorials, cyberware surgery and his verdict.Storytelling
1:07:35EN
Lex FridmanPódcastWhy AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the LoopPresenta: Lex FridmanIn MIT 6.S093's opening lecture, Lex Fridman lays out five research areas of human-centered AI, from machine teaching to arguing machines that flag their own uncertainty.Modelos y entrenamientoHuman-AI CollaborationFundamentos de ML+3
1:13:18EN
Lex FridmanPódcastHuman-Centered or Fully Autonomous? Lex Fridman's Case for Collaboration Between Driver and MachinePresenta: 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.Autonomous DrivingSeguridad y gobernanzaRobotics+3
28:53EN
Lex FridmanPódcastTake Back MIT: Eric Weinstein on Broken Institutions and the Scientists Who Should Reclaim ThemPresenta: Lex FridmanIn a clip from their longer conversation, Eric Weinstein and Lex Fridman argue over whether MIT can be won back from its administrators, and what it means to be betrayed by the institutions we trust.Free SpeechPolitical PhilosophyScience Communication+1
1:11:50EN
Lex FridmanPódcastData Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the WheelPresenta: Lex FridmanIn an MIT lecture, Lex Fridman shows how deep learning can sense pedestrians, driver posture, gaze, emotion and cognitive load, and why that matters for human-centered autonomy.Multimodal y mediosDeep LearningVisión y vídeo+3
17:10EN
Lex FridmanPódcastBitcoin’s puzzle is a Byzantine agreement machinePresenta: Lex FridmanVitalik Buterin explains how proof of work turns a fault-tolerance thought experiment into a shared ledger, and why a 50 percent attack and quantum mining are less simple than they sound.Cryptocurrency
30:30EN
Lex FridmanPódcastFrom Bitcoin Magazine to Ethereum 2.0: Vitalik Buterin on Building a World ComputerPresenta: Lex FridmanVitalik Buterin traces Ethereum's origin in the Bitcoin community, the governance fights that followed, and the proof-of-stake and sharding upgrades still to come.Infraestructura y despliegueCryptocurrency
1:18:48EN
Lex FridmanPódcastData Movement, Not Math: How to Make Deep Learning Fit on Robots and PhonesPresenta: 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.Infraestructura y despliegueInferenceAI cost & performance+3
16:41EN
Lex FridmanPódcastAlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and BiologyPresenta: Lex FridmanA quick explainer on DeepMind's CASP14 result, how protein folding works, how AlphaFold 1 and 2 likely differ, and what the advance could unlock.Modelos y entrenamientoAI for scienceDeep Learning+2
12:16EN
Lex FridmanPódcastRule 30 and Richard Feynman: Stephen Wolfram on Intuition Versus Computational ExperimentPresenta: Lex FridmanWolfram recalls working with Feynman at Thinking Machines, why Feynman hid his calculations, and how building a computer language changed his approach to science.AI HistoryFirst PrinciplesScience Communication+1
1:19:52EN
Lex FridmanPódcastFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End DrivingPresenta: Lex FridmanLex Fridman's MIT 6.S094 lecture explains how convolutional neural networks work, where deep learning helps a self-driving car, and how to train a network to steer from video.Modelos y entrenamientoAutonomous DrivingDeep Learning+3
13:30EN
Lex FridmanPódcastDistance, Jabs and Three Rules: Georges St-Pierre Teaches Lex Fridman Wrestling and Ground DefensePresenta: Lex FridmanA training session in which the former champion explains how takedowns and ground fighting differ between MMA and jiu-jitsu.Martial ArtsCombat SportsJiu Jitsu
1:27:30EN
Lex FridmanPódcastFrom NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One LecturePresenta: Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.Modelos y entrenamientoReinforcement LearningDeep Learning+2
1:05:15EN
Lex FridmanPódcastTaming the Long Tail: How Waymo Builds a Self-Driving FactoryPresenta: Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.Modelos y entrenamientoAutonomous DrivingRobotics+3
1:27:15EN
Lex FridmanPódcastPolicy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement LearningPresenta: 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.Modelos y entrenamientoReinforcement LearningFundamentos de ML+3
1:19:21EN
Lex FridmanPódcastVapnik's Case for Learning by Predicates, Not Just by More DataPresenta: Lex FridmanIn an MIT lecture, Vladimir Vapnik argues that complete statistical learning needs two mechanisms, data and invariants, and that choosing smart predicates is where intelligence lives.Modelos y entrenamientoFundamentos de MLAI research+2
1:03:25EN
Lex FridmanPódcastTheano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your MathPresenta: Lex FridmanPascal Lamblin of MILA walks through how Theano builds, differentiates, optimizes and runs computation graphs, then trains logistic regression, a convnet and an LSTM.Modelos y entrenamientoDeep LearningFundamentos de ML+3
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