필터 결과
113개정렬
37:00EN
Lex Fridman팟캐스트Inside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the Code진행자 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.
15:41ENLex Fridman팟캐스트Abolishing Involuntary Death: Ben Goertzel on Immortality, Pain and the Meaning of Life진행자 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.
1:31:52ENLex Fridman팟캐스트From Phonemes to CTC: How Deep Learning Rebuilt Speech Recognition진행자 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.
21:52ENLex Fridman팟캐스트The Secret Seminar: Eric Weinstein on What Happens When Academia Meets Someone It Can't Categorize진행자 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.
46:21ENLex Fridman팟캐스트Forty-Eight Miles, Twelve Thank-Yous: Lex Fridman Takes On the Goggins Challenge진행자 Lex FridmanLex Fridman runs four miles every four hours for 48 hours and records a gratitude reflection after each session.
17:40ENLex Fridman팟캐스트From AlphaGo Zero to MuZero: How Self-Play Strips Knowledge Out of AI진행자 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.
1:07:22ENLex Fridman팟캐스트Cars That Drive Empty: Emilio Frazzoli on Why Self-Driving Needs Rules, Not Just Data진행자 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.
1:10:12ENLex Fridman팟캐스트Grandpa Lex Visits Night City: A Rusty Gamer's First Hours in Cyberpunk 2077진행자 Lex FridmanLex Fridman plays the opening stretch of Cyberpunk 2077 on release day, narrating character creation, shaky driving, tutorials, cyberware surgery and his verdict.
1:07:35ENLex Fridman팟캐스트Why AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the Loop진행자 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.
1:13:18ENLex Fridman팟캐스트Human-Centered or Fully Autonomous? Lex Fridman's Case for Collaboration Between Driver and Machine진행자 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.
28:53ENLex Fridman팟캐스트Take Back MIT: Eric Weinstein on Broken Institutions and the Scientists Who Should Reclaim Them진행자 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.
1:11:50ENLex Fridman팟캐스트Data Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the Wheel진행자 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.
17:10ENLex Fridman팟캐스트Bitcoin’s puzzle is a Byzantine agreement machine진행자 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.
30:30ENLex Fridman팟캐스트From Bitcoin Magazine to Ethereum 2.0: Vitalik Buterin on Building a World Computer진행자 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.
1:18:48ENLex Fridman팟캐스트Data Movement, Not Math: How to Make Deep Learning Fit on Robots and Phones진행자 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.
16:41ENLex Fridman팟캐스트AlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and Biology진행자 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.
12:16ENLex Fridman팟캐스트Rule 30 and Richard Feynman: Stephen Wolfram on Intuition Versus Computational Experiment진행자 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.
1:19:52ENLex Fridman팟캐스트From Pixels to Steering Wheels: Convolutional Networks and End-to-End Driving진행자 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.
13:30ENLex Fridman팟캐스트Distance, Jabs and Three Rules: Georges St-Pierre Teaches Lex Fridman Wrestling and Ground Defense진행자 Lex FridmanA training session in which the former champion explains how takedowns and ground fighting differ between MMA and jiu-jitsu.
1:27:30ENLex Fridman팟캐스트From NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One Lecture진행자 Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.
1:05:15ENLex Fridman팟캐스트Taming the Long Tail: How Waymo Builds a Self-Driving Factory진행자 Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.
1:27:15ENLex Fridman팟캐스트Policy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement Learning진행자 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.
1:19:21ENLex Fridman팟캐스트Vapnik's Case for Learning by Predicates, Not Just by More Data진행자 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.
1:03:25ENLex Fridman팟캐스트Theano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your Math진행자 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.더 보기113개 중 24개 표시