필터 결과
23개정렬
1:04:50ENUseful AI Work Is Doubling Every Four Months. Schools Should Plan Accordingly.진행자 Nathan LabenzIn a keynote to K-12 educators, Cognitive Revolution host Nathan Labenz argued that task length, not chat quality, is the number that should reshape how schools think.
1:53:50ENA curriculum rewrite takes five years. AI does not wait.진행자 Nathan LabenzJohan Falk, a Swedish teacher-turned-analyst, argues that AI literacy and AI friends are more urgent than putting a tutor in every classroom.
1:01:49ENTwo Academic Hours a Day, and 96 Percent of Kids Say They Love School진행자 Sarah Guo, Elad GilTrilogy founder Joe Liemandt, now principal of Alpha School, tells No Priors how an AI tutor, a two-hour bargain with fifth graders, and a sports-coach ethos are meant to scale to a billion children.
1:22:31ENTwo Hours a Day, 99th Percentile: How Alpha School Made 2-Sigma Real진행자 Nathan LabenzMacKenzie Price explains how Alpha School uses adaptive apps—not chatbots—to finish core academics in two hours a day and still beat the growth models.
32:15ENThe Hardest Part of Learning Is Getting Yourself to Start진행자 Sarah Guo, Elad GilDuolingo CEO Luis von Ahn tells Sarah Guo how two-minute lessons, streaks, an 83 percent accuracy target, and large language models turned language study into a consumer product.
51:19ENSal Khan's $15 Tutor Hit 1.4 Million Users. Making Kids Actually Use It Is the Product진행자 Jacob EffronThe Khan Academy founder tells Redpoint why an AI tutor still cannot teach most students alone, and why districts are paying $15 a year after an $86 billion tutoring hangover.
47:42ENSal Khan Thinks Every Child Can Finally Have a Tutor진행자 Sarah Guo, Elad GilThe Khan Academy founder tells Sarah Guo and Elad Gil how a family tutoring side project became an AI teaching assistant, and why he believes education is on the cusp of its biggest positive shift.
1:23:40ENA $9 Tutor That Refuses to Give the Answer진행자 Nathan LabenzHow Khan Academy spent five months of GPT-4 early access building Khanmigo, a Socratic tutor shipped on the model's launch day.
2:44:16ENIQ at Eleven Predicted Survival at Eighty. Richard Haier on the Finding Psychology Tried to Bury게스트 Richard Haier · 진행자 Lex FridmanLex Fridman talks with psychologist Richard Haier about the g factor, The Bell Curve, group differences, and why intelligence research became the most radioactive topic in the field.
2:02:45ENSteal the Code: You Get Zero Points for Making the Algorithm Run진행자 Lex FridmanCharles Isbell and Michael Littman tell Lex Fridman why machine learning is not just statistics, why struggle has to stay hopeful, and what a $6,600 online master's actually sells.
2:08:26ENSatisfying Lectures Don't Stick: Grant Sanderson on Feynman, Fear, and Hitting Publish게스트 Grant Sanderson · 진행자 Lex FridmanThe 3Blue1Brown creator talks with Lex Fridman about why the nicest explanations can fail, why he envies Bell Labs, and why the experts most qualified to teach are the ones least willing to post.
1:10:16ENMost People See the Black Dress. Stephen Schwarzman Sees the White Lint.게스트 Stephen Schwarzman · 진행자 Lex FridmanThe Blackstone co-founder on pattern recognition, why entrepreneurship is a team sport, and why the United States cannot afford to sleepwalk through AI.
1:29:10ENAndrew Ng: The Hardest Part of AI Is Rarely the Algorithm게스트 Andrew Ng · 진행자 Lex FridmanIn a conversation with Lex Fridman, Ng traces a path from late-night Coursera recordings to factory floors, arguing that messy data, habits, and starting small matter more than hype.
1:18:35ENThe last job Sebastian Thrun wants is one he is already good at게스트 Sebastian Thrun · 진행자 Lex FridmanLex Fridman talks with the roboticist behind Stanley, Google’s self-driving car, Udacity, and Kitty Hawk about learning, safety, and why traffic is a choice.
49:53ENGilbert Strang still teaches with thick chalk and four subspaces게스트 Gilbert Strang · 진행자 Lex FridmanThe MIT linear-algebra legend talks with Lex Fridman about OpenCourseWare, singular values, neural nets as a million folds, and why calculus hogged the undergraduate timetable.
1:44:11ENA Single GPU Is Enough: Jeremy Howard on Teaching Deep Learning게스트 Jeremy Howard · 진행자 Lex FridmanThe fast.ai founder tells Lex Fridman why domain experts, not more papers or more TPUs, will decide whether deep learning actually helps people.
1:28:53ENMachine Learning for Business Students: Powerful, Narrow, and Easier to Fool Than You Think게스트 MIT Sloan · 진행자 Lex FridmanLex Fridman's non-technical MIT Sloan lecture explains how machine learning works, why it is so capable at perception, and where it still breaks in the real world.
48:02ENDon't Do AI for AI's Sake: Marily Nika's Starter Guide to AI Product Management게스트 Marily Nika · 진행자 Lenny RachitskyMeta product lead Marily Nika explains what a model is, when AI is worth building, how PMs can learn, and what working with research scientists is really like.
1:09:31ENThe Staircase to an AI Tutor: Connor Wick on Speak, Moats, and Voice-First Learning진행자 Jacob EffronHow Speak's CEO is building a fluency product one model-capability step at a time, from specialized speech models to evals, pricing, and the future of education.
51:39ENA Tutor for Every Child: Sal Khan on How AI Could Transform Education진행자 Sarah Guo, Elad GilKhan Academy's founder traces his path from tutoring his cousins to building Khanmigo, and explains how GPT-4, classroom change, universities and future skills fit together.
1:55:28ENPlan Your Chess Moves: Peter Deng on Building Products Used by Billions진행자 Lenny RachitskyThe former product leader at OpenAI, Instagram, Uber and Facebook explains how he scales products, assembles teams and hires people who eventually don't need him.
26:45ENStart 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.
1:02:06ENDeep 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.









