필터 결과
9개정렬
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.
1:22:31ENTwo 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.
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.
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.
2:02:45ENSteal 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.
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.
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.
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.







