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월간
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다음
Chelsea Finn: This is the State of the Art in Robotics 59 분 
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Why Everything Weighs Less in Antarctica 12 분 
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How Tech Companies Lie to You. 25 분 
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NASA Found A Very Strange Star 1 분 
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The Pi Song 4.0 (Memorize 400 Digits Of π) 6 분 
Lasers vs Lightning- Which Is More Powerful? 29 분 
I spent 3 days at MIT... the robot hype is worse than you think 8 분 
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Plato’s infamous Ship of State thought experiment - Michael Vazquez 6 분 
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NVIDIA CEO Jensen Huang's Vision for the Future 64 분 
The Massive Machine Hidden in the Deep Ocean 16 분 
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Prohibition - OverSimplified 34 분 
The Macklemore controversy rocking Ed Sheeran’s tour 3 분 
72 Hours Lost in the World’s Richest Dictatorship (no hotel, no food, no transportation...) 38 분 
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The Secret Tunnel Air Compressor!!! 3 분 
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Hasan Piker on what Democrats get wrong | America, Actually 34 분 
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SECRET Garage Car Lift Part 1 30 분 
Why Fast Food Got So Expensive 20 분 Chelsea Finn: This is the State of the Art in Robotics
2026 59 분
Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. Transcript: https://www.ycrootaccess.com/p/chelsea-finn-on-the-next-decade-in Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Chapters: 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A
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