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1:04:14EN
Unsupervised Learning: With Jacob Effron· 2026年7月31日播客The Man Who Helped Stand Up Colossus in 120 Days Is Betting on an AI That Lives in Your House主持 Jacob EffronxAI co-founder Igor Babuschkin left to start River AI, arguing coding agents have crossed a threshold, closed labs are getting squeezed, and the next prize is personal, local intelligence.智能体AI startups & small teamsCompany Building+3
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· 2026年7月4日播客19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chip主持 Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.模型与训练Local inferenceScaling Laws+3
1:32:38EN
Cognitive Revolution "How AI Changes Everything"· 2025年12月6日播客Polish Is 1% of Frontier Training Data, So Poland Built Its Own Model主持 Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.企业与战略Open-weight modelsData quality & curation+3
2:24:18EN
Cognitive Revolution "How AI Changes Everything"· 2024年10月30日播客MMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.主持 Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.模型与训练Local inference评估+3
1:16:11EN
Cognitive Revolution "How AI Changes Everything"· 2024年7月11日播客Scooping Food Into Boxes Is 70 Percent of Plant Labor主持 Nathan LabenzChef Robotics CEO Rajat Bhageria on 20 million plated meals, a foundation model for food, and why the kitchen may leave the house.RoboticsData quality & curationLocal inference+3
1:18:48EN
Lex Fridman播客Data Movement, Not Math: How to Make Deep Learning Fit on Robots and Phones主持 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.基础设施与部署InferenceAI cost & performance+3