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1:04:14EN
Unsupervised Learning: With Jacob Effron· Jul 31, 2026PodcastThe Man Who Helped Stand Up Colossus in 120 Days Is Betting on an AI That Lives in Your HouseHost 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.AgentsAI startups & small teamsCompany Building+3
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· Jul 4, 2026Podcast19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipHost 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.Models & trainingLocal inferenceScaling Laws+3
1:32:38EN
Cognitive Revolution "How AI Changes Everything"· Dec 6, 2025PodcastPolish Is 1% of Frontier Training Data, So Poland Built Its Own ModelHost 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.Enterprise & strategyOpen-weight modelsData quality & curation+3
2:24:18EN
Cognitive Revolution "How AI Changes Everything"· Oct 30, 2024PodcastMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.Host 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.Models & trainingLocal inferenceEvals+3
1:16:11EN
Cognitive Revolution "How AI Changes Everything"· Jul 11, 2024PodcastScooping Food Into Boxes Is 70 Percent of Plant LaborHost 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 FridmanPodcastData Movement, Not Math: How to Make Deep Learning Fit on Robots and PhonesHost 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.Infrastructure & deploymentInferenceAI cost & performance+3