絞り込み結果
7 件並び順
32:20ENWho Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.
1:04:14EN
Unsupervised Learning: With Jacob Effron· 2026年7月31日ポッドキャストThe Man Who Helped Stand Up Colossus in 120 Days Is Betting on an AI That Lives in Your Houseホスト 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.
1:47:38EN
Cognitive Revolution "How AI Changes Everything"· 2026年7月4日ポッドキャスト19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chipホスト 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.
1:32:38ENCognitive Revolution "How AI Changes Everything"· 2025年12月6日ポッドキャストPolish Is 1% of Frontier Training Data, So Poland Built Its Own Modelホスト 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.
2:24:18ENCognitive Revolution "How AI Changes Everything"· 2024年10月30日ポッドキャストMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.ホスト 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.
1:16:11ENCognitive Revolution "How AI Changes Everything"· 2024年7月11日ポッドキャストScooping Food Into Boxes Is 70 Percent of Plant Laborホスト Nathan LabenzChef Robotics CEO Rajat Bhageria on 20 million plated meals, a foundation model for food, and why the kitchen may leave the house.
1:18:48EN
Lex FridmanポッドキャストData Movement, Not Math: How to Make Deep Learning Fit on Robots and Phonesホスト 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.