筛选结果
14 篇排序
1:09:44EN
No Priors: AI, Machine Learning, Tech, & Startups· 2026年10月9日播客 · 2 人对谈10,000 GB300s for Four Weeks: Reflection’s Open-Model Ledger主持 Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.
49:12EN
Sequoia Capital· 2026年7月29日播客The People Writing the Transformer’s Eulogy Want a Lab That Can Take Vacation Without Themo1’s reasoning lead and a Gemini pre-training lead argue the architecture, not more scale, is now the bottleneck — and that replacing it starts with automating kernel generation.
58:46EN
Unsupervised Learning: With Jacob Effron· 2026年4月9日播客Two Dates from OpenAI's Chief Scientist: Intern by September, Automated Researcher by 2028主持 Jacob EffronJakub Pachocki tells Jacob Effron why those timelines still hold, why continual learning is not a detour, and why showing chain of thought in the product would train the monitor away.
1:01:38ENSequoia Capital· 2026年1月6日播客13-Hour Coffee and 2x Throughput: Physical Intelligence Crosses the Deployment LineKarol Hausman and Tobi Springenberg tell Sequoia’s Training Data how a general robot brain, plus real-world RL, turned coffee, laundry, and chocolate boxes into jobs that actually run.
48:51ENSequoia Capital· 2025年6月17日播客The Breakthroughs for AGI Are Already Done, Says OpenAI’s Former Research HeadBob McGrew tells Sequoia’s Training Data that 2025 is the year of reasoning, agents will be priced at the cost of compute, and nothing fundamental will be added to the stack by 2035.
58:11ENSequoia Capital· 2025年4月15日播客Patrick Hsu Wants an App Store for Biology, Not Just Better DrugsThe Arc Institute co-founder argues that Evo 2, trained on genomes rather than proteins, should underpin tools for every step of the scientific method.
5:06:18EN → 简体中文
Lex Fridman· 2025年2月2日播客DeepSeek 宣称训练成本仅 500 万美元，背后是一场关于算力的五年赌注主持 Lex FridmanDylan Patel 与 Nathan Lambert 带着 Lex Fridman 梳理了 DeepSeek-V3 与 R1、出口管制、台积电、超大集群，以及在 AI 竞赛中究竟谁能赚到钱。
1:04:54ENUnsupervised Learning: With Jacob Effron· 2024年11月12日播客He bet company equity that Transformer would last — then drew an AI map for 12,000 companies主持 Jacob EffronDatabricks chief AI scientist Jonathan Frankle tells enterprises when to prompt, fine-tune, or pre-train, and why he still refuses to forecast the next architecture.
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.
46:09ENNo Priors: AI, Machine Learning, Tech, & Startups· 2024年8月1日播客 · 2 人对谈Oriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are Numbered主持 Sarah Guo, Elad GilGoogle DeepMind’s VP of research on Gemini, long context, the coming shift from pretraining to RL, and why AGI may never have a finish line.
1:01:39ENCognitive Revolution "How AI Changes Everything"· 2024年4月13日播客GPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?主持 Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.
1:33:56ENCognitive Revolution "How AI Changes Everything"· 2023年7月6日播客The AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understand主持 Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.
59:28ENCognitive Revolution "How AI Changes Everything"· 2023年6月16日播客From $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Product主持 Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.
2:23:56ENLex Fridman· 2023年3月25日播客 · 2 人对谈GPT-4 is a very early AI. ChatGPT is the line in the history books嘉宾 Sam Altman · 主持 Lex FridmanSix days after GPT-4 shipped, Sam Altman tells Lex Fridman why usability beat raw scale, why alignment and capability are not opposite vectors, and why OpenAI is trying to deploy while the stakes are still low.