筛选结果
7 篇排序
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.
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.
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.