絞り込み結果
7 件並び順
58:46ENTwo Dates from OpenAI's Chief Scientist: Intern by September, Automated Researcher by 2028ホスト 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.
5:06:18ENDeepSeek's $5 million training claim and the five-year bet on computeホスト Lex FridmanDylan Patel and Nathan Lambert walk Lex Fridman through DeepSeek-V3 and R1, export controls, TSMC, megaclusters, and who actually makes money in the AI race.
1:04:54ENHe bet company equity that Transformer would last — then drew an AI map for 12,000 companiesホスト 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.
2:24:18ENMMLU 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.
1:01:39ENGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?ホスト 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.
1:33:56ENThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understandホスト 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.
59:28ENFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Productホスト Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.









