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14 件
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1:09:44EN
No Priors: AI, Machine Learning, Tech, & Startups· 2026年10月9日ポッドキャスト · 2 人10,000 GB300s for Four Weeks: Reflection’s Open-Model Ledgerホスト 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.オープンソースとコミュニティOpen-weight modelsReinforcement Learning+3
49:12EN
Sequoia Capital· 2026年7月29日ポッドキャスト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.モデルと学習AI researchPretraining & midtraining+3
58:46EN
Unsupervised Learning: With Jacob Effron· 2026年4月9日ポッドキャストTwo 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.モデルと学習Continual learningReinforcement Learning+3
1:01:38EN
Sequoia Capital· 2026年1月6日ポッドキャスト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.モデルと学習RoboticsReinforcement Learning+3
48:51EN
Sequoia Capital· 2025年6月17日ポッドキャスト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.モデルと学習Reasoning & test-time computePretraining & midtraining+3
58:11EN
Sequoia Capital· 2025年4月15日ポッドキャスト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.モデルと学習AI for sciencePretraining & midtraining+3
5:06:18EN
Lex Fridman· 2025年2月2日ポッドキャストDeepSeek'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.企業と戦略Open-weight modelsPretraining & midtraining+3
1:04:54EN
Unsupervised Learning: With Jacob Effron· 2024年11月12日ポッドキャストHe 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.企業と戦略Transformers & attentionPretraining & midtraining+3
2:24:18EN
Cognitive 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.モデルと学習Local inference評価(Evals)+3
46:09EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年8月1日ポッドキャスト · 2 人Oriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are Numberedホスト 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.モデルと学習AGI TimelinesPretraining & midtraining+3
1:01:39EN
Cognitive Revolution "How AI Changes Everything"· 2024年4月13日ポッドキャストGPT-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.モデルと学習Scaling LawsSynthetic data+3
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 2023年7月6日ポッドキャストThe 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.モデルと学習機械学習の基礎Scaling Laws+3
59:28EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月16日ポッドキャストFrom $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.モデルと学習ファインチューニングPretraining & midtraining+3
2:23:56EN
Lex Fridman· 2023年3月25日ポッドキャスト · 2 人GPT-4 is a very early AI. ChatGPT is the line in the history booksゲスト Sam Altman · ホスト 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.企業と戦略Post-training安全性とガバナンス+3