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69개정렬
2:51:18ENLindon Gao on Building Dyna Robotics and Finding a Path Through the Fog게스트 Lindon Gao · 진행자 张小珺Dyna Robotics CEO Lindon Gao explains why his company builds intelligence through real workflows, how hardware and research must evolve together, and how a founder can find clarity by letting go of the need to be right.
38:14ENDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom Silicon진행자 Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.
1:16:29ENGrant Sanderson and Alok Puranik: Positional Encodings Through Group TheoryGrant Sanderson, Alok Puranik · 3Blue1BrownStarting with assumptions about attention, they derive matrix exponentials and connect rotations, decay and Jordan blocks to RoPE and ALiBi.
2:59:03ENYou Kaichao on How vLLM Grew from Open-Source Project to AI Infrastructure Company게스트 You Kaichao · 진행자 张小珺Inferact co-founder You Kaichao traces vLLM’s path from Berkeley research to a company, then explains why model, inference, hardware and agent design increasingly have to move together.
3:46:12ENKay Ke on Physical Intelligence, Robot Learning, and the Future of Robotics게스트 Kay Ke · 진행자 张小珺Physical Intelligence researcher Kay Ke traces the field's research lineages and explains Pi's progression from π₀ to π*₀.₆. She also reflects on education, fiction, ambition, and what it means to build robots that work beyond a demo.
36:58ENSTaR's Author Wants Models With EQ, Not Just More IQ진행자 Sarah Guo, Elad GilEric Zelikman, the Stanford and xAI researcher behind STaR and Quiet-STaR, launches humans& on the bet that three years of IQ scaling left models emotionally unintelligent.
1:07:41ENEdwin Chen on Building Surge and the Data Behind AI Models게스트 Edwin Chen · 진행자 Harry StebbingsThe Surge founder discusses a bootstrapped business, training-data quality and the limits he sees in AI benchmarks and synthetic data.
47:05ENLogan Kilpatrick on AI Developers, Interfaces, and the Future of ModelsMatthew Berman, Logan Kilpatrick · Forward FutureIn a July 2025 conversation, Google’s Logan Kilpatrick discusses Windsurf, agent interfaces, coding skills, Veo, and the shifting boundary between models and application scaffolding.
38:10ENo3 should wait exactly as long as the answer takes진행자 Sarah Guo, Elad GilOpenAI researchers Eric Mitchell and Brandon McKinzie tell No Priors why o3's leap is a chain of tools during reasoning — and why you should send the same prompt a hundred times.
35:14ENRobots Have No Wikipedia. Chelsea Finn Is Starting With Three Buildings.진행자 Sarah Guo, Elad GilPhysical Intelligence’s cofounder tells No Priors why a generalist robot model has to be trained across embodiments, why open-sourcing hardware is a bet against the problem going unsolved, and why humanoids are a little overrated.
57:41ENA Hundred Million Cells, Four People, Three Days: Biology's ImageNet Moment진행자 Sarah Guo, Elad GilVevo Therapeutics and the Arc Institute open-sourced Tahoe-100M and a 230-million-cell atlas in the same week — and argued that virtual cells, not just protein folders, are the next language model of biology.
2:36:13ZH松琳解读 DeepSeek、Kimi 与 MiniMax 的注意力新论文게스트 Songlin Yang(松琳) · 진행자 张小珺从动态稀疏注意力到线性注意力混合架构:三条长上下文路线如何兼顾训练、推理与硬件效率
2:49:35ZH潘嘉怡解读 R1、o1 与 Kimi K1.5:强化学习如何教模型学会推理게스트 潘嘉怡 · 진행자 张小珺伯克利博士生潘嘉怡带读 DeepSeek、OpenAI 与 Kimi 的推理模型报告,拆解长思维链、GRPO、奖励设计与蒸馏。R1-Zero 的“顿悟时刻”展示了推理策略如何在强化学习中涌现。
1:27:13ZH李想:汽车将从交通工具进化为空间机器人게스트 李想 · 진행자 张小珺理想汽车 CEO 李想谈公司为何从造车走向人工智能:基座模型是 AI 时代的操作系统,汽车是进入物理世界的重要载体。对他而言,技术路线最终要落实为更好的用户体验、能力与长期价值。
5:15:00ENDario Amodei Has Seen This Scaling Movie Enough Times게스트 Dario Amodei · 진행자 Lex FridmanAnthropic’s CEO puts numbers on frontier training costs and AGI, then hands the mic to the people who shape Claude’s character and look inside its weights.
1:33:07ZH广密复盘全球大模型这一年:Scaling Law、AGI 豪赌与中美格局게스트 李广密 · 진행자 张小珺拾象创始人李广密从硅谷一线观察出发,回顾 OpenAI、Anthropic、Google 与中国模型公司的竞争。他认为,模型能力与成本共同决定新一轮应用爆发,而巨额投入押注的是智能能否持续扩展以及 AI 对经济和产业的长期重构。
46:09ENOriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are Numbered진행자 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.
34:09ENFive Independent Teams Found the Same 10-to-1 Mix That Beats Transformers진행자 Sarah Guo, Elad GilThe authors of S4 and Mamba argue that mostly state-space layers plus a dash of attention is the architecture that actually works—and they are shipping it first as real-time speech.
2:47:17ENIf You Want Human-Level AI, Abandon Generative Models게스트 Yann Lecun · 진행자 Lex FridmanYann LeCun tells Lex Fridman why autoregressive LLMs cannot plan, remember, or understand the physical world — and why open source is the only democratic answer.
40:58EN99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses First진행자 Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.
41:58ENReliability is the bottleneck. Autonomy is what would make AI feel alive.진행자 Sarah Guo, Elad GilIlya Sutskever talks with Sarah Guo and Elad Gil about AlexNet, OpenAI’s capped-profit bet, why smaller models mainly lose reliability, and the line he draws for digital life.
37:01ENDNA Is Code, but You Compile It with a Pipette진행자 Sarah Guo, Elad GilGinkgo Bioworks CEO Jason Kelly tells No Priors why cells are worth the magic, why biology has no AWS, and why a pandemic should look like cybersecurity.
35:24ENThe Author of Attention Is All You Need Now Compiles mRNA Like Bytecode진행자 Sarah Guo, Elad GilJakob Uszkoreit on why Transformers won, why models spend compute on the wrong things, and why drug discovery has to become generation rather than screening.
42:18ENTen Percent of Search Is Health — and the Doctor Is Googling Too진행자 Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.
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