Resultados
85 artículosOrdenar
1:02:54EN
No Priors: AI, Machine Learning, Tech, & Startups· 17 jul 2025Pódcast · 2 vocesEnterprise Coding Tools Flipped the 80/20. Reflection AI Built the Other HalfPresenta: Sarah Guo, Elad GilFormer Gemini reward-model lead Misha Laskin on Asimov, why comprehension beats generation, and why there is no such thing as generalization—only pulling the test distribution into train.
38:10EN
Cognitive Revolution "How AI Changes Everything"· 16 jul 2025PódcastPlus Five for Format, Plus Ten if It CompilesPresenta: Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.
57:31EN
Unsupervised Learning: With Jacob Effron· 22 may 2025PódcastThe New Ruler for Models Is How Long They Can WorkPresenta: Jacob EffronOn Claude 4's launch day, Anthropic researcher Sholto Douglas argued that the next phase of AI is less about harder puzzles and more about hours of unsupervised work.
38:10ENNo Priors: AI, Machine Learning, Tech, & Startups· 1 may 2025Pódcast · 2 voceso3 should wait exactly as long as the answer takesPresenta: 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.
30:46ENNo Priors: AI, Machine Learning, Tech, & Startups· 24 abr 2025Pódcast · 2 vocesDeep Research Turned Web Browsing into a Trainable ProblemPresenta: Sarah Guo, Elad GilOpenAI researcher Isa Fulford explains why a side project on read-only synthesis became an agent, when reinforcement fine-tuning is worth it, and why models will hack the search engine you gave them.
1:14:11ENCognitive Revolution "How AI Changes Everything"· 5 abr 2025PódcastThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGIPresenta: Nathan LabenzDeepMind's thinking-and-inference lead explains why RL-for-reasoning felt sudden, why Gemini shows raw chain of thought, and why a million-token window still is not memory.
1:56:38ENCognitive Revolution "How AI Changes Everything"· 8 mar 2025Pódcasto3 took ARC from 20% to 87%. Nathan Labenz sketches early superintelligencePresenta: Nathan LabenzOn Consistently Candid, the Cognitive Revolution host argues that math and coding will be superhuman by 2026, and that a reasoning core plus specialist “intuitive physics” may already be enough.
32:46EN
Sequoia Capital· 25 feb 2025PódcastDon't draw the flowchart: OpenAI's Deep Research team on training agents end to endIsa Fulford and Josh Tobin explain why Deep Research was trained with reinforcement learning on hard browsing tasks instead of a human-written graph of model calls.
44:34ENUnsupervised Learning: With Jacob Effron· 19 feb 2025PódcastThe Cheaper Intelligence Gets, the More of It We'll BuyPresenta: Jacob EffronAmazon AGI Lab head David Luan on what DeepSeek actually meant, why next-token prediction is not enough, and why agents are still stuck on reliability.
2:16:17ENCognitive Revolution "How AI Changes Everything"· 5 feb 2025PódcastA 10B Model Trained Across Three Continents — and an H200 for $1.49 an HourPresenta: Nathan LabenzPrime Intellect's founders lay out a four-step plan to turn spare global GPUs into collectively owned intelligence, and argue that reinforcement learning just made their job easier.
2:49:35ZH
Zhang Xiaojun Podcast· 4 feb 2025Pódcast · 2 voces潘嘉怡解读 R1、o1 与 Kimi K1.5：强化学习如何教模型学会推理Invitado: 潘嘉怡 · Presenta: 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。
52:30ENSequoia Capital· 28 ene 2025PódcastMove 37 looked like a bug. LLMs still need their AlphaZero momentIoannis Antonoglou, from AlphaGo to Reflection AI, on self-play, reliability, and why compute can still buy intelligence.
1:48:20ENCognitive Revolution "How AI Changes Everything"· 25 ene 2025PódcastFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the TablePresenta: Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.
30:29ENHow Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.
1:27:33ENCognitive Revolution "How AI Changes Everything"· 3 dic 2024PódcastA 70B Model Booked Dinner 20% of the Time. MultiOn Took It to 95% in a DayPresenta: Nathan LabenzMultiOn CEO Div Garg returns to The Cognitive Revolution to recap 18 months of agent hype, the AgentQ stack, and why 2025 might still be the year of vertical agents.
39:22ENNo Priors: AI, Machine Learning, Tech, & Startups· 14 nov 2024Pódcast · 2 vocesA Fields Medalist Stuck for Two Hours. AlphaProof Climbed for Three Days.Presenta: Sarah Guo, Elad GilThe DeepMind team behind AlphaProof tells Sarah Guo and Elad Gil how an AlphaZero-style agent, a Lean verifier, and test-time reinforcement learning reached IMO silver — and why the missing skill is still theory building.
45:22ENSequoia Capital· 2 oct 2024PódcastAlphaGo Thought 30 Seconds a Move. o1 Made “Think Longer” GeneralOpenAI researchers Noam Brown, Ilge Akkaya, and Hunter Lightman on teaching language models to reason, the generator-verifier gap, and a second scaling curve at inference time.
39:45ENSequoia Capital· 24 sept 2024PódcastHarmonic’s Bet: Teach AI Math Directly, and the Riemann Hypothesis Falls by 2029Robinhood’s Vlad Tenev and Harmonic co-founder Tudor Achim tell Sequoia why math is reasoning, why Lean is the verifier, and why they refuse to wait for mathematical skill to emerge from scale.
49:14ENSequoia Capital· 17 sept 2024PódcastHumanoid robots fell from $1.5 million to $30,000. The GPT-3 moment is still a verbNvidia’s Jim Fan tells Sequoia how three kinds of data, a humanoid bet, and a System-1 motor-control model could unlock general-purpose robots.
51:15ENSequoia Capital· 10 sept 2024PódcastAGI Has One Problem Left: Spend a Million Times More on a TokenMagic founder Eric Steinberger tells Sequoia’s Training Data why he trains his own models, why a software engineer is the MVP of AGI, and why 96% still is not a colleague.
1:27:02ZHZhang Xiaojun Podcast· 5 sept 2024Pódcast · 3 voces广密：Self-play RL 可能开启 AGI 的新 Scaling LawInvitado: 广密 · Presenta: 张小珺广密认为，纯靠语言预训练扩展模型的边际收益可能正在减弱，self-play 强化学习是更值得押注的新路径。它能否泛化到代码、视频、机器人和更广的领域，将决定下一阶段的 AGI 竞赛。
50:33ENSequoia Capital· 20 ago 2024PódcastThe 40% data-center save started with an email that ended in a question markPhaidra founder Jim Gao tells Sequoia how an AlphaGo-inspired bet at Google became reinforcement learning that runs billion-dollar plants.
46:09ENNo Priors: AI, Machine Learning, Tech, & Startups· 1 ago 2024Pódcast · 2 vocesOriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are NumberedPresenta: 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.
1:07:05ENSequoia Capital· 16 jul 2024PódcastAlphaGo Combined Learning and Search. Language Models Only Did the First Half.Reflection AI's Misha Laskin tells Sequoia why prompted coding agents stall in the teens, and why the missing piece is still a ground-truth reward.Cargar másMostrando 24 de 85