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12:15ENDeepSeek-R1: Open-Weight Reasoning, Benchmark Results and a First TestMatthew Berman · Forward FutureMatthew Berman examines DeepSeek’s January 2025 release, compares reported benchmarks and prices, and tries a handful of reasoning prompts.
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.
1:27:33ENA 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.
39:22ENA 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.
45:22ENAlphaGo 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.
39:45ENHarmonic’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.
49:14ENHumanoid 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.
51:15ENAGI 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.
1:27:02ZH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。
50:33ENThe 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.
46:09ENOriol 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.
1:07:05ENAlphaGo 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.
52:45ENA $150 million model, $5 million six months later: Benchmark’s AI investment mathPresenta: Nathan LabenzEric Vishria and Quilter’s Sergiy Nesterenko explain why PCB layout is not a Copilot problem, why human board data would be the wrong training set, and which AI companies look like sugar highs.
14:33ENAstribot and Other Robot Demonstrations: Dexterity, Soccer and MobilityMatthew Berman · Forward FutureMatthew Berman reviews 2024 robot demonstrations, from Astribot's manipulation tasks to DeepMind soccer, Unitree flips and a legged monocycle.
56:56ENA Week of PCB Layout in an Hour: Quilter Trains Agents, Not ImitatorsPresenta: Nathan LabenzSpaceX alum Sergiy Nesterenko tells Will Summerlin how Quilter uses reinforcement learning to compile schematics into manufacturable boards, and why cloning human layouts would cap the system at human skill.
9:15ENFigure 01 and Other Robot Demos: What the 2024 Clips ShowMatthew Berman · Forward FutureMatthew Berman reviews a humanoid making coffee, inexpensive robot arms and other robotics demonstrations. The clips show specific tasks, not proof of artificial general intelligence.
1:43:47ENYou Have to Run Before You Can Walk: Marc Raibert on Making Metal Look AliveInvitado: Marc Raibert · Presenta: Lex FridmanThe Boston Dynamics founder tells Lex Fridman why aggressive hopping beat cautious walking, how 109 Atlas falls became a method, and why robots still need a second kind of intelligence.
1:58:43ENA Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.Presenta: Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.
21:00ENJim Fan's Foundation Agent Vision: Skills, Robot Bodies, and SimulationMatthew Berman, Jim Fan · Forward FutureMatthew Berman examines research on Minecraft agents, varied robot bodies, and simulation as steps toward more general embodied AI.
35:16ENWhat Was Known About Q* in 2023—and What Was SpeculationMatthew Berman, Sam Altman, Andrej Karpathy · Forward FutureMatthew Berman surveys rumors, reasoning papers and self-play analogies after OpenAI’s leadership crisis, with important limits on what was verified.
2:27:58ENIt took Atlas fourteen years to walk the way Playter wantedInvitado: Robert Playter · Presenta: Lex FridmanBoston Dynamics CEO Robert Playter traces thirty years from a MIT basement pogo stick to Atlas, Spot, and Stretch — and why the company still designs machines that are allowed to fall.
1:07:10ZHInstructGPT 论文精读:从人工反馈到语言模型对齐从监督微调、奖励模型到 PPO,梳理 InstructGPT 的训练流程、实验结果与局限。
2:29:21ENThe Poker Bot That Never Tried to Read a SoulInvitado: Noam Brown · Presenta: Lex FridmanNoam Brown tells Lex Fridman how a Nash-equilibrium bot crushed heads-up no-limit Hold'em, how Pluribus did it six-handed, and why Diplomacy required an AI that could earn human trust.
2:10:39ENDemis Hassabis: Solve Intelligence, Then Solve Everything ElseInvitado: Demis Hassabis · Presenta: Lex FridmanThe DeepMind CEO tells Lex Fridman why the Turing Test is the wrong exam, how AlphaFold rewrote biology, and why the quiet radio sky suggests we are probably alone.
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