筛选结果
36 篇排序
2:00:51ENResample Every Token and You Can Watch the Model Decide主持 Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
36:19ENThe Benchmark Grid Is Broken: Why Test-Time Compute Has Outrun How We Score Models主持 Sarah Guo, Elad GilOpenAI researcher Noam Brown argues that static leaderboards hide how long a model is allowed to think — and that safety evals, research taste, and the release cycle have not caught up.
1:34:29ENThree IMO Golds Missed the Same Problem. Harmonic Wants Math to Compile主持 Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.
1:16:21ENJensen Huang’s Year-End Case That Token Costs Can Fall a Billion-Fold Without Killing Jobs主持 Sarah Guo, Elad GilOn No Priors, NVIDIA’s CEO uses radiology, open source, and a $2 trillion R&D budget to argue that 2025’s extremes missed the stack.
18:59EN86 Out of 100: The Clips No Priors Could Not Leave on the Cutting-Room Floor主持 Sarah Guo, Elad GilSarah Guo and Elad Gil stitch nine 2025 conversations into one short tour of legal AI, spatial intelligence, jobs, deterrence, and a doctor who made it home for dinner.
1:39:05ENThe Summer GPT-5 Disappointed Does Not Mean AI Progress Has Stalled主持 Nathan LabenzNathan Labenz tells Erik Torenberg why the GPT-5 backlash mixed up product mishaps, naming chaos, and a real jump in capabilities.
36:58ENSTaR's Author Wants Models With EQ, Not Just More IQ主持 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.
30:11ENFrom 12 Percent on AIME to IMO Gold in Two MonthsAlex Wei, Sheryl Hsu, and Noam Brown tell Sequoia's Sonya Huang how a three-person OpenAI sprint used general-purpose reinforcement learning, not Lean, to reach gold-level on the 2025 International Mathematical Olympiad.
48:51ENThe 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.
38:10ENo3 should wait exactly as long as the answer takes主持 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.
1:14:11ENThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGI主持 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.
1:10:23ENA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still Untapped主持 Jacob EffronThe Transformer co-inventor and Gemini researcher Jack Rae walk through test-time compute, Ilya Sutskever's AGI critique, and a four-year-old who now lectures his teacher about lizards.
3:18:45EN5,000 Samples Topped ARC-AGI. Then Claude Faked Alignment to Keep Its Values主持 Nathan LabenzRedwood Research chief scientist Ryan Greenblatt walks through inference scaling, alignment faking, and what it would mean to strike real deals with AIs.
1:48:20ENFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the Table主持 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.
1:33:01ENRare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already Missed主持 Nathan LabenzDr. Catherine Brownstein of Boston Children's Hospital explains why cheap genomes made her job harder, why data sharing still beats any model, and where ChatGPT Pro actually gives her hours back.
2:04:45ENo3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-Toe主持 Nathan LabenzNathan Labenz walks through OpenAI's o3 scores, deliberative alignment, and why 2024's biggest surprise was beating PhDs while failing to book a calendar invite.
1:07:32ENo1 Is a New Generation — and the Day AGI Arrives Will Feel Boring主持 Jacob EffronOpenAI’s former chief research officer Bob McGrew on why o1 is a 100x leap, why agents stall on reliability, and why AGI may land as office tedium rather than a single dramatic moment.
48:18ENNoam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of Headroom主持 Jacob EffronA ChatGPT query costs about a penny. The problems society cares about most, he says, are worth a million dollars.
39:22ENA Fields Medalist Stuck for Two Hours. AlphaProof Climbed for Three Days.主持 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.
29:29ENThe Week AI Won Its First Nobels, Two Investors Listed Who Is Most at Risk主持 Sarah Guo, Elad GilSarah Guo and Elad Gil talk NotebookLM, intelligent NPCs, artificial wombs, OpenAI’s o1, and which businesses look AI-durable.
1:06:48ENo1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning Point主持 Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.
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.
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.
1:56:03ENHumans Near a Perfect Score. The Best AI Still Gets 39%主持 Nathan LabenzZapier co-founder Mike Knoop and François Chollet put $1 million on ARC-AGI, a 2019 test of sample-efficient intelligence that language models still cannot crack under a one-cent-per-puzzle budget.
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