筛选结果
38 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.
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Lab主持 Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models Banned主持 Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.
2:16:29EN → 简体中文从每月两篇到每月六十篇:两周中国行看到的 AI 安全主持 Nathan LabenzNathan Labenz 认为,那句耳熟能详的“中国绝不会慢下来”,一接触到中国真实的 AI 安全生态就站不住了。
1:47:38EN19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chip主持 Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.
1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.主持 Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-Ready主持 Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.
4:25:13EN → 简体中文架构几乎没变,变的是数据、成本和开放权重主持 Lex FridmanNathan Lambert 和 Sebastian Raschka 带 Lex Fridman 回顾了这一年:训练方法和数据集的变化,远大于模型设计的变化。展望 2026 年,争论的焦点似乎是推理成本、智能体,以及真正开放的模型该归谁。
1:54:30ENFrom One in Ten Cancer Cells to One in a Million主持 Nathan LabenzNathan Labenz's solo AMA updates his son's lymphoma treatment, calls Claude Opus 4.5 software AGI, and ranks the live players of early 2026.
1:18:40ENWhen AI Becomes the Oil Well: Luke Drago on the Intelligence Curse主持 Nathan LabenzThe Workshop Labs co-founder tells the FLI Podcast why a world of non-human production could recreate the resource curse — and how private data, open weights, and moonshot careers might still break it.
1:32:38ENPolish Is 1% of Frontier Training Data, So Poland Built Its Own Model主持 Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.
1:09:48ENChina's AI Labs Went From 15 to Six. The Compute Gap Is 15-to-1.主持 Jacob EffronJordan Schneider tells Unsupervised Learning that Chinese models were a private-capital story, that Beijing still thinks in hardware, and that the West's real edge is chips it can serve — not a state master plan.
1:25:54ENHours after training ends, Z.ai puts the weights on the internet主持 Nathan LabenzZixuan Li of Z.ai tells ChinaTalk how a Chinese lab ships GLM, treats open weights as a ticket into Western ecosystems, and still sees itself as an upstart.
2:14:55ENFrom Character AI to a 1% Warning Shot: Gabriel Weil's Case for Liability as AI Governance主持 Nathan LabenzA Touro law professor tells Nathan Labenz how negligence, strict liability, and punitive damages could make frontier labs treat public risk as a hit to their own balance sheet.
54:53ENA Free Voting Board Burned $2 Million. Then It Raised $100 Million.主持 Jacob EffronDatabricks co-founder Ion Stoica explains why LMArena exists, why China is winning open-source models, and why reliability—not another scaling law—is still the thing holding AI back.
1:30:57ENAnt's $320 Billion IPO Was Stopped at the Eleventh Hour: Angela Zhang on China's Tech Tightrope主持 Nathan LabenzA USC law professor walks through the dynamic pyramid that governs Chinese tech, from Didi's listing to DeepSeek's price war, and why she thinks the United States is starting to look more like China.
45:06ENMike Schroepfer: AI’s Bottleneck Is Electricity, and One Tanker of Fusion Fuel Could Power the U.S. for a Year主持 Jacob EffronMeta’s former CTO tells Redpoint why hyperscaler demand is the climate story, why Llama was an access play, and why tomorrow’s CTO still lives or dies on a priority queue.
1:08:51ENChip Controls Are Meant to Buy Ten Years, Not Win the AI Race主持 Nathan LabenzEconomist Noah Smith tells Nathan Labenz that U.S. export controls are a piece of conventional deterrence against China — not a plan to kneecap DeepSeek.
2:33:39EN → 简体中文攻击者后出手:Nicholas Carlini 为什么总能攻破 AI 防御主持 Nathan Labenz谷歌 DeepMind 安全研究员谈简单的损失函数、70% 的稳健准确率,以及为什么修复必须落在系统层面,单靠模型本身不够。
5:06:18ENDeepSeek's $5 million training claim and the five-year bet on compute主持 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.
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.
1:24:57ENA 50,000-GPU cap and a 1,700-chip loophole主持 Jacob EffronSemiAnalysis founder Dylan Patel walks through the AI diffusion rule, China’s compute squeeze, and how xAI stood up a 100,000-GPU cluster in a closed Memphis appliance factory.
3:53:49ENWith One-Twelfth the Data, an Open-Source Vision Model Beat GPT-4o主持 Nathan LabenzWill Hardman of Veratai walks Nathan Labenz through two years of vision-language models, from ViT and CLIP to InternVL, BLINK, and Transfusion.
1:57:00ENLess Than 1%: Why OpenAI's roon Thinks the Good Guys Get AGI First主持 Nathan LabenzDoom Debates host Liron Shapira presses OpenAI technical staffer roon on extinction odds, alignment-by-default, and a plan that even he admits is balancing on razor wire.
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