X:Nathan Lambert
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Nathan Lambert@natolambertAI 评分4343
Nathan Lambert@natolambertAI 评分5050引用Reflection@reflection_aiIntroducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: http://reflection.ai/beam
Nathan Lambert@natolambertAI 评分5959引用Nathan Lambert@natolambertToday we're unveiling Trillium Labs @trillium_labs, a new non-profit to foster the open science of frontier AI. We're building open post-training recipes and will expand into open infra to study RSI, reward-hacking, multi-agent systems, and whatever comes next. We're built around the theory of change that you need more eyes to solve hard technical problems. We have faith in the scientific methods and communities that humanity has built, and worry that AI is becoming too closed to utilize them. Trilliums are wildflowers that bloom briefly in the spring, before the forest canopies fill out. Though they are small, they lay the foundation for the cycles of growth and nourishment through the rest of the year. At Trillium Labs, the recipes will be the slow nutrients for the seasons and the model releases will be the blooms. Building an institution dedicated to this is needed because, much as nature’s trilliums are slow to expand and grow, the open-ecosystem needs time and dedicated resources to catch up. I co-founded with with a long-time friend and collaborator Tom Zick (@thesezickbeats). We're hiring (full time + student collabs/interns), we're fundraising, and we're looking for compute. Please get in touch if you're interested in helping out. Offices based in the Bay Area and Cambridge MA, remote okay. I’m in the Bay Area until for The Curve and COLM to connect with people who are interested. We’re thankful to have initial support from Halcyon Futures and Schmidt Sciences with more funding en route to enable our ambitions of scaling. Our advisors @Thom_Wolf, @HannaHajishirzi, @gneubig and @ctnzr have been instrumental to building the ecosystem that exists today, and I’m stoked to get to keep working with them.
Nathan Lambert@natolambertAI 评分3232
Nathan Lambert@natolambertAI 评分2020
Nathan Lambert@natolambert精选AI 评分6767Nathan Lambert 发文表示很高兴看到 Google 用 Gemini 4 给大家带来惊喜。他认为更多实验室站上前沿对消费者(竞争)和世界(减少权力集中)都有益,并期待其在真实场景中的表现。
推荐理由:作者从竞争与权力分散角度解读 Gemini 4 的发布,提供了一个看多家前沿实验室竞争格局的视角。
Nathan Lambert@natolambertAI 评分5151
Nathan Lambert@natolambertAI 评分2020引用David Sacks@DavidSacksThe Bretton Woods of Super Intelligence
Nathan Lambert@natolambertAI 评分2222如果 X 允许使用 App 的人把他们的回复标记为人类撰写,或者你可以把回复限制为经过验证的人类,整个体验会好 10 倍。 我不想不得不把回复限制在我关注网络内的账号。所有机器人也都是认证的。
Nathan Lambert@natolambertAI 评分5454
@natolambert@natolambertAI 评分3737 引用@ns123abc@ns123abc🚨BREAKING: OpenAI just SCRAPPED the release of GPT-6.1 Astra 24 hours before DevDay "safety and deception concerns" it’s over https://t.co/nl9lRufSq2
@natolambert@natolambertAI 评分1212 @natolambert@natolambertAI 评分1515 @natolambert@natolambertAI 评分1919 @natolambert@natolambertAI 评分2121
@natolambert@natolambertAI 评分2020 对于那些认为蒸馏主要是 SFT 副产物、并且不清楚在 RL 定义的时代它如何变得越来越有影响力的人来说,这是一大胜利。https://t.co/PSvWtJ4Sj6
@natolambert@natolambertAI 评分55 https://t.co/9yxgo8Oie8 https://t.co/zj2u1zR6nh

@natolambert@natolambertAI 评分99 @natolambert@natolambertAI 评分1818 我不觉得我在 Interconnects 上什么都写对了,但在这样一个快速变化的时代,公开梳理自己的想法真的很有趣。这会迫使你不断改变一些观点,并培养出好的思考直觉。我们需要更多 AI 研究者这样做。
@natolambert@natolambertAI 评分2222 @natolambert@natolambertAI 评分2121 如果你今天想找一个不同的观点——它假设 AI 模型会加速 AI 研究的过程,但不会导致快速的 RSI 和近期风险的爆发:https://t.co/sLE4tuFi4t
@natolambert@natolambertAI 评分5858 Nathan Lambert 认为 Nvidia 以约 100 亿美元收购 Hugging Face 是一笔划算买卖,其影响 AI 讨论方向的能力每年就值这个数。
@natolambert@natolambertAI 评分1414 @natolambert@natolambertAI 评分1010 @natolambert@natolambertAI 评分1818
@natolambert@natolambertAI 评分1111 我收到了一些反对意见,说如果某人真心相信某件事,那就不算散布恐惧,这话有道理。那我觉得这些公司就是在以某种方式给人洗脑——从文化上驱使人们接受没有证据支持的信念。
@natolambert@natolambertAI 评分3131 引用@natolambert@natolambertWe have evidence that 1) AI agents are very good at improving software used to train AI 2) LLMs can be superhuman in many domains These do not combine to AI will be so powerful it will kill us in N years. We're forecasting inevitable saturation and unknown future breakthroughs.
@natolambert@natolambertAI 评分2727 @natolambert@natolambertAI 评分55 @natolambert@natolambertAI 评分2828

