编辑部 发自 凹非寺 量子位 | 公众号 QbitAI AI热潮还在汹涌，但如果你还不知道如何参与……那为什么不来 量子位 呢？ 我们是一家以 追踪AI新进展 为核心的内容平台，经过8年积累，目前拥有顶流影响力，广泛且备受认可的产业资源，以及时代风口的最佳观 测和学习生态位。 目前，我们有 三大方向 岗位招聘，希望你是 （或者能成为） 这三个方向的内容专家： 岗位均为全职，工作地点：北京中关村。 岗位面向： 加入我们，你可以获得： 任职要求： 以下是岗位详情： 所有岗位不同能力层级职位均在开放，欢迎结合个人履历和经验申请。 AI产业方向 岗位职责： AI产业方向 ：关注基建层创新，包含芯片、AI Infra、云计算； AI财经方向 ：关注AI领域创投和财报，跟踪产业链资本动向； AI产品方向 ：关注AI在应用和硬件终端方向的进展。 社招：覆盖编辑、主笔、主编各个层级，按能力匹配岗位； 校招：应届毕业生，接受实习且可转正。 站在AI浪潮之巅 ：第一时间接触和了解AI领域最新技术和产品，构建完整的AI认知体系。 玩转AI新工具 ：将各种AI新技术、新工具应用于工作，提升工作效率和创造力。 打造个人影响力 ：通过撰 ...

Core Insights - The article emphasizes the importance of Scaling Laws in achieving Artificial General Intelligence (AGI) and highlights Google's success with its Gemini 3 model as a validation of this approach [5][19][21]. Group 1: Scaling Laws and AGI - Scaling Laws were initially discovered by DeepMind, not OpenAI, and have been pivotal in guiding research directions in AI [12][14][18]. - Google DeepMind believes that Scaling Laws are essential for the development of AGI, suggesting that significant data and computational resources are necessary for achieving human-like intelligence [23][24]. - The potential for Scaling Laws to remain relevant for the next 500 years is debated, with some experts expressing skepticism about its long-term viability [10][11]. Group 2: Future AI Developments - In the next 12 months, AI is expected to advance significantly, particularly in areas such as complete multimodal integration, which allows seamless processing of various data types [27][28][30]. - Breakthroughs in visual intelligence are anticipated, exemplified by Google's Nano Banana Pro, which demonstrates advanced visual understanding [31][32]. - The proliferation of world models is a key focus, with notable projects like Genie 3 enabling interactive video generation [35][36]. - Improvements in the reliability of agent systems are expected, with agents becoming more capable of completing assigned tasks [38][39]. Group 3: Gemini 3 and Its Capabilities - Gemini 3 aims to be a universal assistant, showcasing personalized depth in responses and the ability to generate commercial-grade games quickly [41][44][45]. - The architecture of Gemini 3 allows it to understand high-level instructions and produce detailed outputs, indicating a significant leap in intelligence and practicality [46]. - The frequency of Gemini's use is projected to become as common as smartphone usage, integrating seamlessly into daily life [47].

英伟达小模型持续获胜。 ARC-AGI 2最新成绩，4B小模型 NVARC 以 27.64% 的公开榜成绩力 压GPT-5 Pro 18.3%登顶榜首。 且每任务成本仅20美分，大约是GPT-5 Pro单任务成本（超过7美元）的 1/36。 闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 据官方分析，此次NVARC夺冠的亮点在于 零预训练深度学习方法 ，没有依赖大规模通用数据集进行前期预训练， 规避了预训练模型的领域 偏见、数据依赖等问题。 而ARC-AGI 2确实是一个消除了与公共训练数据重叠的更高难度测试， 主要是看测试模型能否高效地获取超出其训练数据的新技能。 快来看看"性价比之王"是如何"练"成的？ 不靠参数堆料 英伟达的策略是将复杂推理移至离线的合成数据管道， 训练能在评估时快速运行的较小模型。 简单来说就是 大规模合成高质量数据 ，然后对现有模型进行优化， 并且 将昂贵的计算工作转移到离线进行 。 为了确保数据质量，他们将复杂的推理管线拆分成不同的阶段，每个阶段都可以独立验证。 通过这种方式，他们建立了一个含320万+ 增强样本的合成数据集，其中每个样本最多有7对输入/输出。 | Sourc ...

Core Insights - The MEET2026 Intelligent Future Conference is a significant event in the AI sector, featuring prominent speakers from academia and industry, including Tsinghua University and major tech companies like Baidu and Google Cloud [1][21][39] - The conference will cover a wide range of topics related to AI, including large language models, embodied intelligence, and cloud computing applications [3][39] - The event aims to provide practical insights and discussions on the current state and future of AI technology, focusing on real-world applications rather than theoretical concepts [33][34] Highlights - Highlight 1: The conference will feature a GenAI dialogue and an Agent roundtable, addressing pressing questions about AI's impact on industries and the evolution of autonomous technologies [5][8][12] - Highlight 2: Nearly thirty influential guests from academia and industry will participate, discussing the latest advancements and challenges in AI, including insights from Tsinghua University and leading tech firms [17][21] - Highlight 3: The event will release two important documents: the "2025 AI Top Ten Trends Report" and the "2025 AI Annual List," summarizing key developments and influential figures in the AI landscape [35][39] Event Details - The MEET2026 conference is scheduled for December 10, 2025, at the Beijing Jinmao Hotel, focusing on how AI technologies can drive societal progress [37][39] - The agenda includes various sessions led by industry leaders, covering topics from AI's role in enhancing productivity to the future of AI agents [41][42]

鹭羽 发自 凹非寺 量子位 | 公众号 QbitAI 而且绝非个例……常春藤里有一个算一个，"残疾"比例都高到离谱。 蛤？ 38% 的斯坦福本科生都是"残疾人"？？？ 不过，你要是以为这是"身残志坚"的励志故事，那就大错特错，NoNoNo！ 他们的目的只有一个—— 考试时间能延长50%！ 斯坦福38%本科生注册"残疾" 根据数据显示，今年38%的 斯坦福大学 本科生注册为"有残疾"，其中24%的学生都在秋季学期享受学术或住房特殊照顾。 但事实上，在他们当中，绝大多数并非传统意义上的身体残疾，而是ADHD （注意力缺陷多动障碍） 、焦虑抑郁诸如此类的心理问题。 获得该"残疾"认证的方式也相当简单， 只需要一张基础的医生证明 。 过去可能需要对应的临床数据佐证，例如药物治疗、相关病史等，但随着2008年《美国残疾人法案》 （ADA） 的修订，法律上对残疾的定 义扩充到"阅读、学习、集中注意力、思考"等认知学习过程受影响的群体。 高等教育与残疾协会 （AHEAD） 也随之发布指南，要求高校采取更为宽松的审核制度——更重视学生的个人感受，而不是仅依赖医学诊 断。 与此同时，诊断标准也在逐步放宽，2013年美国精神病学 ...

梦晨 发自 凹非寺 量子位 | 公众号 QbitAI GPU编程变天了。 英伟达发布最新版 CUDA 13.1 ，官方直接定性： 这是自2006年诞生以来最大的进步 。 核心变化是推出全新的 CUDA Tile编程模型 ，让开发者可以 用Python写GPU内核 ，15行代码就能达到200行CUDA C++代码的性能。 消息一出，芯片界传奇人物 Jim Keller 立即发问： 英伟达是不是亲手终结了CUDA的"护城河"？如果英伟达也转向Tile模型，AI内核将更容易移植到其他硬件上。 Jim Keller参与设计过AMD Zen架构、苹果A系列芯片、特斯拉自动驾驶芯片的"硅仙人"，他的判断在行业里相当有分量。 那么问题来了：CUDA这次到底改了什么？为什么会被认为是"自毁长城"？ GPU编程范式从"线程"到"瓦片" 要理解这次更新的意义，得先回顾一下传统CUDA编程有多折磨人。 过去20年，CUDA一直采用 SIMT（单指令多线程）模型 ，开发者写代码时，需要手动管理线程索引、线程块、共享内存布局、线程同步， 每一个细节都要自己操心。 想要充分利用GPU性能，特别是用上Tensor Core这类专用模块，更 ...

Jay 发自 凹非寺 量子位 | 公众号 QbitAI 15年内被裁4次，在惨遭微软、Meta等大厂优化后，美国传奇牛马老李头，今年又被苹果单方面分手了。 丢掉工作的六个月来，他参与了无数面试，收件箱里得到的反馈几乎是清一色的： 「很抱歉，经过综合评估，您这次的面试没有通过。」 没办法，谁叫他今年已经57岁了？ 按说，老李在科技公司摸爬滚打十多年，资历够、经历多，怎么着都算是各大厂争抢的香饽饽。 可现实偏不按套路走——被裁之后，他并没有像旁人想象的那样无缝衔接，反而在足足半年时间内都深陷漫无天日的求职泥沼。 不过也是老李入行晚， 十四年前加入微软当产品经理的时候，他就已经43岁了 。 哪想到，「老来俏」的好日子才过三年，噩耗就从天而降—— 微软宣布启动 1.8万人规模的大裁员 ，老李也不幸成为KPI。 虽说靠着缓冲期，他又在内部找了个岗位续上了工作，但活儿实在太无聊。拖了几年，他还是决定挥手告别微软。 老李的第二站是 Meta 。 这一次，他在Meta担任产品营销经理，负责一款AR眼镜。 不过，被大厂裁出心理阴影的老李，似乎靠AI摸索出了一条能保住饭碗的道路…… 连续被裁四次，苹果只拿他当合同工 去年九月，本 ...

嘻疯 发自 凹非寺 量子位 | 公众号 QbitAI 豆包升级上新，火山引擎带着 图 像创作模型 Doubao-Seedream-4.5 来了。 新模型有三个主打点。 一是强化了 原 图保持能 力 ，最大化保持原图的人脸、光影与色调、画面细节，可以用来P图。 例如"只保留绿线中的人物，将其他角色都删掉"： 再复杂一些，将白天变为黑夜： 二是重点强化了 多图组合生成能力 。 在官方展示中，输入8张参考图，并指定画面布局后，让它生成图画故事书封面： 童话故事书封面：小女孩与小狐狸站在发光森林小屋前，月亮巨大而梦幻，星尘在他们周围飘浮；萤火虫的光点点亮草地；小白花细致 点缀；雾气营造柔和深度；古铜色童话边框华丽包围整个场景；色调是蓝紫与暖金对撞；角色面部特征保持原图一致；整体梦幻、温 柔、魔法感强烈，适合作为儿童绘本封面。 把图片中的英文转成手写体中文： Seedream-4.5 能 精准执 行复杂指令，将多种元素精准识别提取出来 ，并自然融合： 同样地，让多个角色"拍"一张大合照： 模型也能生成无违和感的群像画面： 反过来，根据一张参考图，一次性生成6张海报，比例分别改成1:1、2:3、4:3、16:9、1:2、 ...

Core Insights - The competition focus in AI large models is fundamentally shifting towards Reinforcement Learning (RL) as the next growth engine, with significant advancements in RL training methods [2][3][10] - The cost of running RL on trillion-parameter models has been prohibitively high, limiting access to only a few companies, but recent breakthroughs have drastically reduced these costs [4][5][11] - Mind Lab's innovative approach using LoRA for efficient RL training has achieved a 90% reduction in GPU consumption while maintaining performance, marking a paradigm shift in training methodologies [6][18][20] Group 1: Reinforcement Learning Advancements - The marginal returns of pre-training are declining, and the industry is actively seeking new growth engines, with RL emerging as a key focus [2][10] - RL is transitioning from a supplementary role to becoming the main battleground for the evolution of large models, essential for adapting trillion-parameter models to agent tasks [3][10][11] - Mind Lab's solution involves using LoRA for parameter-efficient adaptation, significantly reducing the computational load of RL training [13][18] Group 2: Cost and Efficiency - The cost of running LoRA RL on the Kimi K2 model is only about 10% of traditional full-parameter RL, enabling broader access to RL training [18] - Training stability has improved, with consistent increases in reward and task success rates during training, avoiding catastrophic failures [19] - The general capabilities of the models have been preserved while enhancing specific task performance through LoRA RL [20] Group 3: Technical Challenges and Solutions - The challenges of running RL on trillion-parameter models include imbalanced routing, communication overhead, and complex parallel layouts [21][24][25] - Mind Lab's mixed cooperative parallel engine design addresses these challenges by unifying various parallel processing methods, optimizing resource scheduling [26] - The introduction of truncated importance sampling ratios helps mitigate distribution mismatches during RL training, ensuring effective learning [30] Group 4: Memory Mechanisms and Real-World Applications - Mind Lab has developed a new memory mechanism called Memory Diffusion, which mimics human-like "intelligent forgetting" to enhance memory efficiency [42][45] - This approach allows the model to dynamically compress and retain meaningful experiences while discarding irrelevant information, achieving high accuracy in benchmarks [49] - The concept of Research-Product Co-Design emphasizes the importance of real-world feedback in training, leading to more effective RL environments [50][54] Group 5: Future Directions and Industry Impact - The transition from a pre-training era to an experiential intelligence era is underway, focusing on how intelligence grows in real-world contexts [59][62] - Mind Lab aims to enhance model learning efficiency and adaptability, positioning itself as a leader in the next generation of AI research [66] - The team's diverse expertise and commitment to open-source collaboration are expected to accelerate advancements in AI technologies [64][68]

Core Insights - The MEET2026 Intelligent Future Conference is a significant event in the AI sector, featuring prominent speakers from academia and industry, including Tsinghua University and major companies like Baidu and Google Cloud [1][21][39] - The conference will cover a wide range of topics related to AI, including large language models, embodied intelligence, and cloud computing applications [3][39] - The event aims to provide practical insights and discussions on the current state and future of AI technology, focusing on real-world applications rather than theoretical concepts [33][34] Highlights - Highlight 1: The conference will feature a GenAI dialogue and an Agent roundtable, addressing pressing questions about AI's impact on industries and the evolution of autonomous technologies [5][8][12] - Highlight 2: Nearly thirty influential guests from academia and industry will participate, discussing the latest advancements and challenges in AI, including insights from Tsinghua University and leading tech companies [17][21] - Highlight 3: The event will release two important documents: the "2025 AI Top Ten Trends Report" and the "2025 AI Annual List," summarizing key developments and influential figures in the AI landscape [35][39] Event Details - The MEET2026 conference is scheduled for December 10, 2025, at the Beijing Jinmao Hotel, with a focus on how AI technologies are transforming various sectors [37][39] - The agenda includes a series of talks and discussions from industry leaders, covering topics from AI's role in enhancing productivity to the future of AI agents [41][42]