Core Insights - Lovart, also known as "星流AI" in China, has rapidly gained attention in the AI application field, with significant engagement on social media and a surge of users seeking trial invitations [1][3] - The emergence of Lovart signifies a shift from traditional AI tools to a new model of creative collaboration, redefining the relationship between creators and AI [3][19] Group 1: Old World Challenges - The previous generation of AI tools, referred to as AIGC 1.0, only addressed the initial stages of the creative process, leaving creators to handle the majority of integration and editing tasks manually [6] - The introduction of workflow tools like ComfyUI marked the AIGC 2.0 era, but their complexity deterred most designers, making them more suitable for AI experts rather than general creators [6][7] Group 2: New Model Introduction - Lovart's founder, Chen Mian, identified that creators need a comprehensive solution rather than just advanced tools, likening the new model to a "chef team" that handles all aspects of creative work [7][8] - The core idea of Lovart is to transform AI from a mere tool into a "Creator Team," allowing users to act as clients who provide input while AI manages the execution [8][19] Group 3: Interaction Redefined - Lovart's product design emphasizes a natural interaction model, using a metaphor of a "table" where creators can easily communicate their needs and see the results in real-time [9][11] - The interface consists of a large canvas for visual work and a dialogue box for user instructions, streamlining the creative process and enhancing user experience [10][11] Group 4: Market Positioning - Lovart strategically targets the overlooked "creative individual" and professional consumer segments, avoiding direct competition with industry giants like Adobe and Midjourney [14] - The company focuses on creating unique user experiences by integrating domain knowledge with AI capabilities, rather than simply improving existing tools [14][15] Group 5: Future Outlook - Lovart is positioned at the forefront of the emerging Agent era, which is expected to revolutionize the creative industry by enhancing collaboration and efficiency [15][19] - The founder believes that the true potential of AI lies in its ability to replace not just individual tools but entire collaborative teams, fundamentally changing the creative landscape [19][21]

Core Viewpoint - The advertising industry is facing significant challenges, with WPP's unexpected profit warning indicating a potential downturn, leading to a decline in shares across major ad groups and raising concerns about the impact of AI on traditional agency business models [1][2][10]. Company Summary - WPP has reported a combination of client losses, a slowdown in new business pitches, and cautious marketing strategies due to economic uncertainty, forecasting a revenue decline of 3% to 5% for 2025 [2][4]. - The outgoing CEO of WPP highlighted that new business pitches in 2025 are at one-third of the level compared to the same period last year, reflecting decreased marketer confidence [4]. - WPP has lost key clients, including Pfizer and Coca-Cola's North America account, and has undergone restructuring efforts to enhance competitiveness, which have caused distractions within the business [16][18]. - WPP plans to invest £300 million (approximately $407 million) annually in AI and related technologies, including an investment in Stability AI and the development of an AI-powered platform called WPP Open [14][15]. Industry Summary - The advertising sector is grappling with the rise of AI, which presents both opportunities and threats, as it may streamline services traditionally offered by agencies and challenge their business models [3][5]. - Analysts have noted a sharp decline in new business pitches, suggesting that corporate clients may be replacing some agency services with in-house AI solutions [5][9]. - Major agency groups like Publicis and Omnicom are committing to invest hundreds of millions in AI to adapt their operations [11]. - The competitive landscape is shifting, with Publicis performing well and maintaining its rating despite downgrades for WPP, IPG, and Omnicom due to immediate risks posed by AI [17][18].

作者 | 瑶大 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1925984408785127117 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 paper：https://arxiv.org/pdf/2501.15564 自动驾驶算法两大模块：场景理解、决策。 场景理解：理解周围的环境、预测agents的行为； 做决策：生成安全舒适的轨迹、可定制化多样化（可保守可激进）的驾驶行为。 diffusion planner这篇工作关注 planner 做决策部分，关注闭环场景的性能。 对于自动驾驶规划这一部分有几种方式： rule-based ：如PDM（https://arxiv.org/pdf/2306.07962），选择道路中心线，基于周车的行为预测，利用 IDM得到几种候选轨迹，利用nuPlan的评分标准查看哪条轨迹是最好的。 rule-based的迁移性不好，在某个环境、系统下调好的规则不一定适用于其他场景。大 ...

Group 1 - The rapid development of artificial intelligence is reshaping the global economic landscape, creating both opportunities and challenges for businesses [2][7] - The concept of "pulsation speed" is introduced as a key to understanding current business dynamics, emphasizing the need for flexibility and foresight over scale in fast-paced industries [4][5] - The book highlights the transition of supply chain design from a cost center to a strategic asset, showcasing examples from companies like Dell and Chrysler [5][6] Group 2 - The notion that all competitive advantages are temporary challenges traditional strategic theories, as illustrated by Kodak's failure to adapt to digital trends despite having the necessary technology [3][8] - The emergence of AI technologies has accelerated the pace of change, leading to a state of "hyper-competition" where competitive advantages can diminish within days [8][9] - The book provides actionable frameworks for businesses to navigate the evolving landscape, emphasizing the importance of adapting to change rather than relying on static barriers [9][10]

不圆 发自 凹非寺 量子位 | 公众号 QbitAI AI的"创造力"居然是一种技术缺陷？？ 两位 物理学家 以 生物系统自我组装的过程 为参考，提出并验证了一个大胆的假设—— 扩散模型的去噪过程就像细胞的分化重组，图像生成AI无法精确"复制"的原因也可能和它 的"基因"（架构）有关。 在一篇已被ICML 2025接收的论文中，这两位研究者通过建立有扩散模型特性的数学模型证 明： AI的"创造力"本质上是一种确定性过程——是模型架构直接且必然产生的结果。 他们的假设从何而来？他们又做了什么来证明这个假设？ 让我们一起来看。 事情的起因：算法的独特创造力 人工智能系统在进化的过程中越来越模仿人类的思维能力，并展现出了一种独特又怪诞的"创 造力"天赋。 （所谓AI味？） 以扩散模型为例，作为DALL·E、Imagen和Stable Diffusion等图像生成工具的核心，其设 计初衷是精确拟合训练数据的分布，生成与训练图像 完全一致 的副本。 然而在实践中，它们似乎在 即兴创作 ，将图像中的元素融合以创造出新的东西——不是无 意义的彩色团块，而是具有语义意义的连贯图像。 是什么赋予了它们即兴发挥的能力？ 巴黎高等 ...

以下文章来源于硅星人Pro ，作者周一笑 硅星人Pro . 硅（Si）是创造未来的基础，欢迎来到这个星球。 本文来自微信公众号： 硅星人Pro （ID：gh_c0bb185caa8d） ，作者：周一笑，题图来自：AI生成 最近，你可能刷到过一些奇趣的猫咪视频。 主角通常是一只很胖的橘猫，像人一样在送外卖，或者刚看完电影就冲进健身房假装减肥。这些有点 好笑、有点可爱的"大橘剧场"，配上魔性的"喵喵"音乐，正在抖音、小红书和TikTok上到处传播。 如果说"大橘剧场"还在模仿人类的喜怒哀乐，那另一类刷屏的视频，则直接挑战起了物理定律。比如 那只在奥运会赛场上，从10米跳台完成一套专业动作的三花猫。它的姿势、翻转、入水，看起来都 和真的一样。这让一些网友第一次看到时，都怀疑是不是自己眼花了。 这些视频就是现在最火的AI猫咪内容。它们大概有两种路数。一种就像"大橘剧场"，给猫加上拟人 化的剧情，核心是讲个小故事。有的甚至发展成了有连续剧情的"宠物短剧"。比如一个 叫"Chubby"的AI胖橘猫，在各种视频里被创作者安排了"进监狱"、"和孩子分离"的悲惨故事，赚足 了全球网友的眼泪。 另一种就直接是技术展示，告诉你现在 ...

Core Insights - A new method for generating satellite imagery using geographic climate prompts and Stable Diffusion 3 (SD3) has been proposed by teams from the Technical University of Munich and ETH Zurich, resulting in the creation of the largest and most comprehensive remote sensing dataset, EcoMapper [1][2][4]. Dataset Overview - EcoMapper consists of over 2.9 million RGB satellite images collected from 104,424 global locations, covering 15 land cover types and corresponding climate records [2][5]. - The dataset includes a training set with 98,930 geographic points, each observed over a 24-month period, and a test set with 5,494 geographic points observed over 96 months [5][6]. Methodology - The research developed a text-image generation model based on fine-tuned SD3, which utilizes climate and land cover details to generate realistic synthetic images [4][8]. - A multi-condition model framework using ControlNet was also developed to map climate data or generate time series, simulating landscape evolution [4][12]. Model Performance - The study evaluated the performance of SD3 and DiffusionSat models in generating climate-aware satellite images, with metrics indicating significant improvements over baseline models [14][19]. - The SD3-FT-HR model achieved the lowest Fréchet Inception Distance (FID) score of 49.48, indicating high realism in generated images [15][16]. Climate Sensitivity Analysis - The generated vegetation density was found to be significantly correlated with climate changes, with performance varying under extreme weather conditions [16][18]. Applications and Future Directions - EcoMapper provides a framework for simulating satellite images based on climate variables, offering new opportunities for visualizing climate change impacts and enhancing integration of satellite and climate data for downstream models [22][26].

本文来自微信公众号：硅星人Pro （ID：gh_c0bb185caa8d），作者：周一笑，题图来自：AI生成 最近，你可能刷到过一些奇趣的猫咪视频。 主角通常是一只很胖的橘猫，像人一样在送外卖，或者刚看完电影就冲进健身房假装减肥。这些有点好 笑、有点可爱的"大橘剧场"，配上魔性的"喵喵"音乐，正在抖音、小红书和TikTok上到处传播。 这些视频就是现在最火的AI猫咪内容。它们大概有两种路数。一种就像"大橘剧场"，给猫加上拟人化的剧 情，核心是讲个小故事。有的甚至发展成了有连续剧情的"宠物短剧"。比如一个叫"Chubby"的AI胖橘猫， 在各种视频里被创作者安排了"进监狱"、"和孩子分离"的悲惨故事，赚足了全球网友的眼泪。 另一种就直接是技术展示，告诉你现在的AI到底有多厉害。那只跳水的猫就是最好的例子。一个叫"Pablo Prompt"的海外用户做了视频，发出来后，他自己都说"疯了"，因为Instagram上的播放量冲着2亿去了。 如果说"大橘剧场"还在模仿人类的喜怒哀乐，那另一类刷屏的视频，则直接挑战起了物理定律。比如那只 在奥运会赛场上，从10米跳台完成一套专业动作的三花猫。它的姿势、翻转、入水，看起来都 ...

本文第一作者为美国东北大学博士生沈轩，研究方向为高效人工智能，致力于在 GPU、移动端、FPGA 和 ASIC 等多种硬件平台上实现大模型的高效部署与加 速。第二作者为香港中文大学的韩晨夏，研究方向聚焦于计算机体系结构与 AI 系统的高效化设计。 在高质量视频生成任务中，扩散模型（Diffusion Models）已经成为主流。然而，随着视频长度和分辨率的提升，Diffusion Transformer（DiT）模型中的注意力机制 计算量急剧增加，成为推理效率的最大瓶颈。这是因为在视频生成中，DiT 通常使用 3D 全局注意力来建模时空一致性， 虽然效果出色，但计算量会随着 token 数 量呈平方增长 ，带来了巨大的计算负担。在 HunyuanVideo 等视频生成模型中，注意力模块计算时间占比超过 80%，生成仅 8 秒的 720p 视频甚至需要接近一小时 的时间。因此，提升视频生成模型的生成速度成为了迫切的需求。 现有视频生成加速方法，如 Sparse VideoGen（https://arxiv.org/abs/2502.01776）和 AdaSpa（https://arxiv.org/abs/250 ...

本文 由 中国人民大学高瓴人工智能学院李崇轩团队和 字节跳动 Seed团队 共同完成。 第一作者郑晨 宇 是中国人民大学高瓴人工智能学院 二年级 博士生， 主要研究方向为基础模型的优化、泛化和可扩 展性理论， 导师为李崇轩副教授，论文为 其 在 字节跳动 Seed 实习期间完成。 第二作者张新雨是字 节跳动研究员，主要研究方向为视觉生成模型。 李崇轩副教授为 唯一 通讯作者。 近年来， d iffusion Transformers已经成为了现代视觉生成模型的主干网络 。随着数据量和任务复杂 度的进一步增加， d iffusion Transformers 的规模也在快速增长。 然而在模型进一步 扩大 的过程 中，如何调 得较好的超 参 （如学习率） 已经成为了一个巨大的问题，阻碍了大规模 diffusion Transformers释放 其 全部的潜能。 为此，人大高瓴李崇轩团队和字节跳动 Seed团队的研究员引入了大语言模型训练中的 μP理论 ，并将 其扩展到 diffusion Transformers 的训练中。 μP通过调整网络不同模块的初始化和学习率，实现不 同大小diffusion Transf ...