Core Viewpoint - The article discusses the evolution and significance of high-precision positioning technology, particularly UWB (Ultra-Wideband) systems, which are transforming from mere tools into essential infrastructure for digital transformation and smart cities [2][3]. Group 1: Industry Background and Strategic Importance - High-precision positioning technology addresses the "last mile" challenge in complex indoor and outdoor environments, where traditional GPS fails, making it a critical need for industry and national security [2][4]. - The core value of high-precision positioning lies in its ability to provide accurate location data in complex environments, transitioning from area perception to point-level perception [2][4]. Group 2: Technical Evolution and Challenges - Traditional GPS technology has limitations in indoor environments, leading to a surge in demand for high-precision positioning solutions that can achieve centimeter-level accuracy [4][5]. - High-precision positioning must overcome challenges such as signal degradation, multipath effects, and dynamic environmental changes to maintain accuracy [5][6]. Group 3: Mainstream Technologies and Their Boundaries - UWB technology is highlighted as a leading solution, achieving positioning accuracy of 10-30 cm even in challenging environments, while facing high deployment costs and power consumption issues [16][17]. - Bluetooth high-precision positioning offers a cost-effective alternative but is limited by environmental factors affecting signal strength [18][19]. - 5G technology shows potential for high-precision positioning but is currently constrained by base station density and requires specialized terminals [20][22]. Group 4: Market Space and Application Scenarios - The global high-precision positioning market is expected to grow significantly, with projections indicating a market size reaching hundreds of billions by 2025, driven by applications in autonomous driving, smart logistics, and smart cities [46][47]. - In China, the market is anticipated to grow at a compound annual growth rate of 35%, reaching approximately 126.34 billion yuan by 2028 [46][47]. Group 5: Investment Opportunities and Trends - The industry is witnessing increased investment activity, with significant funding rounds for leading companies, indicating strong investor interest in high-precision positioning technologies [56][57]. - Investment focus is shifting towards companies with strong scene penetration and ecosystem integration capabilities, as well as those demonstrating scalable application potential [60][61]. Group 6: Future Outlook and Recommendations - The high-precision positioning industry is on the brink of widespread application, with a diverse range of technologies coexisting to meet various market demands [68]. - Companies that can effectively integrate technologies and demonstrate strong ecosystem capabilities are likely to gain competitive advantages in the evolving market landscape [68].

Core Insights - The article discusses the transformative potential of spatial intelligence in AI, emphasizing its ability to interact with the three-dimensional world through perception, navigation, operation, reasoning, and environment generation [4][6][8] - The integration of various algorithms and technologies, such as computer vision, deep learning, and multimodal learning, is crucial for the development of spatial intelligence [6][7] Group 1: Spatial Intelligence Development - Spatial intelligence is defined as the capability of AI to interact with the three-dimensional world, relying on multiple forms of algorithms and technologies [4][6] - The development of spatial intelligence involves challenges such as integrating diverse data types and executing complex tasks [2][4] - The company is focusing on creating a multimodal fusion spatial intelligence model that aligns with user scenarios, utilizing pre-trained large models and reinforcement learning techniques [6][7] Group 2: Technological Foundations - Key technologies for spatial intelligence include computer vision, deep learning, 3D representation learning, and visual-language models [6][7] - The company has extensive experience in various technical fields, which has been applied to multiple projects and solutions [6][7] - The ability to process and analyze diverse data types, including text, images, sounds, and environmental data, enhances the robustness and generalization of spatial intelligence models [7][8] Group 3: Future Plans and Market Strategy - The company aims to develop specialized AI agents for mobile terminals and smart environments, enhancing the value and competitiveness of Chinese products in overseas markets [7][8] - Short-term goals include creating AI agents with human-like thinking and long-term memory capabilities for wearable devices and robots [8] - Long-term objectives involve evolving from specialized AI agents to general intelligence agents, exploring advanced spatial intelligence and autonomous learning technologies [8]

Core Viewpoint - The article discusses the transformation of spatial intelligence through architectural innovation and multimodal integration, moving from laboratory research to industrial applications, emphasizing the need for advanced algorithms and technologies to handle complex spatial reasoning in the physical world [2][4][5]. Group 1: Spatial Intelligence Definition and Technologies - Spatial intelligence is defined as the ability of artificial intelligence to interact with the three-dimensional world through various forms such as perception, navigation, operation, reasoning, and environment generation, relying on technologies like computer vision, deep learning, 3D representation learning, and multimodal learning [4][5]. - The implementation of spatial intelligence depends on multiple algorithms and technologies, including computer vision for perception, 3D representation learning for understanding geometric and topological structures, and visual-language models for semantic understanding and spatial reasoning [4][5][7]. Group 2: Development and Application - The company is developing a multimodal spatial intelligence model in the AIoT field, integrating heterogeneous data from various edge devices to enhance spatial perception, environmental understanding, and causal reasoning capabilities [7][8]. - The deployment of AIoT edge devices enables the collection of vast, diverse, and fine-grained spatiotemporal data, addressing data insufficiency issues in spatial intelligence development [8]. Group 3: Future Plans and Market Strategy - The next development phase aims to meet the demands of the Middle East and overseas markets by creating specialized AI agents based on accumulated data and experience, enhancing the competitiveness of Chinese products and solutions abroad [9]. - Short-term goals include developing AI agents for mobile terminals, such as smart wearable devices and robots, to improve interaction capabilities and intelligence levels [9]. Long-term objectives focus on evolving from specialized to general AI agents, exploring advanced spatial intelligence and autonomous learning technologies [9].

Core Viewpoint - AUO's digital transformation services aim to empower various industries by leveraging its extensive experience in smart manufacturing and digitalization [1][2]. Group 1: Company Overview - AUO has established AUO Digital Technology Services (Suzhou) Co., Ltd. to provide integrated solutions combining AI with manufacturing elements [1]. - The company has served over 1,000 manufacturing enterprises across more than 10 countries, covering 34 industries including electronics, healthcare, and automotive [1]. Group 2: Digital Transformation Strategy - The concept of "minimum element digitalization" allows users to select digital components tailored to their needs, minimizing transformation costs [1][3]. - AUO aims to share its manufacturing expertise to help other companies achieve digital transformation, creating a reciprocal growth model [2][3]. Group 3: Future Factory Concept - AUO defines the future factory as one that integrates large-scale AI capabilities, evolving from advanced factories that focus on lean, automated, and digital processes [5][6]. - The future factory will feature three core elements: autonomous intelligence, embodied intelligence, and spatial intelligence, supported by knowledge, digital, and embedded models [6][7]. Group 4: Client Segmentation and Services - Clients are categorized based on revenue, with tailored services ranging from enterprise hosting for smaller firms to co-creation with top-tier global clients [5]. - The company emphasizes the importance of large-scale factories for maximizing efficiency and value through system reuse [6].

中国科技企业出海的典型代表之一。 本文为IPO早知道原创 作者｜ Stone Jin 微信公众号｜ipozaozhidao 据 IPO早知道消息， 特斯联与阿联酋穆罕默德 ·本·拉希德图书馆的合作 ，已成为 中国科技企业 出海的典型代表之一。 特斯联与穆罕默德 ·本·拉希德图书馆的合作始于2022年，旨在将智能机器人投入图书馆日常运营， 依托计算机视觉算法、多模态交互、云边端协同架构等技术，为图书馆提供覆盖空间全域场景的自动 化储藏、图书检索、图书递送、全天候巡控、火灾及烟雾探测等智能化服务。 UN s r 口1 RIVIA : 0 B 一方面， 依托自研打造的计算机视觉算法，特斯联智能机器人具有图书定位误差小、响应速度快等 特点，能够精准完成智能导引、检索、递送等工作 ；另一方面， 通过串联电梯、书架等 40余类设 备，特斯联智能机器人通过多模态交互系统为读者提供多样化服务。 伴随大语言模型能力的突破，作为其能力的延展与补充，以智能机器人为代表的智能体正在通过多模 态工具整合、复杂决策闭环、动态环境交互，弥补大模型在空间智能中的局限性，推动其从 "知识提 供者"向"自主行动者"的角色进化。在与穆罕默德·本· ...

2024年以来，基础模型能力的不断跃迁，使得作为第三代AI核心方向的"空间智能"加速涌现。 空间智能意指在动态三维世界中理解、推理、生成和行动的统一能力 ， 在特斯联的视角中，其能力 主要通过 "空间要素"、"空间模型"、"空间智能体" 三大核心要素实现，亦即：基于对物理空间内 空间要素的感知、理解和处理，以空间大模型作为核心计算与推理引擎，使空间智能体具备自主决 策、执行以及交互的智能能力，从而建立物理世界与数字世界的无缝链接和融合，进而实现万物互 联、跨越虚实、空间觉知的AI能力。 空间智能实现的三大核心要素：空间要素、空间大模型、空间智能体 近日，特斯联正式发布完整空间智能战略，构建以空间智能大模型（Space-Aware LM）为核心驱 动的空间智能体（Space-Aware Agent），以实现空间智能落地实践，升级在大模型时代的全新空 间智能体验。 空间智能体还可以通过自然语言和人交互，精准理解人的意图，提供空间信息数据和分析，同时执行 和操作物理设备，实现物理世界的反馈，实现从物理世界到数字世界再到物理世界的任务执行闭环。 随着数据积累和行为变化，空间智能体将进行周期性的强化学习，提高空间理解的 ...

图片来源： No Priors Z Highlights 李飞飞，著名的人工智能专家和斯坦福大学计算机科学与电子工程系的教授，因其在计算机视觉和深度学习领域的开创性工作而广受认可，被誉为 "AI 教 母 " ，后创办了 World Labs ，专注于空间智能领域。本次访谈由 No Priors 发布于 2025 年 6 月，她剖析了 World Labs 背后的人文和技术动机，还讨论了 3D 世界建模面临的挑战、她组建卓越团队的策略等等。 创业初心：点燃空间智能新火种 Sarah ： 听众们大家好，欢迎回到《 No Priors 》播客。今天的嘉宾是李飞飞，她是计算机视觉和深度学习领域的先驱。她创建了具有开创性的 ImageNet 数据集，帮助点燃了深度学习革命。李飞飞是斯坦福大学教授、 Stanford HAI （ Institute for Human-Centered AI ）的联合主任。她曾领导 Google Cloud AI ，为国际政策制定者提供建议，并最近共同创立了 World Labs 公司，致力于开发具备空间智能的 AI 。飞飞，非常感谢你今天加入我们。 李飞飞 ： 谢谢邀请，这将会很有趣。 ...

一、"液态玻璃"加持，"无边泳池"再升级：苹果设计语言的十年之变 新一届WWDC，苹果带来了自iOS 7之后最大的UI迭代。苹果的设计语言，正在经历从"拟物化"到"扁平 化"，再到如今"液态玻璃"（Liquid Glass）的演进。 毛玻璃材质并非全新概念，但苹果此次将其提升到了系统级的高度，统一应用于iPhone、iPad、Mac乃 至Vision Pro。可以想象，系统级别的图标都变成了一层层毛玻璃，这些毛玻璃之间存在三维空间关 系。 例如，输入密码的按键，每一个数字都成为一个圆形的毛玻璃，若背后是桌面壁纸的人脸，人脸会因光 线透过不均匀毛玻璃折射而产生夸张的变形。这种设计，既保留了扁平化的简洁，又通过半透明和光影 效果营造了物理世界的深度感。 苹果官方解释，这一改变是"考虑到设备的演进和算力的进步"。知名科技博主"汉阳"曾在2019年就指 出，毛玻璃设计对系统功耗要求较高，是"拉动内需"的体现。 如今，苹果的算力足以支撑更精致的毛玻璃效果，并将这种统一观感拓展到全系设备。这不仅提升了美 学，更重要的是在潜移默化地教育用户心智，为苹果一直强调的空间智能铺路。所有带屏幕的苹果产 品，其设计意象都是在信息的海 ...

1. 通用 Agent 之外，Agentic Age 流量赛还有哪些「隐藏副本」？ Agentic AI 的「流量入口」逻辑，与传统互联网时代有何根本不同？有哪些产品被视为当前最值得争夺的战略高地，而又是谁 在主导这些战略入口？在「流量入口即生态」的新范式下，各主力玩家如何划定阵地？有哪些路线分歧？ 2. 烧钱一年，李飞飞的「空间智能」愿景有变化吗？ 机器之心PRO · 会员通讯 Week 24 --- 本周为您解读 ② 个值得细品的 AI & Robotics 业内要事 --- ① AI 助手可以跨平台自主执行任务，绕过传统平台的注意力分发模式。过去的互联网时代，用户获取信息和服务 的入口主要集中在搜索引擎、社交平台、门户网站等传统节点。用户主动搜索或点击链接，即可获得所需内容。 World Labs 的愿景有变化吗？AI 技术如何「反直觉」发展？为什么没有空间智能的 AI 是不完整的？空间智能如何解锁从「单 一现实」到「多元宇宙」的未来？为什么李飞飞没有更早重视 3D 表征？ ... 本期完整版通讯含 2 项专题解读 + 31 项 AI & Robotics 赛道要事速递，其中技术方面 12 项，国内方面 ...

机器人大讲堂获悉，智能感知研发商「留形科技」完成数千万元 Pre-A轮融资。 本轮融资的投资方包括弘毅 投资等，此次融资资金将主要用于核心零部件定制生产、产品规模化交付及市场拓展。 目前，留形科技已与多家头部机器人厂商展开深入合作，计划于 2025年7月进行留形Odin1的量产，并将在 未来进一步拓展其产品在建筑测绘、工业巡检、机器人导航等领域的海内外市场，推动空间智能产品的全球产 业化布局。 据企查查显示，留形科技此前已获真格基金、俊盛投资的融资支持。 据公开资料显示，留形科技 （ 全称：深圳留形科技有限公司）成立于 2022年，是一家专注与智能三维感知 与重建技术的创新型科技企业。公司致力于推动相关技术在机器人导航与数字孪生、建筑测绘、工业巡检等领 域的深度融合应用。 在核心团队方面 ， 留形科技的创始人徐威是香港大学 MaRS Lab博士，在SLAM算法和机器人领域有深厚 的技术积累。 此外，技术战略顾问张富教授曾任职大疆创新顾问科学家，并 参与 了 Livox激光雷达的研究 工作。留形科技的核心团队成员大多来自香港大学、卡耐基梅隆大学、哈尔滨工业大学、北京航空航天大学等 顶尖高校，团队硕博占比高达60 ...