Core Viewpoint - Co-packaged optics (CPO) technology is emerging as a promising solution to enhance bandwidth and energy efficiency in data centers, particularly for applications involving generative AI and large language models. However, manufacturing challenges remain, particularly in fiber-to-photonics integrated circuit (PIC) alignment, thermal management, and optical testing strategies [1][20]. Group 1: CPO Technology and Benefits - CPO enables network switches to route signals at speeds of terabits per second while significantly improving bandwidth and reducing power consumption required for AI model training [1][20]. - The technology achieves a bandwidth density of 1 Tbps/mm, optimizing rack space in increasingly crowded data centers [1][6]. - CPO can reduce power consumption associated with high-speed data transmission from approximately 15 pJ/bit to around 5 pJ/bit, with expectations to drop below 1 pJ/bit [6][7]. Group 2: Manufacturing Challenges - Key challenges in CPO manufacturing include achieving precise alignment between fiber and PIC, which is critical for effective optical signal coupling [8]. - The most common passive alignment method is the V-groove technique, which connects the fiber directly to the PIC to minimize loss [8][9]. - Efficient coupling between standard single-mode fibers and silicon waveguides is complicated due to significant differences in size and refractive index, leading to potential light loss [8][9]. Group 3: Thermal Management - CPO systems are sensitive to temperature fluctuations caused by high-power devices like GPUs and ASICs, which can affect the performance of photonic devices [11][12]. - A temperature change of just 1°C can lead to approximately 0.1nm wavelength shift in most photonic systems, necessitating careful thermal management strategies [11][12]. - Advanced thermal interface materials and monitoring circuits are deployed to maintain PIC temperature within predefined ranges [11][13]. Group 4: Reliability Design - Ensuring reliability in CPO systems is crucial, especially with multi-chip integration, requiring known good die (KGD) testing and optical testing solutions [14][16]. - High reliability designs incorporate redundancy, such as backup lasers, to maintain operation in case of component failure [15][16]. - Integrated monitoring and self-correcting features are being developed to detect performance degradation and facilitate quick recovery [15][16]. Group 5: Integration Techniques - Both 2.5D and 3D packaging methods are utilized in CPO, with 2.5D placing electronic ICs and PICs side by side on a silicon interposer [17][18]. - 3D integration allows for optimal manufacturing processes for each chip type, enhancing performance while increasing complexity and cost [18][19]. - The integration of optical features with traditional CMOS processes is becoming more compatible, facilitating advancements in CPO technology [17][18].

知识图谱 (KG) 是由现实世界实体（节点）及其相互关系（边）组成的结构化网络，以机器可读的形式 对知识进行编码。在人工智能领域，知识图谱是语义理解、推理和数据集成的强大工具。它们为人工智 能系统提供上下文，通过链接不同的数据源并揭示隐藏的关系，实现更易于解释、更准确的决策。 知识图谱的概念最初由谷歌 2012 年的知识图谱推广，实际上建立在语义网络和本体领域数十年的研究 基础之上，最早可追溯到 20 世纪 60 年代。如今，知识图谱已广泛应用于各行各业，从支持搜索引擎和 语音助手，到推动科学研究和企业分析的发展。未来的创新将致力于实现知识图谱构建的自动化，增强 推理能力，并将知识图谱与人工智能模型紧密结合，从而构建更值得信赖、更具情境感知能力和更智能 的系统。 定义和结构 知识图谱是一种将知识表示为一组实体（节点）及其之间关系（边）的网络。每个节点通常对应于由唯 一 ID 或 URI 标识的现实世界概念或对象（例如，人物、地点或物品）；每条边表示连接两个实体（例 如， Person worksFor Company ）的特定关系或谓词。属性 (Attribute) 可以注释节点和边以捕获其他详 细信息（例如 ...

Core Viewpoint - The Hong Kong Monetary Authority (HKMA) and the Hong Kong University of Science and Technology (HKUST) Business School have signed a memorandum of cooperation to enhance collaboration in cybersecurity research, addressing the needs of the Hong Kong financial industry [1][2] Group 1: Collaboration Details - The memorandum establishes a strategic cooperation framework focused on cybersecurity, aiming to promote relevant research and knowledge growth [1] - The collaboration will utilize advanced technologies such as large language models to explore innovative supervisory technology (Suptech) and regulatory technology (Regtech) solutions [1] - The goal is to enhance the HKMA's regulatory capabilities and strengthen the financial sector's cybersecurity resilience [1] Group 2: Objectives and Impact - The partnership aims to develop practical application solutions, increase industry awareness of emerging threats, and cultivate cybersecurity professionals to support the ongoing development of the financial industry [1] - HKMA and HKUST will actively engage with financial institutions to validate research outcomes and gain deeper insights into the evolving cybersecurity needs and challenges faced by the industry [1] - The collaboration is expected to contribute to the resilience of Hong Kong's financial ecosystem by addressing real-world cybersecurity challenges [2]

ters we the 来源：猎云网 第五届BEYOND国际科技创新博览会(BEYOND Expo2025)于5月21日至24日举行。 5月24日，在闭幕式上，阿里巴巴集团董事长蔡崇信现身现场，提到阿里巴巴对组织架构进行了一些调整。 蔡崇信称，阿里巴巴将专注于几大核心业务：一是电子商务；二是云计算；三是希望确保人工智能渗透到业务的各个方面，既面向客户，也面向内部。 此外，蔡崇信还发表了年轻人就业的观点。 他认为，年轻人应因为想获取更多技能和知识而工作，这才是工作的意义。 同时，他表示，当你将机器人技术与人工智能结合起来时，想到了非常令人兴奋的事情。比如，机器人可以为你煮咖啡，或者可以到你家清洁地板。 但他也认为，世界上大多数智能机器人不需要看起来像人类。 他举例，如果你想让一个机器人来清洁你的地毯，回家打扫你的厨房或客厅，你真的想要一个看起来像人类的东西吗？我会感到害怕。我只想要一个看起来 像吸尘器的东西能智能地在房间里完成清洁工作。 "当我们谈论机器人时，我们总是会想起小时候看过的电影。它们看起来都像人，但它们显然不是人。现在，我们是否正在努力向与人类完全一样的机器迈 进？我认为这实际上是一种技术。还有很多 ...

随着大型语言模型（LLM）的飞速发展，模型能力与效率的平衡成为了前沿研究的关键议题。 腾讯混 元团队最新推出的混元TurboS模型，是一款新颖的 超大型 Hybrid Transformer-Mamba架构MoE模型 。该模型通过Mamba架构在长序列处理上的卓越效率与Transformer架构在上下文理解上的固有优势的 有机协同，实现了性能与效率的精妙平衡。 混元TurboS引入了创新的自适应长短思维链机制，能够根据问题复杂度动态切换快速响应模式与深度 思考模式，从而优化计算资源分配。更重要的是，其模型激活参数达到了56B（总参数560B），是业 界首个大规模部署的Transformer-Mamba专家混合（MoE）模型。 架构创新以及参数量的保证，让模型效果进步明显，国际最权威的大模型评测榜单LMSYS Chatbot Arena最新排名显示: 混元Turbo S 取得了整体1356的高分，在所有239个参赛模型中位列全球前7名。 | Rank* | Rank | Model | Arena 4 | વેરૂર A | Votes | A Organizatio License | 4 | | --- | ...

Core Viewpoint - The article discusses the introduction of a new model called MeanFlow, which utilizes average velocity to achieve a one-step generation framework, significantly improving the state-of-the-art (SOTA) in image generation tasks [1][5][10]. Group 1: Model Development - The MeanFlow model is trained from scratch without any pre-training, distillation, or curriculum learning, achieving a Fréchet Inception Distance (FID) score of 3.43, which is a notable improvement over previous one-step diffusion/flow models [3][10][13]. - The model introduces the concept of average velocity to represent flow fields, contrasting with instantaneous velocity used in flow matching methods [5][9]. Group 2: Experimental Results - Experiments conducted on ImageNet at a resolution of 256×256 demonstrated that the MeanFlow model achieved a 50% to 70% relative advantage over previous state-of-the-art methods in terms of FID scores [13][19]. - The model's performance was evaluated through an ablation study, showing various configurations and their corresponding FID scores, with the best results achieved under specific parameter settings [15][19]. Group 3: Scalability and Comparison - The MeanFlow model exhibits good scalability in terms of model size, with different configurations yielding competitive FID scores compared to other generative models [16][19]. - A comparison of the MeanFlow model with other generative models indicates that it significantly narrows the gap between one-step diffusion/flow models and their multi-step predecessors [19][20]. Group 4: Research Team and Background - The research was conducted by a team from MIT and CMU, including notable contributors such as PhD student Geng Zhengyang and other students of He Kaiming [21][22][23]. - The team aims to bridge the gap between generative modeling and simulations in physics, addressing multi-scale simulation problems [20].

据媒体援引熟悉苹果(AAPL.US)内部讨论情况的人士的消息报道称，苹果在人工智能(AI)领域的持续挣 扎有可能破坏其在智能手机市场的主导地位，并危及该公司从机器人技术到下一代硬件等更广泛的雄心 壮志。 尽管苹果在2018年通过一项高调的人事任命曾一度激起外界对其AI战略的期待，但如今这家科技巨头 的AI之路却遭遇严重阻力。2018年，苹果聘请前谷歌(GOOGL.US)高管John Giannandrea领导AI战略。 这一任命曾被视为关键转折点，尤其在Siri远远落后于竞争对手的语音助手的背景下。 如今，苹果正进行架构重组。John Giannandrea已失去对Siri和相关产品开发的控制权，领导权转交给 Vision Pro头显项目负责人Mike Rockwell。苹果也在寻求与外部AI公司合作，例如OpenAI和Anthropic， 以增强自身能力。 测试聊天机器人 与此同时，工程师正在重构Siri架构，打造一个完全基于大型语言模型的新系统。苹果还在内部测试自 家聊天机器人，目标是实现与ChatGPT看齐的水平。在市场推广方面，苹果计划将Siri从"Apple Intelligence"这一更广泛品牌 ...

科洛宁还提到，人工智能的飞速发展引发了人们对滥用人工智能和通用人工智能的担忧。一些国家利用 其在人工智能领域的主导地位，对他国进行胁迫，阻挠它们与被视为威胁的国家开展合作。鉴于此，那 些希望建立公平世界秩序的国家需加深彼此间的合作，例如在金砖国家框架下，秉持互惠互利的原则， 共同推动全球科技治理体系的完善。 科洛宁强调，俄罗斯科学界对与中国以及其他志同道合的国家携手，共同推动全球在人工智能和通用人 工智能领域的协调发展与有效治理持开放态度。欢迎其他国家参与俄罗斯AGI社区研讨会等开放活动， 以及数学AI等联合会议，并期待各方逐步完善人工智能技术管理的联合战略。 另据相关报道，由外国顶尖专家组成的"瓦尔代"国际辩论俱乐部（Valdai Discussion Club）项目主任季 莫费·博尔达切夫（Timofei Bordachev）同样指出，人工智能是前沿科技领域，中国和俄罗斯都具备相 应技术和人才，两国可以通过在这一领域的合作，树立起科技合作的典范，并为全球南方国家在科学、 文化和教育领域的解放贡献力量。这不仅将为两国开辟全新的合作领域，还将切实推动南南合作，这对 于构建一个更加平衡、公正的世界秩序至关重要。 ...

Core Viewpoint - The article discusses the limitations of copper and fiber interconnects in next-generation data centers and introduces a third solution, e-Tube, which aims to support the growing demands of AI workloads and data bandwidth requirements [1][10][16]. Group 1: Challenges in Data Center Expansion - Data center AI accelerator clusters face increasing complexity due to the emergence of new technologies, particularly generative AI and large language models (LLMs), which are pushing data bandwidth beyond traditional interconnects, rapidly doubling to 800G and soon reaching 1.6T [1]. - The need for improved performance, cost control, and energy efficiency presents significant challenges for network operators [4]. Group 2: Limitations of Current Technologies - Data centers currently rely on 400G and 800G network equipment, using copper cables for short distances and fiber optics for long distances, but both technologies are approaching their respective limits in terabit interconnect speeds [3][6]. - Copper cables, while cost-effective and reliable for short distances, suffer from channel loss due to skin effect, limiting their transmission range and scalability in high-density data centers [3][6]. Group 3: Transition to Optical Interconnects - Large-scale enterprises are shifting towards optical interconnects, such as Active Optical Cables (AOC), which can provide connections over several kilometers but come with increased complexity, power consumption, and costs, potentially up to five times that of copper cables [8]. - Optical technologies are less reliable due to performance variations with temperature changes and the eventual failure of optical components, which can also introduce significant latency [8]. Group 4: Introduction of e-Tube Technology - The e-Tube platform offers a scalable multi-terabit interconnect solution using plastic medium waveguides to transmit radio frequency data, overcoming the limitations of copper and fiber optics [10][12]. - e-Tube cables, made from low-density polyethylene (LDPE), can efficiently transmit data without the high-frequency losses associated with copper, supporting data speeds from 56G to 224G and beyond [12]. Group 5: Advantages of e-Tube - e-Tube technology results in a tenfold increase in cable coverage, fivefold reduction in weight, twofold decrease in thickness, threefold reduction in power consumption, and a thousandfold decrease in latency, all while reducing costs by three times [14]. - This technology is positioned as an ideal alternative to copper cables as data centers transition to 1.6T and 3.2T speeds, providing unique power efficiency and compatibility with existing network infrastructure [14][16].

Summary of Uber's Q1 2025 Earnings Call Company Overview - **Company**: Uber Technologies, Inc. (UBER.US) - **Date**: May 7, 2025 Key Points Financial Performance - Uber reported a strong Q1 2025 performance with total bookings and trip volume both increasing, adjusted EBITDA reached $1.9 billion, a 35% year-over-year increase, and free cash flow hit a record $2.3 billion [1][2] - Monthly active users grew by 14% to 170 million, with trip volume increasing by 18% and global retention rates at an all-time high [2] Autonomous Vehicle Initiatives - Uber partnered with Waymo to deploy approximately 100 autonomous vehicles in Austin, achieving high utilization rates and positive consumer feedback, with average usage exceeding 99% compared to human drivers [3][4] - Plans to expand the autonomous vehicle fleet in Austin and other regions like Atlanta are underway [4] Pricing Strategy and Market Dynamics - Uber observed that price elasticity remains similar to past trends, where a $1 price increase negatively impacts transaction volume, but consumers are adapting to stable pricing [5] - The competitive landscape in the U.S. ride-hailing market is intense, with competitors like Bolt and DK&D in international markets, yet Uber maintains a leading position [6] Growth Outlook - Uber anticipates stronger revenue and profitability growth in Q2 2025, setting a solid foundation for the peak season in the second half of the year [7] - The company is focused on providing high-quality services and has established clear strategies and ambitious goals for future growth [7] Delivery Business Performance - The gross margin for Uber's delivery business expanded to 3.7%, a 70 basis point increase year-over-year, driven by advertising revenue and economies of scale [3][10] - The delivery business showed strong profitability with a contribution margin of 9% in Q1, indicating robust growth potential in grocery and retail sectors [10] Insurance Costs and Innovations - Uber expects moderate increases in insurance costs in 2025 but aims to alleviate cost pressures through innovations and policy adjustments [3][11] - The company is implementing driver behavior scoring to enhance safety and reduce insurance costs, with positive feedback received [11] Macro Economic Environment - The macroeconomic environment has not shown significant changes in audience growth, maintaining a stable frequency of service usage [12][13] - Uber's diverse service categories, including dining and transportation, are less affected by macroeconomic uncertainties [13] International Market Developments - In Europe, Uber has achieved a leading position in the UK food delivery market through organic growth, with France and Germany identified as key markets for future expansion [16] Emerging Market Opportunities - Sparse mobility markets present growth opportunities for Uber, with 20% of trips now coming from these areas, which are growing faster than urban core markets [18][19] - Uber plans to launch hundreds of new cities by 2025, focusing on achieving sustainable profitability in these markets [18] Future of Autonomous Driving - The autonomous driving sector is evolving, with companies like Waymo leading the way, and Uber is collaborating with various partners to develop and deploy autonomous technologies in Europe [11][15] Conclusion - Uber's strategic focus on enhancing service quality, expanding autonomous vehicle initiatives, and navigating competitive pressures positions the company for continued growth and profitability in the evolving mobility landscape [7][19]