Core Insights - The 2025 Hannover Messe showcased the ongoing transformation in the industrial sector driven by artificial intelligence, particularly generative AI, although no groundbreaking technologies were introduced [1] - The report by IoT Analytics highlighted that generative AI has become an integral part of industrial software, moving beyond being a buzzword to a common feature in major industrial software products [3][4] - Agentic AI is emerging as the next significant trend in the industry, although it remains in its early stages of development [7][9] Trend Summaries Trend 1: Generative AI Fully Integrated into Industrial Software - Generative AI has transitioned from a focus on coding to being embedded across industrial software, with major software vendors showcasing integrated functionalities [3] - Leading companies like Siemens and ABB have developed various industrial assistants that leverage generative AI for tasks such as design, planning, and operational support [4][6] Trend 2: Emergence of Agentic AI - Agentic AI is viewed as a significant future opportunity, with many vendors promoting its capabilities, although practical applications are still limited [7][9] - Companies are exploring multi-agent frameworks, but these remain in early exploratory phases without substantial real-world validation [8] Trend 3: Significant Innovations in Edge Computing - Edge computing is evolving to integrate AI technology stacks, enhancing local processing capabilities and responsiveness [10] - Companies like Bosch Rexroth are demonstrating platforms that support AI model deployment at the edge, optimizing for specific industrial scenarios [10][11] Trend 4: Growing Demand for DataOps Platforms - DataOps is becoming essential for managing the increasing volume of data in industrial settings, with platforms expanding their capabilities to support AI lifecycle management [13][14] - Companies are focusing on data governance to ensure compliance with regulations like GDPR, enhancing data observability and tracking [14] Trend 5: AI-Driven Digital Threads Transforming Design and Engineering - Digital threads are reshaping engineering processes by ensuring data continuity throughout the product lifecycle, as demonstrated by Siemens' new solutions [17] - Autodesk's Project Bernini showcases how generative AI can enhance early design processes, promoting a multi-modal design approach [17] Trend 6: Sensorization of Predictive Maintenance - Predictive maintenance solutions are increasingly integrating custom hardware with analytics models, focusing on sensor quality and system compatibility [18][19] - New solutions are extending predictive maintenance capabilities to previously overlooked asset categories, enhancing monitoring and fault detection [18] Trend 7: Rising Demand for Private 5G Networks - The demand for private 5G networks is growing, particularly in the US and Asia, but integration with existing infrastructure remains a significant challenge [21][22] - Companies are developing solutions that combine generative AI, edge computing, and private 5G for real-time industrial safety and asset monitoring [22] Trend 8: Sustainable Solutions Enhanced by AI - AI is improving carbon emissions tracking and compliance efficiency, with various applications being upgraded to enhance data visibility and accuracy [23] - Collaborative efforts, such as those between Microsoft and Accenture, are optimizing compliance processes through AI integration [23] Trend 9: Cognitive Capabilities Empowering Robotics - Robotics manufacturers are incorporating cognitive AI and voice interaction features, allowing users to control robots through voice commands [24] - This trend aims to enhance flexibility and reduce the need for specialized skills in manufacturing and logistics [24] Trend 10: Digital Twins Evolving into Real-Time Industrial Co-Pilots - Digital twins are transitioning from static models to dynamic tools that assist in operations, training, and quality control [25] - Companies like EDAG Engineering and Siemens are showcasing how AI-driven digital twins can optimize processes and enhance training efficiency [25]

此外，在能力提升方面突出"精准滴灌"。《若干措施》聚焦数字经济技术、管理能力有效提升，定制化 开展专业能力培训；实行数字经济企业家培育导师制度，创新性开展数字经济领域经营管理领军人才认 定，选聘知名企业家担任导师，为新生代企业家提供经营管理指导咨询。 "数字经济是济南的优势所在，更是潜力所在。为更好地赋能济南市数字经济产业高质量发展，增加数 字经济人才有效供给，我们研究制定了《济南市关于加强数字经济人才发展的若干措施(试行)》。"徐 磊介绍，《若干措施》围绕数字经济人才引进、能力提升、服务优化等主要环节，提出了12条具体举 措，主要有四个突出特点： 徐磊表示，一是在人才引进方面突出"高精尖缺"。《若干措施》聚焦大模型、生成式人工智能、网络安 全等紧缺产业需求，坚持高端人才引领、国内国外并举，吸引集聚高层次数字经济人才。通过编制池保 障、项目资助等多种方式，加大对数字经济高端人才、顶尖人才以及首席数据官等领军型人才引进力 度。深化"揭榜挂帅"机制，吸引国内外数字经济领域工程师、创客团队等定向攻关、来济创业。 其次，在人才培育方面突出"校企联动"。《若干措施》聚焦校地融合发展，持续支持校企共建现代产业 学院、公共 ...

Group 1: Core Concepts of Emotional Economy - The rapid development of artificial intelligence (AI) is leading to a transformative shift towards an "emotional economy," where human emotions and empathy are elevated in importance [1] - Marketing professionals are increasingly required to develop "soft" skills, such as intuition and sensitivity, as AI takes over analytical tasks [1][3] - The concept of "professional capital" is introduced, emphasizing the unique role of marketers in leveraging their emotional intelligence and intuition in the marketing field [1][3] Group 2: Importance of Intuition and Sensitivity - Marketing decisions are akin to a tennis player's instinctive reactions, where intuition and "feel" play a crucial role in decision-making [2][3] - AI can provide data-driven insights, but the unique context of each marketing situation necessitates human intuition for effective decision-making [2][3] - The significance of marketers' intuition and sensitivity is heightened in the context of AI-driven marketing practices [3] Group 3: Marketing Revolution in the Age of AI - The emergence of generative AI technologies poses a potential threat to traditional marketing roles, as these technologies can automate content creation and marketing strategies [4][5] - Generative AI has proven effective in generating marketing content and creative ideas, leveraging historical data to enhance marketing efforts [4][5] - The focus of marketing has shifted towards emotional engagement, with AI capable of analyzing consumer behavior to create emotionally resonant content [5][6] Group 4: Role of Marketers in AI-Driven Marketing - Despite AI's capabilities, marketers remain essential for selecting, modifying, and executing AI-generated content, as they understand consumer emotions and market dynamics [15][18] - Marketers' experience and theoretical knowledge are critical in transforming AI-generated ideas into actionable marketing strategies [19][20] - The dynamic nature of marketing requires human intervention to adapt strategies based on real-time market changes, which AI alone cannot achieve [19][24] Group 5: Practical Theories and Experience - Practical theories, such as service-dominant logic, guide marketers in understanding consumer needs and avoiding short-sighted marketing strategies [20][22] - Marketers' accumulated experience enhances their intuition and decision-making capabilities, allowing them to navigate complex market environments effectively [23][24] - The interplay of experience and intuition is vital for marketers to maintain their relevance and effectiveness in an AI-driven landscape [23][26] Group 6: Future of Marketing in the Emotional Economy - The predictions made in "The Emotional Economy" regarding the impact of AI on work processes are applicable to the marketing sector, indicating that marketers cannot be fully replaced [25] - Companies are encouraged to cultivate their unique skills and knowledge to enhance their professional capital in the emotional economy [26]

报告提出，全球各区域就业市场呈现不平衡发展，就业增长速度差异明显。亚太地区仍是全球就业增长 最快的地区，2025年其就业增长预期为1.7%，新增就业约3400万人；非洲就业保持较快速度增长，其 人口结构支持劳动力扩张，但增长更依赖劳动密集型行业，且非正规就业高达85%，就业质量较低；报 告将美洲2025年就业增长预期由1.6%下调至1.2%，受美国经济发展放缓和贸易政策不确定性影响，拉 美国家非正规就业率偏高；欧洲和中亚地区受能源转型、地缘政治不稳定影响显著，是就业增长最慢的 地区，就业增长预期仅为0.6%，且青年和移民群体就业质量问题突出；阿拉伯国家增长分化显著，石 油出口国与依赖进口的国家走势各异，总体上就业增长虽强，但非正规就业增长较正式就业增长更快， 且多数国家仍依赖公共部门吸纳就业，私营部门活力不足。 报告强调，当前生成式人工智能对就业市场有着深远影响。最新研究显示，全球约有23.8%的劳动者在 工作中一定程度上面临着生成式人工智能的挑战。尤其是在高收入国家，这一比例高达三分之一。报告 认为，生成式人工智能更可能改变职业中的某些任务，而不是完全替代整个职业。例如，文书工作中的 数据录入和报告生成等任 ...

WNS (WNS) 2025 Conference June 03, 2025 04:20 PM ET Speaker0 Well, good afternoon, everyone. My name is Dave Koning. I'm a senior research analyst at Baird covering payments and services. Thrilled to introduce WNS. We have Dave Mackie here. He just said he's been at WNS for fourteen years. I've been at Baird for twenty three, so there's a lot of years involved. And WNS, a leader in business process management, recently reaccelerated really nicely getting back on track to normalize growth. And why don't I tu ...

Group 1 - The event "Guarding My World - Together 'Child' Walk" was successfully held in Dalian, China, on Children's Day, focusing on children's safety and science education [1][2] - The "One Meter Science" initiative aims to shift the perspective of science education from teaching children to empowering them to engage with science [2][6] - The event featured 30 sets of children's science comics covering various safety themes, designed from a child's perspective to facilitate learning [2][10] Group 2 - The design of the comic display area encourages parents to engage with children at their level, promoting a more equal and understanding interaction [4][6] - The event included interactive zones such as the "Fire Safety Education Interactive Zone," where children learned self-protection skills related to recent fire incidents [10][11] - The "Potato Dou Serious Science" series has gained significant recognition, with over 21 billion views and numerous accolades, becoming a popular choice for family science education [22]

Core Insights - A new wave of AI-native companies is emerging globally, defined as those that integrate AI as a core product or service from inception, driving value creation and business innovation [1] - The research focuses on three key questions regarding the technologies, applications, and structural changes brought about by AI-native enterprises [1] Group 1: Overview of Global AI-native Ecosystem - The global generative AI landscape has formed three main foundational model ecosystems centered around OpenAI, Anthropic, and Google [2] - OpenAI's ecosystem is the largest, with 81 startups and a total valuation of approximately $63.46 billion, showcasing a diverse range of applications [2] - Anthropic's ecosystem consists of 32 companies valued at around $50.11 billion, focusing on enterprise-level applications with high safety and reliability requirements [3] - Google's ecosystem is the smallest, with 18 companies valued at about $12.75 billion, but it is growing rapidly due to its technological integration and vertical innovation [3] Group 2: Multi-model Access Strategy - Many AI-native companies are adopting multi-model access strategies to leverage the strengths of different foundational models and reduce dependency on a single ecosystem [4] - Companies like Anysphere and Jasper support multiple model integrations, enhancing their competitive edge [4] - These companies typically follow a B2B2B model, focusing on sectors like data, marketing, and finance, which allows them to cater to diverse client needs [4] Group 3: Self-developed Models - A growing number of companies are focusing on developing their own models, categorized into two types: unicorns targeting general models and those specializing in vertical markets [5][6] - Companies like xAI and Cohere aim for breakthroughs in foundational models, while others like Midjourney and Stability focus on niche applications [6] Group 4: Differentiated Ecosystem Strategies - The competition in generative AI has shifted from model capabilities to ecosystem building, with OpenAI, Anthropic, and Google each pursuing distinct strategies [8] - OpenAI emphasizes platform attractiveness, Anthropic focuses on safety, and Google leverages its integrated ecosystem for comprehensive solutions [9][10][11][13] Group 5: Developer and Channel Strategies - OpenAI provides a general development platform with a plugin ecosystem, incentivizing developers to innovate [14] - Anthropic emphasizes a B2B integration strategy centered on safety, using protocols to connect its models with external systems [15] - Google offers a full-stack AI development environment, integrating its models deeply into its product ecosystem [16][19] Group 6: Pricing Strategies - OpenAI employs an API billing model with subscription options, gradually lowering prices to expand its user base [23] - Anthropic uses a flexible pricing strategy, focusing on value for high-value clients while maintaining competitive pricing [24] - Google combines low pricing with cross-subsidization strategies to rapidly expand its market share [25] Conclusion - The ecosystem barriers and user stickiness in the AI industry are still in the early stages of formation, with significant potential for change as technology evolves [26]

长飞光纤 2024 年年度股东大会会议资料 长飞光纤光缆股份有限公司 会议资料 二〇二五年六月 长飞光纤 2024 年年度股东大会会议资料 长飞光纤光缆股份有限公司 六、 大会审议事项 投票股东类型 序 议案名称 A股 H股 号 股东 股东 关于提请股东大会授权董事会办理公司员工持股计划有关事项的 议案 非累积投票议案 长飞光纤 2024 年年度股东大会会议资料 核全年业绩公告 关于续聘毕马威华振会计师事务所（特殊普通合伙）为公司 2025 年度审计机构的议案 《股 东大会议事规则》《董事会议事规则》《审计委员会工作细则》《提名及薪酬委 员会工作细则》《战略委员会工作细则》及《独立董事工作细则》等内部管理制 度的修订，完善了权责明确、运作规范的经营管理体系，提升了科学决策水平和 风险控制能力。董事会二零二四年度的主要工作报告如下： 一、业务回顾 二零二四年，全球光纤光缆需求持续承压，行业及公司同时面临挑战与机遇。 在国内市场，一方面 5G 及 FTTH 覆盖率已达全球领先水平，传统光纤光缆产品 有效需求不足，增速不断趋缓。根据国家统计局发布的数据，二零二四年国内累 计光缆产量同比下降约 18.2%。另一方面， ...

本文节选自彭博终端"彭博行业研究《2025年生成式AI展望》"，彭博终端用户可运行{NSN SWJ7Y1DWX2PS0 }阅读。如您还不是终 端用户，您可在文末"阅读原文"联系我们预约产品演示。 彭博行业研究 2025年生成式AI展望 生成式人工智能（AI）和大语言模型（LLM）的应用已经渗透到科技领域的各个环节并迅速发 展。预计到2032年， 这个市场将创造约1.8 万亿美元的收入。 彭博行业研究认为，随着由思维链和强化学习加持的推理模型更受青睐，LLM的应用可能从基 于文本的搜索扩大至各种图片、音频和视频的分析；除了LLM赋能的合同审查和客服聊天机器 人等现有用例外，集成写作和编程助手以及利用文本和语音提示词生成图像和视频的工具，也 将推动生成式 AI智能体在消费端和企业端的部署；DeepSeek问世后，大多数LLM公司都致力 于提高模型效率，从而实现大规模推理。 核心议题： 长按或扫描二维码 阅读完整报告 推理超过训练的时间有望提前： 推理支出超过训练支出的时间可能比我们之前的预测至 少提前三年。 大语言模型之间的差距缩小： OpenAI的GPT、谷歌的Gemini、Meta的Llama、 Anthro ...

Core Viewpoint - World Labs, co-founded by Fei-Fei Li, has open-sourced a core technology called Forge, a real-time 3D Gaussian Splatting renderer that operates seamlessly across various devices, including desktops, low-power mobile devices, and XR [1][6]. Group 1: Technology Overview - Forge is a web-based 3D Gaussian Splatting renderer that integrates with three.js, enabling fully dynamic and programmable Gaussian splatting [2]. - The underlying design of Forge is optimized for GPU, serving a role similar to traditional 3D graphics components known as "shaders" [3]. - The technology allows developers to handle AI-generated 3D worlds as easily as manipulating triangle meshes, according to Ben Mildenhall, co-founder of World Labs [5]. Group 2: Features and Capabilities - Forge requires minimal code to start and run, supporting multiple splat objects, cameras, and real-time animations/edits [4]. - It is designed as a programmable 3D Gaussian Splatting engine, providing unprecedented control over the generation, animation, and rendering of 3D Gaussian splats [8]. - The renderer employs a painter's algorithm for sorting splats, which is a core aspect of its design [13]. Group 3: Rendering Process - The key component managing the rendering process is ForgeRenderer, which compiles a complete list of splats in a three.js scene and determines the drawing order using an efficient bucket sort algorithm [14]. - Forge supports multi-view rendering by generating additional ForgeViewpoint objects, allowing for simultaneous rendering from different perspectives [15]. Group 4: Future Plans - World Labs aims to elevate multimodal AI from 2D pixel planes to full 3D worlds, with plans to launch its first product in 2025 [17]. - The company intends to develop tools beneficial for professionals such as artists, designers, developers, filmmakers, and engineers, targeting a wide range of customers from video game developers to film studios [17].