Core Viewpoint - The financial landscape and business are undergoing a transformation due to the rise of intelligence, necessitating financial institutions to embrace the intelligent revolution and accelerate localized deployment [1][2] Group 1: Digitalization and Intelligence in Finance - The digitalization of the financial industry is still incomplete, but the intelligent phase has already begun [1] - The financial sector has been at the forefront of digitalization and intelligence, initiating a dual empowerment process [1] - Current applications of generative artificial intelligence in finance are still in the early stages, with strong independent capabilities in single issues but lacking in multi-step task execution [1] Group 2: Strategies for Localized Deployment - A comprehensive solution should be designed for full-scenario revolution, establishing a roadmap to create intelligent workflows and reorganize information and data flows [1] - A leapfrog deployment strategy is recommended, focusing on lightweight localized deployment of AI technologies, as costs are decreasing and demands for diverse intelligent products are increasing [2] - Strengthening foundational capabilities is crucial, including building large-scale domestic computing clusters and addressing data silos within financial institutions [2]

值得一提的是，针对使用对象多为未成年人的学习教育类模型，《指引》建议其重点建设具备科学性的 高质量训练数据集，充分过滤其中的错误知识内容。 在模型训练阶段，《指引》强调，重点防范生成有害内容与个人信息泄露风险，通过引入未成年人保护 评价机制，将生成内容安全性作为评价生成结果优劣的主要指标之一，建议在模型训练过程中引入保护 未成年人权益的安全原则，约束模型的输出符合伦理规范、减少偏见等。同时，通过对模型输出内容识 别和过滤，改写或去除有害内容，以确保其输出内容的安全性。 在场景应用阶段，《指引》提到，服务提供者宜采取有效技术防护措施和安全管理措施，保障服务内容 的安全性、可靠性和真实性。其中包括，结合不同场景生成内容诱导或引起未成年人效仿危险行为的风 险，并采取预警、风险提示、风险阻断等合理应对措施，特别是要警惕特殊应用场景风险——包括但不 限于娱乐场景未成年人沉迷、情感依恋等问题。 在服务运营阶段，《指引》就未成年人身份认证、未成年人模式、网络防沉迷、权限管理和消费管理等 方面提出建议。比如在消费管理机制上，《指引》指出，网络社交、网络游戏、网络直播、网络音视 频、在线教育等场景下的生成式人工智能服务所产生的 ...

埃森哲CEO表示，埃森哲培训了50万名员工使用生成式 人工智能。 ...

Group 1 - Nvidia's CEO Huang Renxun praised the UK's AI talent as "the envy of the world," highlighting the need for more infrastructure to unlock this potential [1] - The UK government announced a partnership with Nvidia to cultivate more AI-skilled talent and expand related research, with a focus on enhancing data center capabilities [2] - Huang emphasized that AI is both a technology and an infrastructure, capable of profoundly impacting various industries, and should be viewed similarly to electricity [2] Group 2 - The UK government plans to invest £1 billion (approximately $1.4 billion) to increase the country's computing power by 20 times, addressing the infrastructure gap for AI development [1] - Liquidity, a startup loan institution, announced plans to establish its European headquarters in London and invest £1.5 billion in related enterprises over the next five years [3] - The UK government is prioritizing technology and growth in its economic plans, with significant funding allocated to the science sector, including £86 billion (approximately $116 billion) for pharmaceuticals, green energy, and military technology [3]

据"网信上海"公众号消息，截至6月9日，上海市新增11款已完成备案的生成式人工智能服务，其中阶跃 多模态等在列，累计已完成82款生成式人工智能服务备案。 ...

Core Viewpoint - The "Longteng" AI-assisted execution system has been officially launched in Longhua District People's Court, marking the first nationwide application that covers the entire process of handling execution cases, aiming to address the challenges of execution difficulties through innovative solutions and robust data support [1][2]. Group 1: System Features and Functionality - "Longteng" integrates data from 21 application platforms, including the national execution system and electronic files, to streamline operations and eliminate redundant data entry [2]. - The system features a "Property One-Stop" interface that consolidates case information, execution amounts, and property feedback, along with one-click disposal and intelligence analysis capabilities [2]. - It includes tools such as an execution amount calculator and tax invoice verification to enhance execution efficiency [2]. Group 2: Performance and Impact - Since its trial run on December 2 last year, "Longteng" has processed 7,847 execution cases and completed property control for 3,052 cases, resulting in an average processing time reduction of 28.9 days from January to April this year [2]. - The system has led to a 20% year-on-year decrease in petition numbers related to execution cases in Longhua Court this year [4]. Group 3: Advanced Analytical Capabilities - "Longteng" employs deep analytical functions of generative AI to conduct intelligent analysis using vast data, including vehicle entry records and road camera data to track the whereabouts of debtors [3]. - It integrates corporate data from "Tianyancha" to analyze debtors' business profiles and uncover hidden assets [3]. - The system can assess debtors' compliance capabilities and recommend execution plans based on comprehensive data analysis [3]. Group 4: Process Management and Oversight - "Longteng" connects with public demand platforms to generate reports and send alerts to judges, ensuring accountability in case management [4]. - The system provides real-time warnings for critical deadlines and generates lists of problematic cases for leadership oversight, enhancing regulatory support for execution standardization [4].

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 ...