在科技巨头微软首席执行官Satya Nadella退出星巴克董事会大约一年后，星巴克正在强化与微软的关 系。 自OpenAI于2022年底推出ChatGPT以来，各大公司一直尝试在自身运营中应用生成式人工智能，并期望 人工智能热潮能够降低成本，甚至可能推高股价。沃尔玛和摩根大通等企业巨头都已为其员工推出了人 工智能助手。 这个为期三天的活动是为了重振星巴克在美国疲软的销售。去年上任的星巴克首席执行官Brian Niccol 的首要任务包括将每单服务时间缩短至四分钟。快速准确地回答咖啡师的问题有助于实现这一目标。 星巴克首席技术官Deb Hall Lefevre表示："这只是创新技术如何为我们的合作伙伴服务的又一个例子， 并确保我们尽一切努力简化运营，让他们的工作变得更轻松、更有趣，这样他们就可以做他们最擅长的 事情。" 咖啡师无需翻阅手册或访问星巴克内网，只需使用柜台后配备绿点助手的平板电脑即可获取一系列问题 的答案，从如何制作冰镇浓缩咖啡到排除设备故障。 智通财经APP获悉，星巴克(SBUX.US)计划本月在35家门店推广其与微软(MSFT.US)Azure OpenAI平台 合作开发的生成式人工智能(AI ...

Core Viewpoint - The smartphone purchasing intention is weakening, particularly in the U.S. market, with a future 12-month purchasing intention of 36%, showing a month-on-month decline and year-on-year stability [1][12]. Group 1: Market Trends - The ideal replacement cycle for smartphones has extended to 31.1 months, indicating a slowdown in replacement behavior [2][13]. - U.S. purchasing intention has significantly dropped to 37%, down from 50% and 44% in the previous quarters [2][12]. - Despite a 3.2% year-to-date increase in smartphone sales as of April, the overall forecast for global smartphone sales remains conservative, with only a 1% year-on-year growth expected in 2025 and flat growth in 2026 [2][9]. Group 2: Impact of Tariffs - Tariffs are affecting global smartphone buyer sentiment, with 19% of respondents citing concerns over tariffs impacting prices as a reason for not purchasing [3][10]. - Among those likely to purchase a smartphone in the next 12 months, 82% are willing to accept some price increase due to tariffs, but 62% would seek cheaper alternatives if the increase is deemed excessive [3][13]. - The potential for OEMs to raise prices to offset tariff impacts could lead to a broader price increase across the market, affecting demand [3][10]. Group 3: Consumer Interest in AI Features - Interest in generative AI features in smartphones is slowly increasing, but it has not yet translated into significant changes in purchasing behavior [4][14]. - Only 34% of respondents indicated they would purchase a smartphone earlier or pay extra for generative AI features, suggesting that interest has not yet driven substantial demand [4][14]. Group 4: Stock Recommendations - UBS maintains a positive outlook on several smartphone-related stocks, including ASE, Broadcom, Hon Hai, and MediaTek, among others, with buy ratings [5][8]. - Caution is advised for Hua Hong Semiconductor, rated as a sell, and LG Display, rated neutrally [6][7].

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 ...