朱平表示，生成式AI的快速发展正在改变客户与保险机构的交互方式。过去以被动响应为主的服务模 式，正在向更加连贯、智能、随时可达的体验转变。这意味着，服务正在从以产品为中心的单点处理， 迈向以关系为核心的全周期陪伴。 朱平认为，未来的保险服务将呈现出更高频、更低摩擦、更智能的交互形态。随着多模态大模型能力的 增强，服务将突破传统边界，融入客户在健康、医疗、资产管理及家庭规划等场景中的日常决策。未来 保险产品的组合与SKU将呈指数级增长，服务模式将更贴近客户的实际需求，以智能方式实现动态匹 配。"保险将成为日常生活和长期规划的一部分，而不再是一纸静态合约。" 据介绍，作为平安集团唯一对外金融科技输出窗口，金融壹账通正在将平安在客户服务、保险科技、智 能风控等领域的成熟能力体系化输往全球市场。依托在SaaS化平台、业务场景数字化方面的长期积累， 金融壹账通持续推动金融科技在东南亚、中东等地区的落地，帮助当地金融机构提升效率、优化体验、 加速数字化转型。未来，金融壹账通将继续依托平安集团的技术底座与场景优势，以可复制、可规模 化、可持续的方式促进中国金融科技能力在国际市场发挥更大价值。 本报讯(记者李冰)近日，202 ...

Core Insights - The article discusses the transition of AI development from a "toy" phase to a monetizable stage, highlighting the launch of Baidu's no-code application platform "MiaoDa" which has generated over 500,000 commercial applications in just eight months [1][2] Group 1: MiaoDa Platform Progress - MiaoDa has seen a daily increase of over 150% in new applications, with half of these applications featuring backend capabilities, covering over 200 scenarios including education, business, content creation, and enterprise services [1][3] - The platform has created economic and efficiency value exceeding 5 billion yuan [1][3] Group 2: Creator Support Initiatives - Baidu announced the "Creator Dream Plan," aiming to support 1 million creators over the next three years through traffic support, revenue sharing, project matching, and technical assistance [1][2] - By 2026, Baidu plans to select 15 high-potential projects for expedited investment opportunities, with individual developers potentially receiving over 1 million yuan [1][2] Group 3: User Demographics and Application Types - 81% of MiaoDa users are non-programmers, primarily from workplace and university backgrounds, with applications serving over 10 million users and around 100,000 daily users [3][4] - The applications fall into three main categories: monetizable tools like e-commerce mini-programs, business software for low-cost internal systems, and AI efficiency applications for various life scenarios [3][4] Group 4: User Case Studies - Users from various industries shared success stories, such as engineers creating cost-saving design systems and content creators developing AI tools for niche markets [4] - Young creators have also utilized MiaoDa to develop innovative applications, showcasing the platform's potential to enhance creativity and practical skills among youth [4] Group 5: Industry Trends - The maturation of no-code platforms is enabling a broader range of individuals to participate in innovation and commercialization at lower costs, indicating a shift in competitive dynamics in the AI sector [5][6] - Future competition may focus on building a comprehensive ecosystem that encompasses creation, distribution, and monetization [6]

即将过去的2025年，谷歌是表现最好的硅谷科技大厂之一： 股价累计上涨了63%，最多的时候涨了70%，大幅度地跑赢了标准普尔500和纳斯达克指数；谷歌还 曾经十分逼近4万亿美元大关，差一点点就要成为继微软、英伟达、苹果之后，人类历史上第四家 市值突破4万亿美元的公司。 过去的一年半，简直是谷歌"绝地反击"的一部成功历史，完全可以拿来拍电影。 01 截止2024年5月，在生成式AI的阴影之下，谷歌怎么看都像是快完蛋了的样子：Gemini大模型表现不好，TPU 没有外部客户，TensorFlow平台则完全被PyTorch替代。 人们严肃地怀疑，大模型终将取代搜索引擎，成为未来十年用户获取信息的首要途径。虽然谷歌的核心广告业 务表现比较强劲，但华尔街认为那是暂时的——关键是谷歌在AI方面的组织战斗力很低，在技术端和产品端均 未能做出有效反应，或许折射了决策机制和企业文化方面的某种缺陷。 Gemini 3.0完全基于TPU训练，由此进一步点燃了外部客户采购TPU的兴趣，2026-2027年将成为 TPU大举占领外部市场的时刻，今后人工智能芯片市场的第一名仍然是英伟达，第二名却不是 AMD，而是谷歌。 更重要的是，谷歌 ...

本文共同第一作者为西安交通大学硕士生常建磊和博士生梅若风。柯炜为西安交通大学副教授。论文通讯作者为西安交通大学教授许翔宇，其研究方向涵盖三维 视觉、生成式 AI 与具身智能（个人主页：https://xuxy09.github.io/）。 生成式模型正在成为机器人和具身智能领域的重要范式，它能够从高维视觉观测中直接生成复杂、灵活的动作策略，在操作、抓取等任务中表现亮眼。但在真实 系统中，这类方法仍面临两大「硬伤」： 一是训练极度依赖大规模演示数据，二是推理阶段需要大量迭代，动作生成太慢，难以实时控制。 针对这一核心瓶颈，西安交通大学研究团队提出了全新的生成式策略学习方法 EfficientFlow 。该方法通过将 等变建模与高效流匹配（Flow Matching）深度融合 ， 在显著提升数据效率的同时，大幅压缩推理所需的迭代步数 ，在多个机器人操作基准上实现了 SOTA 的性能，并将推理速度提升一个数量级以上。 相关论文《EfficientFlow: Efficient Equivariant Flow Policy Learning for Embodied AI》 已被 AAAI 2026 接收，代码已开 ...

编者荐语： 日经中文网"开设了"日经BP精选"栏目。日经BP是日本经济新闻社媒体集团的一员，成立于1969年。作 为日本领先的B2B媒体公司，聚焦经营管理、专业技术及生活时尚三大主要领域。敬请读者关注。 以下文章来源于日经BP ，作者日经BP 华为正在通过自主 AI 半导体提高存在感（图片来源：日经 XTECH ） " 华为王国正逐步形成 " ， 一位专家这样分析称。展望 2026 年的中国半导体产业，将以华为为 中心加速自产化。在 AI 半导体方面，英伟达将迎来华为和新兴企业中科寒武纪科技等的挑 战。另一方面，中国的制造设备和材料距离最先进水平仍有差距 …… 从2026年的中国半导体产业前景来看，将以通信设备大企业华为（HUAWEI）为中心加速自产化。垄断 生成式AI（人工智能）半导体市场的美国英伟达（NVIDIA）的"阵地"将迎来华为和新兴企业中科寒武 纪科技的挑战。在美国的压力下，探索自主半导体供应链的中国企业或提高存在感。 美国最大银行摩根大通在2025年的预测中指出，华为和寒武纪的AI半导体出货量将在2026年总计超过 100万个。与有关2024年出货量的该公司预测相比增至2倍以上。 阅读更多内容请 ...

Core Viewpoint - The article emphasizes the transformative impact of artificial intelligence (AI) on the manufacturing industry, highlighting the shift from traditional automation to AI-driven cognitive and optimization processes, and the importance of participating in this evolution rather than being a mere observer [2][3]. Group 1: AI in Manufacturing - AI is evolving into the "second brain" of factories, moving beyond simple automation to take over cognitive tasks, predictions, and optimizations, as evidenced by applications in companies like Hisense and Siemens [3]. - The penetration rate of AI in logistics has exceeded 37%, leading to significant efficiency improvements [3]. - The focus for entrepreneurs has shifted to specific metrics such as production speed and inventory reduction, making the stakes of AI adoption in manufacturing high due to substantial investments and safety concerns [4]. Group 2: Hannover Messe - Hannover Messe is positioned as a critical platform for defining the future of manufacturing, showcasing real solutions to pressing industry challenges rather than abstract concepts [6][8]. - The event serves as a "pressure test" for trends before they become industry standards, with significant participation from over 4,000 top companies and 200,000 decision-makers annually [9][11]. - The 2026 Hannover Messe will focus on "Generative AI and Industrial Collaboration," featuring key themes such as the transition from tool applications to AI-driven processes, the integration of green concepts into profitable manufacturing, and the collaboration across industry boundaries [12][13][15]. Group 3: Hidden Champions - The article discusses the concept of "hidden champions" in Germany, which are small to medium-sized enterprises that dominate niche markets globally despite low public visibility [17]. - These companies thrive by focusing on "gap market" strategies, avoiding mainstream competition, and leveraging a robust education-research-industry ecosystem to foster innovation [18][19]. - The challenges faced by these hidden champions, such as rising energy costs and labor shortages, highlight the need for resilience and adaptability in a rapidly changing global landscape [20]. Group 4: Strategic Insights - Peter Löscher, former global CEO of Siemens, will share insights on how traditional manufacturing firms can navigate technological changes and balance global resource integration with local market innovation [27][28]. - The discussion will also cover how the competitive landscape of manufacturing is being reshaped in the era of generative AI, emphasizing the importance of ecosystem collaboration to build core competitive advantages [29]. - The journey to Hannover is framed as an opportunity to witness cutting-edge technologies and learn from successful business models that can help companies thrive in the face of global challenges [30][31].

Group 1: Manus 1.6 Release - Manus 1.6 Max has transitioned from an "auxiliary tool" to an "independent contractor," resulting in a 19.2% increase in user satisfaction, capable of independently completing complex Excel financial modeling and data analysis [1] - New mobile development features support end-to-end app development processes, allowing users to generate runnable iOS and Android applications simply by describing their needs [1] - The introduction of Design View allows for localized image editing, precise text rendering, and multi-layer composition, addressing the uncontrollable issues of AI-generated images [1] Group 2: OpenAI Circuit-Sparsity Model - OpenAI has released the Circuit-Sparsity model with only 0.4 billion parameters, enforcing 99.9% of weights to be zero, retaining only 0.1% non-zero weights, which addresses model interpretability issues [2] - The sparse model forms a compact and readable "circuit," reducing the scale by 16 times compared to dense models, although it operates 100 to 1000 times slower [2] - The research team proposed a "bridge network" solution to insert encoder-decoder pairs between sparse and dense models, enabling interpretable behavior editing of existing large models [2] Group 3: Thinking Machines Product Update - Thinking Machines, founded by former OpenAI CTO Mira Murati, has opened access to its Tinker product, an API for developers to fine-tune language models [3] - The update includes support for Kimi K2 Thinking fine-tuning (designed for long-chain reasoning) and Qwen3-VL visual input (available in 30B and 235B models) [3] - A new inference interface compatible with OpenAI API has been introduced, allowing users to easily integrate with any platform that supports OpenAI API, simplifying the post-training process for LLMs [3] Group 4: NotebookLM Integration with Gemini - NotebookLM has officially integrated with the Gemini system, allowing users to add NotebookLM notes as data sources for Q&A within Gemini conversations [4] - Gemini acts as a "hub" connecting multiple NotebookLM notes, resolving the issue of NotebookLM not supporting notebook merging, enabling simultaneous queries across multiple notes [4] - The content from NotebookLM can now be used alongside online information, facilitating a mixed analysis of "personal data + global information," integrating into Google's core AI product line [4] Group 5: Tongyi's Model Releases - Tongyi Bailing has upgraded the Fun-CosyVoice3 model, reducing initial latency by 50% and doubling the accuracy of mixed Chinese-English recognition, supporting 9 languages and 18 dialects for cross-lingual cloning and emotional control [5] - The Fun-ASR model achieves a 93% accuracy rate in noisy environments, supports lyrics and rap recognition, and covers 31 languages for free mixing, with the initial word latency reduced to 160ms [5][6] - The open-source Fun-CosyVoice3-0.5B provides zero-shot voice cloning capabilities, while the lightweight Fun-ASR-Nano-0.8B version offers lower inference costs [6] Group 6: Zoom's AI Claims - Zoom claims to have achieved a score of 48.1% on the "Human Last Exam" HLE benchmark, surpassing Google Gemini 3 Pro's score of 45.8% by 2.3 percentage points [7] - The company employs a "federated AI approach," combining its small language model with both open-source and closed-source models from OpenAI, Anthropic, and Google, using a Z-scorer scoring system for output selection [7] - This score has not appeared on the official HLE leaderboard, and on the same day, Sup AI announced a score of 52.15%, indicating Zoom's ambition to become the AI hub in enterprise workflows [7] Group 7: Gemini 3's CFA Exam Performance - Recent research indicates that reasoning models have passed all levels of the CFA exam, with Gemini 3.0 Pro achieving a historic high of 97.6% on Level 1 and GPT-5 leading Level 2 with 94.3% [8] - In Level 3, Gemini 2.5 Pro scored 86.4% on multiple-choice questions, while Gemini 3.0 Pro reached 92.0% on open-ended questions, showing significant improvement from previous years [8] - Experts caution that passing exams does not equate to practical capability, noting that AI struggles with ethical questions and cannot replace analysts' strategic thinking and client communication [8] Group 8: OpenEvidence Valuation Surge - OpenEvidence is undergoing a $250 million equity financing round, with a post-money valuation reaching $12 billion, doubling from its previous round two months ago [9] - The company generates revenue by selling advertising space for chatbots to pharmaceutical companies, with an annual advertising income of approximately $150 million, tripling since August, and a gross margin exceeding 90% [9] - An OffCall survey indicates that about 45% of U.S. doctors use OpenEvidence, answering approximately 20 million questions monthly, with its medical journal information being more accurate than general chatbots [9] Group 9: OpenAI's Sora Development Insights - OpenAI's development of the Android version of Sora was completed in just 28 days by a team of 4 engineers collaborating with the AI agent Codex, consuming around 5 billion tokens, with approximately 85% of the code generated by AI [10] - The team utilized an "exploration-validation-federation" workflow, allowing Codex to handle heavy coding tasks while engineers focused on architecture, user experience, and quality control, achieving a 99.9% crash-free rate [10] - Codex is responsible for 70% of OpenAI's internal PR weekly, capable of monitoring its training process and handling user feedback, creating a self-evolving model of "AI iterating AI" [10]

据中证报报道，商务部等6部门印发《促进服务外包高质量发展行动计划》，其中提到，到2030年培育 一批具有国际竞争力的服务外包龙头企业，建设一批创新能力强、特色优势产业明显的服务外包集聚 区，提升服务外包数字化、智能化、绿色化、融合化发展水平，显著增加就业人数，使服务外包成为创 新提升服务贸易、创新发展数字贸易的重要组成部分。 中证报指出，服务外包是服务贸易的重要组成部分和数字贸易的重要实现途径，在构建新发展格局中具 有重要作用。云计算、大数据、人工智能、物联网等新兴技术将深度融入服务外包行业，推动行业向数 字化、智能化、高端化方向转型。特别是生成式AI技术的应用，将大幅提升服务外包的效率和质量， 推动行业向智能化、平台化转型。 传统服务模式将向"产业赋能+生态服务"模式升级，服务外包将从单纯的"成本中心"转变为"价值中 心"。服务外包行业将在数字化转型、技术创新和政策支持下实现高质量发展，成为推动服务贸易和数 字贸易的重要引擎。 公司方面，据中证报表示，A股相关概念股有超讯通信 、康龙化成等。 *免责声明：文章内容仅供参考，不构成投资建议 *风险提示：股市有风险，入市需谨慎 ...

Group 1: Business Collaborations and Revenue Generation - The company has secured compliance data service business orders from major domestic and international AI model companies, including Alibaba, Tencent, and Microsoft, for model training purposes [2] - The company is a primary supplier of multimodal data for Tencent's mixed Yuan model and has established a partnership with Microsoft that includes both compliance data for exclusive model training and copyright material integration [2] - The company has launched a creative ToB custom service platform that integrates with top global models like Midjourney and nanobanana, facilitating various content generation services [2] Group 2: Data Resources and Industry Position - The company possesses over 700 million high-quality, copyright-compliant content data for AI model training, leading the industry [4] - It has a comprehensive structured metadata system with 3 million structured tags and industry knowledge graphs, providing a solid foundation for compliant training and commercial applications [4] - The company emphasizes that the demand for high-quality copyright data will continue to grow as AI models evolve, positioning itself as a key player in the generative AI value chain [4] Group 3: Content Monetization and IP Management - The company is actively exploring IP revenue-sharing models in collaboration with major platforms, recognizing the importance of high-quality compliant content for monetization opportunities [4][5] - Strategic partnerships have been established with platforms like Vidu and Jianying, allowing the company to provide authorized content to users beyond the Jianying platform [4] - The company aims to enhance its collaboration with generative AI platforms to explore IP licensing management and content revenue-sharing models [5]

Core Insights - Black Forest Labs, a German AI startup, has gained recognition for its FLUX.2 model, ranking second in the latest Artificial Analysis text-to-image model rankings, just behind Google's Nano Banana Pro [2][3] - The company has achieved significant financial milestones, raising over $450 million since its inception in August 2024, with a recent $300 million Series B funding round that tripled its valuation to $3.25 billion [8][22] - Black Forest Labs has established partnerships with major tech companies, including a $140 million multi-year contract with Meta, and collaborations with Adobe and Canva, indicating strong market demand for its AI image generation technology [9][19] Financial Performance - As of August 2023, Black Forest Labs reported an annual recurring revenue of $96.3 million, with projections to reach $300 million by the fiscal year 2026 [19] - The company’s valuation increased from $1 billion to $3.25 billion within a year, reflecting investor confidence and market traction [8][22] Technological Advancements - The FLUX.2 model has been noted for its impressive performance, nearly matching Google's offerings, and supports high-resolution image generation up to 4K [20][22] - Black Forest Labs has positioned itself as a leader in open-source AI models, with its FLUX series gaining significant traction in the developer community, evidenced by over 225,000 downloads on Hugging Face [5][20] Strategic Partnerships - The company has secured substantial contracts with industry giants, including a $35 million payment from Meta in the first year of their partnership, increasing to $105 million in the second year [16] - Collaborations with xAI, Adobe, and Canva have further solidified its market presence, with total contract values exceeding $300 million [19] Market Positioning - Black Forest Labs aims to differentiate itself by focusing on the creative industry, particularly in Hollywood, while maintaining a commitment to intellectual property and enhancing creator capabilities [25] - The company’s strategic location in Freiburg, away from Silicon Valley, has fostered a focused development environment, contributing to its unique corporate culture [23][24]