Industry Investment Rating - The report does not explicitly provide an investment rating for the industry [1] Core Viewpoints - The report emphasizes the importance of high-quality computing power in driving the development of artificial intelligence (AI) and the digital economy [1] - It highlights the transition from quantity to quality in computing power development, focusing on efficiency, intelligence, and sustainability [1][13] - The report identifies key challenges such as insufficient computing power supply, low intelligence levels, and energy consumption issues [8][9][10] Development Status and Challenges Development Status - **Policy**: Governments worldwide are accelerating the construction of computing power competitiveness through policy support and strategic planning The US plans to invest over $2511 billion in AI-related fields, while China is promoting high-quality development of computing power infrastructure [5] - **Technology**: Generative AI technologies, such as ChatGPT, are rapidly advancing, driving the need for more powerful computing resources [7] - **Market**: Investments in computing power are increasing globally, with the US planning to invest $2800 billion in chip technology and China's "East Data West Computing" project attracting over 435 billion yuan in direct investment [8] - **Scale**: Global computing power is expanding, with intelligent computing power growing by 136% year-on-year, reaching 335 EFLOPS by the end of 2023 [12][13] - **Development Level**: China's computing power industry is shifting from scale expansion to quality improvement, focusing on application-driven development and green energy efficiency [13][14] Challenges - **Challenge 1**: Insufficient computing power supply and mismatched demand, with AI model training requiring massive computing resources [14] - **Challenge 2**: Low intelligence levels of computing power, making it difficult to meet diverse application scenarios [15] - **Challenge 3**: Energy consumption and carbon emissions are significant issues, with computing centers consuming 1500 billion kWh of electricity in 2023 [16] - **Challenge 4**: Rising demand for diverse computing power, but the level of universal accessibility and affordability remains low [17] - **Challenge 5**: Incomplete supply chains and underdeveloped ecosystems, with compatibility issues between different hardware and software platforms [18] - **Challenge 6**: Simple performance evaluation systems, lacking comprehensive assessment of actual computing power performance [19] Definition, Connotation, and Characteristics Definition - High-quality computing power is defined as advanced computing capability based on the latest AI theories, combined with algorithms and data, driving productivity and economic development [20][21] Connotation - **Technological Innovation**: High-quality computing power serves as the main engine for AI model training and application, reducing the threshold for AI adoption [23] - **Optimization of Production Factors**: It optimizes the allocation of data and resources, enhancing the efficiency of production, distribution, and consumption [24] - **Industrial Transformation**: It drives the integration of advanced technologies with traditional industries, fostering new business models and services [24] Characteristics - **High Computational Efficiency**: Focuses on both theoretical and practical performance, with an average computational efficiency of 118 GFLOPS/W in China [25][26] - **High Intelligence Efficiency**: Combines efficient AI processing with intelligent optimization capabilities [27] - **High Carbon Efficiency**: Aims to maximize computing output with minimal carbon emissions, emphasizing lifecycle carbon management [28] - **Accessibility**: Ensures computing power is widely available and affordable, supporting diverse application scenarios [29] - **Sustainability**: Emphasizes technical compatibility, complete supply chains, and open industrial ecosystems [30] - **Evaluability**: Requires a comprehensive evaluation system to reflect the actual performance of computing power [31] Development Path and Outlook Development Path - **System Design**: Focuses on improving computational efficiency through optimized system architecture and resource management [32] - **Collaborative Drive**: Enhances intelligence efficiency through the integration of computing power, algorithms, and data [33] - **Lifecycle Management**: Promotes carbon efficiency through green procurement, design, and operation [34] - **Infrastructure First**: Advances universal accessibility and affordability by building intelligent computing centers [36] - **Ecosystem Prosperity**: Encourages sustainable development through open and standardized industrial ecosystems [38][39] - **Diverse Evaluation**: Accelerates the standardization of computing power development through comprehensive evaluation systems [42][44] Outlook - **Market Environment**: China's vast application market and supportive policies provide a strong foundation for the computing power industry [46] - **Universal Accessibility**: The trend towards universal accessibility will unlock the potential of AI across various fields [47] - **Intelligent Upgrade**: The integration of AI and computing power will drive deep economic and social development [48] Evaluation System Exploration Background - The evaluation system for computing power is transitioning from hardware-focused assessments to comprehensive evaluations that consider application performance and lifecycle carbon management [51][52] Principles - The evaluation system should be policy-aligned, systematic, targeted, comprehensive, operable, and adaptable to future changes [58] Practice - The "Five-in-One" evaluation system assesses computing power quality across five dimensions: computational efficiency, intelligence efficiency, carbon efficiency, accessibility, and sustainability [60][70] Significance - The evaluation system standardizes and accelerates the high-quality development of the computing power industry, providing guidance for technological innovation and infrastructure construction [70] Application Recommendations - Accelerate the development of supporting standards and tools to ensure the effective implementation of the evaluation system [71] - Conduct evaluation tests in typical AI application scenarios and expand theoretical research to support the industry's high-quality development [73]

R 阿里云 aliyun.com Ed Stir 2024 RESEARCH REPORT CAICT 中国信通院 [ KNOWN ] [ UNKNOWN ] COVER GENERATED BY: WANXIANG 2.0 阿里云计算有限公司 中国信息通信研究院安全研究所 2024年9月 大模型安全研究报告 版权声明 LEGAL NOTICE 阿里云计算有限公司与中国信息通信研究院共同拥有本报 告的版权，并依法享有版权保护。任何个人或机构在转载、 摘录或以其他形式使用本报告的文字内容及观点时，必须 明确标注"资料来源：阿里云计算有限公司与中国信息通 信研究院"。对于任何未经授权的转载或使用行为，我们 将依法追究其法律责任。 RESEARCH REPORT FOUNDATION MODEL SAFETY 2 3 大模型安全研究报告 前 言 FORWORD 当前，由 ChatGPT 引发的全球大模型技术竞赛正推动人工智能由专用弱智能向通用强智能迈进， 这不仅标志着智能水平的显著提升，也预示着人机交互方式和应用研发模式的重大变革。大模型在 各行各业的广泛应用，为第四次工业革命的爆发提供了蓬勃动力和创新潜力。 然而 ...

Investment Rating - The report does not explicitly provide an investment rating for the telecommunications industry Core Insights - The telecommunications industry in China is experiencing a critical period of transformation and upgrade, driven by the need for digitalization and the integration of artificial intelligence (AI) technologies [3][8][10] - The report highlights that the growth of the telecommunications sector is facing cyclical slowdowns, necessitating the cultivation of new growth drivers [10][12] - AI is identified as a key engine for the transformation of the telecommunications industry, enhancing operational efficiency and enabling new business models [18][21][25] Summary by Sections 1. Telecommunications Industry Transformation - The telecommunications industry has made significant progress in digital transformation, but is now facing a bottleneck in growth, with revenue growth rates declining [8][10] - The industry has achieved a compound annual growth rate (CAGR) of 3.86% during the 13th Five-Year Plan and 7.30% since the 14th Five-Year Plan, with a net profit margin above 9.5% [9][10] - The integration of AI technologies is seen as a potential catalyst for the next phase of transformation, with the government emphasizing the importance of AI in driving new productive forces [3][15] 2. AI as a Driving Force - The report outlines three aspects of the AI-driven transformation in the telecommunications industry: AI as a core value addition, a key growth driver for core services, and an enhancer of operational efficiency [20][21] - AI technologies are expected to penetrate various operational aspects, leading to improved service delivery and operational management [19][21] - The report notes that the application of AI can create significant value, with estimates suggesting a potential value creation of $600-1000 billion for the telecommunications sector [17] 3. Global and Domestic AI Strategies - Global telecommunications companies are pursuing diverse AI strategies, with a focus on innovation and resource allocation to support AI integration [26][28] - Chinese telecommunications companies are systematically advancing their AI development, achieving notable progress in product-level and scenario-level applications [30][32] - The report emphasizes the need for a robust ecosystem to support AI development, including partnerships and collaborative efforts to enhance service delivery [30][31] 4. Challenges and Recommendations - The telecommunications industry faces several challenges in AI implementation, including the complexity of large model applications and the need for a robust infrastructure [4][10] - Recommendations for telecommunications companies include strategic planning for AI integration, enhancing core capabilities, and fostering an environment conducive to innovation [4][10][30] - The report suggests that the industry should focus on optimizing the policy environment for AI and telecommunications integration to facilitate growth [4][10]

Industry Overview - Data has become a new type of production factor, recognized globally as a strategic resource, with countries competing to harness its value [3] - China has taken a leading role in the global data element market by pioneering data trading platforms and data asset accounting [3] - The integration of new technologies, models, and infrastructure is driving the development of the data element sector, with AI playing a significant role in accelerating data value release [3] - Data assetization has introduced new models like "data credit" and "data trusts," expanding the application scenarios of data elements [3] - Data infrastructure, including advanced storage, is critical for the lifecycle management of data, ensuring its availability, flow, and security [3][4] Data Elements and AI Synergy - AI accelerates the transformation of cold data into warm and hot data, with large models like GPT requiring massive datasets for training [7][18] - AI-driven applications generate vast amounts of hot data, increasing the demand for high-frequency data storage solutions [19] - High-quality datasets are crucial for AI model training, with dataset size and quality directly impacting model performance [21] - Advanced storage technologies, such as all-flash arrays, are essential for supporting AI's high-performance data processing needs [19][24] Data Assetization and Storage - Data assetization has led to significant breakthroughs in data asset valuation and registration, with China pioneering data asset accounting [25][27] - Data asset circulation and trading have increased the volume of data copies, necessitating robust storage solutions [31] - Advanced storage facilities act as secure "vaults" for data assets, ensuring their safety, reliability, and scalability [32][33] - The integration of storage with other data infrastructure components, such as networks and computing, is vital for efficient data management [37] Data Infrastructure Development - Data infrastructure, including storage, is a key enabler of data element utilization, supporting data collection, processing, and circulation [34][35] - Storage facilities are one of the "six foundations" of data infrastructure, alongside networks, computing, data collection, data circulation, and data security [35][37] - The construction of data infrastructure is accelerating, with advanced storage capacity expected to reach 30% of total storage by 2025 [12][40] - Regional storage centers, such as those in Guizhou and Chongqing, are emerging as models for integrating storage and computing, enhancing data value realization [42][43] Recommendations for Future Development - Promote the construction of high-quality datasets and advance the application of AI-driven storage technologies [45] - Improve the data asset valuation system by incorporating storage security and risk management metrics [46] - Accelerate the construction of advanced storage facilities and optimize the layout of data infrastructure to support regional and industrial needs [48]

| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |-------|------------------|---------------------|-------|-------|-------|-------|-------|------------------|-------|---------------| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | CAICT 中国信通院 | 亚控科技 WellinTech | | | | | | (ap 展きつまみず | | JINCHUAN 密川 | 工业设备网联化技术与实践白皮书 前高 自工信部印发《工业互联网创新发展三年行动计划 ( 2018-2020 ) 》以来，经过近几年卓有成效的发展，我国 工业互联网体系已初步形成,平台应用蓬勃发展,生态繁荣, 对工业数据上通下达，工业网络开放互通不断提出新的要求。 由于传统工业网络先天存在的互通难、开放性差等问题，如何 实现I ...

权威发布：2024 年 8 月国内市场手机出货量 2404.7 万部， 其中 5G 手机占比 82.1%。 一、国内手机市场总体情况 2024 年 8 月，国内市场手机出货量 2404.7 万部，同比增长 26.7%，其 中，5G 手机 1975.4 万部，同比增长 26.3%，占同期手机出货量的 82.1%。 2024 年 1-8 月，国内市场手机出货量 1.95 亿部，同比增长 16.6%，其中， 5G 手机 1.65 亿部，同比增长 23.9%，占同期手机出货量的 84.5%。 239 339 513 535 579 473 338 327 445 266 408 395 555172 358 377 475 274 354 418 1646 1770 1734 1330 2017 1732 1506 1564 2872 2644 2709 2420 2617 1253 1774 2023 2553 2213 2065 1975 87%83% 77% 71% 77%78%81%82%86% 91%87%86% 82%88%83%84%84% 89%85%82% 0% 10% 20% 30% 40% 50 ...

CAICT 中国信通院 ［幸运 트题报告 ■ | --- | --- | --- | --- | --- | --- | --- | |-------|-----------|-------|-------|-------|--------------|-------| | | | | | | | | | | | | | | | | | | 全球 5G | | | | 标准必要专利 | | 中国信息通信研究院知识产权与创新发展中心 2024年9月 | --- | --- | |-------------------------------------------------------------------------------------------------------|-------| | 版权声明 | | | | | | 本报告版权属于中国信息通信研究院，并受法律保护。 转载、摘编或利用其它方式使用本报告文字或者观点的，应 | | | 注明"来源：中国信息通信研究院"。违反上述声明者，本 | | 前 言 自 2021 年 3GPP 立项通过 5G 标准 Rel-18 版本的首批项目以来， 5G ...

CAICT 中国信通院 | --- | --- | --- | |-------|-------|--------------------| | | | | | | | | | | | 算力时代全光网架构 | 中国信息通信研究院技术与标准研究所 2024年9月 | --- | --- | |------------------------------------------------------|------------------------| | 版权声明 | | | | | | 本报告版权属于中国信息通信研究院，并受法律保护。 | | | 转载、摘编或利用其它方式使用本报告文字或者观点的，应 | | | 注明 " 来源：中国信息通信研究院 " | 。违反上述声明者，本院 | | 将追究其相关法律责任。 | | 前 言 随着行业数字化转型的深入及人工智能（AI）大模型技术的发展， AI 在千行百业中的融合应用日益丰富，企业及家庭用户对算力资源 的需求快速增长，数字经济已进入以人工智能+算力为核心生产力要 素的算力时代。全光网作为运送和支撑调度算力资源的关键底座，其 重要性日益凸显。算力时代下，各类算力应 ...

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Digital trade is increasingly recognized as a vital component of global trade, with its share in total exports rising to 13.8% in 2023, up 1.4 percentage points from 2022 [11] - The global digital trade rules are evolving, with "pure digital" agreements leading the way, reflecting a shift towards more flexible and efficient negotiation frameworks [4][25] - Emerging economies are beginning to play a significant role in shaping digital trade rules, with countries like Singapore, China, and India actively participating in the development of new regulations [4][39] Summary by Sections 1. New Situations Facing Digital Trade Rule Formulation - The importance of digital trade is growing, with geographical shifts in trade patterns, particularly towards Asia [10][12] - New digital technologies are driving changes in international trade methods, although disparities in global development remain pronounced [15] - The regulatory environment for digital trade is becoming increasingly stringent, impacting the pace of digital trade growth [19] 2. Overall Progress of Digital Trade Rules - A new multi-layered structure of global digital trade rules is emerging, characterized by multilateral, bilateral, and regional agreements [24][25] - There is a noticeable divergence in the progress of digital trade facilitation and liberalization topics, with consumer trust rules advancing more rapidly [34][35] - Emerging economies are starting to play a crucial role in rule-making, with a significant increase in the number of digital trade agreements signed in Asia [37][38] 3. Latest Trends in Key Rules and Issues - New technology rules are beginning to emerge, enhancing innovation cooperation and supply chain resilience [55] - Artificial intelligence has become a focal point, prompting discussions on new and existing regulatory frameworks [59] - Rules governing cross-border data flow are entering a new adjustment phase, with ongoing evolution in rule templates [65] 4. Outlook for Digital Trade Rules - The importance of digital inclusivity is expected to rise, with a focus on bridging the digital divide and enhancing access to digital technologies [74] - Environmental sustainability in the digital economy is becoming a new focal point, with international organizations emphasizing the need for sustainable practices [77] - The development of rules related to artificial intelligence and emerging technologies is gaining momentum, with various countries actively pursuing regulatory frameworks [79]

Industry Overview - The 2024 Government Work Report introduced the "AI+" strategic action, aiming to empower various industries with AI, particularly through large models that drive intelligent transformation in software engineering [3] - Intelligent software development tools, leveraging large models, significantly reduce technical barriers for developers and enhance development efficiency and quality [3] - Despite advancements, challenges remain in areas such as model selection, tool integration, and scenario-specific implementation [3] Intelligent Development Evolution - Software engineering has evolved through three stages: Software Engineering 1.0 (structured methods), 2.0 (agile development), and 3.0 (intelligent software engineering driven by large models) [6][7][8] - Software Engineering 3.0 focuses on AI-driven tools that enhance the entire software lifecycle, including development, testing, and operations, with core characteristics like intelligence, data-driven processes, and adaptability [10][11][12] Market and Tool Landscape - The intelligent development tool market is rapidly growing, with GitHub Copilot leading with 1.8 million paid subscribers and a 64.5% market share as of April 2024 [14] - Domestic tools, such as those from Huawei, Alibaba, and Baidu, are also emerging, with over 40 tools available, though performance and user experience vary [14] - Tools are increasingly adopted across industries, including tech, finance, telecom, and manufacturing, with significant efficiency gains reported [14][15] Core Capabilities of Intelligent Development - Key capabilities include code generation, code completion, unit test generation, code conversion, code explanation, and code inspection [32][33][37][42][46] - These capabilities aim to improve coding efficiency, code quality, and developer productivity, with tools like GitHub Copilot showing a 55% increase in coding speed and 46% more code written [15][16] Challenges in Intelligent Development - Organizations face challenges in cultural transformation, talent acquisition, and integrating AI tools with existing workflows [19][20] - Technical challenges include model selection, tool integration, and ensuring security across data, models, and tools [20][21] Case Studies - **Cloud Services**: A major cloud service provider implemented intelligent development tools, achieving a 57% unit test coverage rate and generating over 2.2 million lines of AI-generated code [81][82] - **Software Services**: A software service company developed an AI-powered platform, reducing project communication time from weeks to days and improving development efficiency by 30% [84][85] - **Power Industry**: A state grid company used AI tools to improve code quality and reduce project delivery time by 40%, with code quality scores exceeding 90% [87][89] - **Finance**: A state-owned bank leveraged AI tools to convert legacy Flex code to React, achieving a 40% accuracy rate and significantly improving unit test coverage [91][93] - **Manufacturing**: A leading home appliance manufacturer adopted AI tools to enhance code readability and development efficiency, addressing challenges in code maintenance and innovation [95]