Group 1 - The core viewpoint emphasizes the importance of high-quality data sets in the development of artificial intelligence, with a focus on their construction and promotion across various sectors [2] - As of June 2023, over 35,000 high-quality data sets have been established in China, totaling more than 400PB, which is approximately 140 times the digital resources of the National Library of China [2] - The demand for data trading has surged, with a cumulative transaction value of nearly 4 billion yuan for high-quality data sets by June 2023, and the total scale of listed high-quality data sets reaching 246PB [2] Group 2 - The development of high-quality data sets relies on the support of the data annotation industry, with the National Data Bureau establishing seven data annotation bases in cities like Chengdu, Shenyang, and Hefei [3] - The proportion of Chinese data used in model training has exceeded 60% in most domestic models, with some models reaching 80% [3] - Future plans include systematic efforts to advance high-quality data set construction, focusing on key areas such as embodied intelligence, low-altitude economy, and biomanufacturing [3]

Core Insights - The establishment of trusted data spaces aims to resolve data circulation challenges and enhance the value of data elements through distributed architecture and consensus mechanisms [2][3] - The initiative is expected to create new opportunities for the data industry and significantly impact various sectors, driving high-quality economic development [2] - By 2028, the goal is to establish over 100 trusted data spaces, achieving breakthroughs in operations, technology, ecology, standards, and security [1] Group 1: Trusted Data Space Development - The National Data Bureau has announced a pilot list of 63 enterprises for the 2025 trusted data space innovation development project, categorized into urban, industry, and enterprise directions [1] - The project aims to cultivate a rich resource environment, innovative applications, and a prosperous ecosystem for trusted data spaces over two years [1] - Trusted data spaces are seen as essential infrastructure for data resource sharing and are crucial for building a unified national data market [1] Group 2: Industry Impact - Trusted data spaces are expected to facilitate efficient connections and cross-domain sharing of industrial data resources in key sectors such as equipment, new energy vehicles, and energy [2] - In the financial sector, trusted data spaces will enhance the sharing and deep mining of customer credit data and market risk data, improving risk assessment models [2] - The integration of government data, supply chain information, and consumer behavior in credit risk scoring models can significantly improve risk identification accuracy [2] Group 3: Financial Ecosystem Collaboration - The construction of trusted data spaces promotes collaboration and value co-creation within the financial ecosystem, involving banks, regulatory bodies, technology companies, and core enterprises [3] - Financial modeling, as a key tool in this ecosystem, provides precise decision support and risk management services by integrating diverse data resources and technologies [3] - The application of artificial intelligence and cross-industry data fusion is becoming a trend, expanding the use of financial modeling beyond traditional finance professionals to fields like accounting and finance [3]