解析“人工智能+”行动蓝图，未来十年这些重点领域将获益

Core Viewpoint - The "Artificial Intelligence (+)" action plan outlines a ten-year blueprint for integrating AI into six key areas, aiming for significant advancements by 2027, 2030, and 2035, ultimately transitioning China into a smart economy and society [1][9]. Group 1: Key Areas of Focus - The six key areas for AI integration include scientific technology, industrial development, consumption enhancement, public welfare, governance capability, and global cooperation [2][4]. - In scientific technology, the focus is on accelerating AI-driven research paradigms and major scientific discoveries [2][5]. - For industrial development, the plan emphasizes cultivating new models and technologies, promoting smart manufacturing, and advancing agricultural digitization [4][5]. - Consumption enhancement aims to improve service quality across various sectors, including entertainment, e-commerce, and healthcare, through AI-driven innovations [4][5]. - Public welfare initiatives will leverage AI to create smarter work environments and improve services in education, healthcare, and elderly care [2][4]. - Governance capability will focus on smart city infrastructure and enhancing public service efficiency through AI [4][6]. - Global cooperation will promote AI as a public good, fostering an open ecosystem for AI capability building [4][6]. Group 2: Implementation Goals - By 2027, the goal is to achieve over 70% penetration of new intelligent terminals and AI applications across the six key areas, with a noticeable impact on public governance [7][9]. - By 2030, the target is to exceed 90% penetration of AI applications, positioning the smart economy as a crucial growth driver for China's development [9]. - By 2035, the aim is to fully transition into a smart economy and society, providing robust support for achieving socialist modernization [9]. Group 3: Challenges and Recommendations - Current challenges include uneven application of new intelligent terminals and AI, particularly among small and medium enterprises and rural areas, leading to a fragmented distribution of benefits [10][11]. - Recommendations include building reusable AI components, improving data governance, and establishing standards for interoperability to address existing bottlenecks [11].