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BEIJING, Aug. 29 (Xinhua) -- China aims to achieve breakthroughs in the application of artificial intelligence (AI) in key sectors by 2027, an official said on Friday when outlining the country's latest efforts to accelerate AI development.
At a press briefing, Huo Fupeng, director of the innovation-driven development center under the National Development and Reform Commission (NDRC), described the coming one to two years as a "critical window" for AI deployment. He urged the mobilization of resources across society to drive progress in six priority areas: science and technology, industry, consumption, public welfare, governance, and global cooperation.
Huo's remarks came as China published a set of guidelines earlier this week on deeply implementing the "AI Plus" initiative, which lays out a systemic approach to strengthen AI supportive infrastructure and accelerate the integration of AI technology across economic and social domains.
The new policy document is a key step in cultivating new quality productive forces and a necessary move for promoting the digital economy's transformation into a smart economy and an intelligent society, according to Huo.
The document outlines three milestone goals. By 2027, new-generation intelligent terminals and agents are expected to reach a penetration rate of more than 70 percent, with the core industries of the intelligent economy expanding rapidly. By 2030, that penetration rate should surpass 90 percent, enabling AI to become a significant economic driver. By 2035, China aims to fully enter a new phase of an intelligent economy and intelligent social development.
The NDRC emphasized that the latest move is only the first step, which will be followed by a mix of policy tools, financial support and institutional innovation.
Zhang Kailin, deputy director of the NDRC's department of innovation and high-tech development, said the NDRC will introduce more detailed sector-specific plans and clearer policy guidance, while expediting the development of standards to promote data sharing, model interoperability and coordinated growth of AI systems.
He also stressed the importance of government-led investment to support AI innovation, including optimizing the distribution of computing resources to reduce R&D costs, building shared technology platforms, expanding the application in critical scenarios, and promoting the use of AI-enabled products among the public.
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