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The NDRC's framework for the 15th Five-Year Plan puts AI adoption in a "point, line, surface" structure: scenario-specific applications in hazardous work, industry-level pilot bases and standards, and institutional policy. The emphasis on practical deployment and lowering adoption barriers is more concrete than many national AI strategies, but the article states intentions rather than funding, timelines, or measurable targets. Readers should watch whether the promised classification guidelines and sector implementation plans materialize with specifics.
Surse care acoperă36Kr, LessWrong, Google Cloud Blog, SiliconANGLE
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NDRC: Improve AI-Plus Policy System, Refine Implementation Plans for Key Industries

36Kr1 min de cititArticolul și perspectiva sunt în engleză.

Wang Ruomeng, deputy director of the Department of High-Tech and Innovation Development at the National Development and Reform Commission, said that during the 15th Five-Year Plan period, China will continue to leverage its complete industrial system, rich application scenarios, and strong engineering capabilities, focus on using practical applications to drive technological innovation, and advance the AI-Plus initiative through coordinated efforts at the point, line, and surface levels. At the "point" level, applications will be developed by scenario, with priority given to dangerous, dirty, tiring, tedious, and difficult scenarios such as mining, high-altitude work, and emergency rescue, using AI to solve industry pain points and establish more application models and benchmarks. At the "line" level, efforts will focus on industries, continuing to promote the construction of national AI application pilot bases in manufacturing, energy, transportation, and healthcare; addressing common issues in industry applications; coordinating the supply of computing power, models, data, and other resources; accelerating standard-setting and application promotion; and lowering the barriers to AI adoption in industries. At the "surface" level, the focus will be on institutions, improving the AI-Plus policy system, refining implementation plans for key industries, and formulating classification guidelines for application scenarios to provide stronger institutional support for deeper AI-Plus development. (Securities Times)

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