The Zhitong Finance App learned that on August 28, the National Development and Reform Commission held an August press conference. Li Chao, deputy director of the Policy Research Office of the National Development and Reform Commission, said that in the next step, the National Development and Reform Commission will focus on practical use and effective implementation, using intelligent training sites and application pilot bases as a starting point, so that robots can iterate technology in real scenarios and form a closed loop of applications around real needs. On the one hand, coordinate the layout of an intelligent training ground to strengthen the supply of elements such as data, models, and standards. In terms of data, build a high-quality real machine data collection system, improve the quality and scale of the supply of personalized intelligent data, and solve the “data hunger” problem of physical intelligence training. In terms of models, we rely on high-quality data and real scenarios to support model companies to explore multiple technology routes, encourage innovation in cutting-edge directions such as visual-language-motion models and world models, and accelerate technology convergence and application implementation. In terms of standards, promote the construction of a specific intelligent technology standard system, reduce the cost of adapting models across entities through uniform standards, and promote technology co-construction and sharing.

The Zhitong Finance App learned that on August 28, the National Development and Reform Commission held an August press conference. Li Chao, deputy director of the Policy Research Office of the National Development and Reform Commission, said that in the next step, the National Development and Reform Commission will focus on practical use and effective implementation, using intelligent training sites and application pilot bases as a starting point, so that robots can iterate technology in real scenarios and form a closed loop of applications around real needs. On the one hand, coordinate the layout of an intelligent training ground to strengthen the supply of elements such as data, models, and standards. In terms of data, build a high-quality real machine data collection system, improve the quality and scale of the supply of personalized intelligent data, and solve the “data hunger” problem of physical intelligence training. In terms of models, we rely on high-quality data and real scenarios to support model companies to explore multiple technology routes, encourage innovation in cutting-edge directions such as visual-language-motion models and world models, and accelerate technology convergence and application implementation. In terms of standards, promote the construction of a specific intelligent technology standard system, reduce the cost of adapting models across entities through uniform standards, and promote technology co-construction and sharing.