摘要
在国家的统一规划部署下,中国人工智能及其相关产业快速发展,在对产业与区域的渗透中表现出技术依赖、数据支撑、逐顶竞争和广泛渗透四个典型特征,呈现出技术应用“枝繁根浅”、区域渗透“城强乡弱”、产业发展“政引企从”、区域布局“东研西算”四大态势。在技术、数据、政策、市场等多方面因素的驱动下,人工智能与区域和产业发展出现高度融合的趋势,呈现范围经济主导下产业融合的产业发展模式和“小节点—大网络”的区域集聚模式。尽管中国人工智能产业已经初具规模,并在人工智能应用层领域保有较强的竞争力,但是在关键工艺和核心技术方面仍与发达国家有较为明显的差距,在对产业与区域渗透中面临人才、技术、安全、伦理、区域分化等多方面的挑战。需要加快人工智能人才队伍建设、推进核心技术攻关、构建安全防护体系、完善法律规范、协调智能资源区域分配,推动人工智能更深刻、广泛地赋能产业与区域发展,助力中国经济体系和治理能力现代化。
Under the unified national planning and deployment, China’s artificial intelligence(AI) and its related industries have developed rapidly, showing four typical characteristics: technology dependence, data dependence, race to top and extensive penetration. Based on these characteristics, four major trends have been formed, namely, "outstanding application technology but weak core technology" "well developed in cities while weak in villages" "guided by government" and "separated research/data center in eastern/western China". Driven by many factors such as technology, data, policy and market, AI has accelerated its penetration into regions and industries, forming a regional agglomeration mode of "small nodes-big networks" and an industrial development mode of integration of industries based on scope economy. Although China’s AI industry has begun to emerge and has strong competitiveness in the application layer, there is still a significant gap between China and developed countries in core technologies. What’s more, China’s AI industry faces challenges from talents, technology, safety, ethics and regional differentiation. It is necessary to construct AI talent team, tackle core technology, optimize security system, improve legal norms,coordinate the regional distribution of intelligent resources, so as to extent AI’s positive effects on industry and region and promote the modernization of China’s economic system and governance capacity.
作者
苏玺鉴
胡安俊
Su Xi-jian;Hu An-jun
出处
《经济学家》
CSSCI
北大核心
2023年第2期79-89,共11页
Economist
基金
国家社会科学基金青年项目“人工智能、资本深化、技能溢价与区域不平衡研究”(18CJL033)。
关键词
人工智能
产业与区域渗透
动力
模式
挑战
AI
Penetration of Industries and Regions
Dynamics
Models
Challenges
