人工智能+行动 来了

Core Viewpoint - The article emphasizes the implementation of the "Artificial Intelligence +" initiative across six key areas to foster new productive forces and support China's modernization efforts, with specific goals set for 2027, 2030, and 2035 [1]. Group 1: Six Key Areas of "Artificial Intelligence +" - "Artificial Intelligence + Science and Technology": Accelerate scientific discovery processes and drive technological innovation [2]. - "Artificial Intelligence + Industry Development": Cultivate new models and business formats, promoting intelligent development across industrial and agricultural sectors [2]. - "Artificial Intelligence + Consumer Quality": Expand new service consumption scenarios and foster new product consumption formats, promoting intelligent terminal connectivity [2]. - "Artificial Intelligence + Welfare": Create intelligent work methods and more effective learning approaches to enhance quality of life [2]. - "Artificial Intelligence + Governance Capability": Establish a new paradigm of human-machine coexistence in social governance and create a multi-governance framework for safety [2]. - "Artificial Intelligence + Global Cooperation": Promote inclusive sharing of artificial intelligence and build a global governance system for AI [3]. Group 2: Fundamental Support Capabilities - Enhancing foundational model capabilities, innovating data supply, strengthening intelligent computing power coordination, and optimizing the application development environment are essential [4]. - Promoting a thriving open-source ecosystem, strengthening talent development, ensuring policy and regulatory support, and improving safety capabilities are also critical [4]. Group 3: Participation and Opportunities - The "Artificial Intelligence +" initiative allows for broad participation, with opportunities in ecosystems that seamlessly integrate business processes and quickly cultivate user habits to form network effects [5]. - The value of vertical data is significant, especially when high-quality user data continuously feeds back to improve model capabilities, creating a snowball effect [5]. - Talent development is crucial for the success of the initiative [6].