人工智能会怎样全面赋能高质量发展？

Group 1: Core Insights of the "15th Five-Year Plan" - The "15th Five-Year Plan" emphasizes "high-quality development" and adjusts growth targets to "maintain within a reasonable range," highlighting a shift towards quality and efficiency in economic growth [1] - The plan introduces the concept of "new quality productivity" and aims to build a modern industrial system centered on advanced manufacturing, indicating a strategic upgrade in the national technology strategy [1] - The focus on artificial intelligence (AI) has significantly increased, with the term mentioned 8 times in the plan, reflecting its importance as a key strategic engine for national development [2] Group 2: AI Application and Industry Impact - The shift from focusing on AI technology to promoting AI applications across various industries marks a fundamental change in national strategy [3] - AI applications are expected to drive productivity and improve living standards, with significant potential already demonstrated in various sectors [9] - The "AI+" initiative outlines six key areas for AI application: scientific technology, industrial development, consumption stimulation, public welfare, governance capability, and global cooperation [4] Group 3: AI in Specific Sectors - In scientific research, AI is revolutionizing drug development by significantly shortening the time required for identifying disease-related biological targets and enhancing the efficiency of molecular design [5] - In industrial development, AI is integrated into production and management processes, improving product quality and operational efficiency, with reports indicating procurement efficiency improvements of over 30% [6] - In the consumer sector, AI enhances user experience through personalized recommendations and intelligent customer service, leading to increased sales conversion rates [7] Group 4: Challenges and Future Directions - Despite the initial successes of AI applications, challenges remain in expanding these applications from isolated breakthroughs to widespread adoption across industries [13] - Data quality and accessibility issues, including the prevalence of "data silos," hinder the training of AI models and the effectiveness of AI applications [13][14] - The need for improved algorithms and models, as well as addressing the challenges of computing power, particularly in the context of high-performance chips, is critical for the future of AI development in China [15]