中国互联网协会副秘书长戴炜：AI赋能新型工业化面临三重瓶颈

Core Insights - The integration of AI technology is reshaping industrial production processes and creating new value models, driving high-quality development in manufacturing, but there are still significant bottlenecks to overcome [1][2] Group 1: Bottlenecks in AI and Industrial Integration - The first bottleneck is a weak foundation, characterized by fragmented and siloed traditional industries, leading to data islands that hinder cross-domain collaboration [1] - The second bottleneck is a technological gap, where the high real-time, high-reliability, and strong noise characteristics of industrial scenarios make it difficult for AI models to adapt and generalize, failing to meet flexible production needs [1] - The third bottleneck is governance lag, with new issues such as cross-border industrial data flow, algorithmic black-box decision-making, and human-machine responsibility boundaries raising higher demands for safety, ethics, and regulatory frameworks [1] Group 2: Solutions to Overcome Challenges - To address these challenges, a more open attitude, systematic thinking, and pragmatic actions are required, focusing on technology leadership to solidify the foundation of industrial intelligence [2] - Emphasis should be placed on scene innovation to unleash new momentum for industrial upgrades, promoting the implementation of smart factories and flexible production lines to enhance production efficiency and optimize energy consumption [2] - Strengthening safety controls is essential to establish a solid foundation for industrial development, balancing innovation with risk prevention to safeguard against potential threats from AI integration [2] Group 3: Collaborative Ecosystem Development - Building a collaborative ecosystem is crucial, which involves enhancing top-level design, improving industrial data rights, and establishing AI ethical norms [3] - Accelerating the intelligent transformation of enterprises and cultivating talent that understands both industrial and AI domains is necessary for effective integration [3] - The ultimate goal is to create a virtuous cycle of "technology research and development - scene validation - scale replication - value feedback" empowered by AI in new industrialization [3]