"人工智能作为一种通用赋能引擎，已打破学科之间、理论与实验之间、科研与产业之间的传统边界， 正在重构知识生产与应用的全链条。"丁奎岭认为，学科边界是人为划定的，AI能提升跨界融合能力， 催生新兴交叉学科；理论与实验存在边界，是因传统理论计算工具无法解释现实现象，AI为破解这一 难题提供了途径；科研与产业存在边界，是因科技成果转化效率难以满足产业需求，AI可提升科研创 新效率。 对于如何更好地拥抱与发展人工智能，丁奎岭介绍，上海交大已系统规划并实施"AI十条"发展战略，重 点聚焦人工智能学科跃升、AI专项行动、智慧文科提升等十大领域，全方位推进AI赋能教育教学、科 技研发、管理服务等各项工作，推动其在科学研究、工程技术突破、医学创新等领域实现深度应用。 此外，丁奎岭还介绍，学校打造的智算平台"致远一号"，是目前全国高校中规模最大的国产智算算力基 础设施，已于去年12月正式落成启用。该平台可高效支撑千亿参数大模型的训练任务，为全校师生提供 全链条的AI应用服务。同时，学校组建了专业的计算服务团队，积极与企业及校内各用户单位合作， 推动人工智能全面融入人才培养、科学研究及校园治理等工作中。（记者李昉） 近日，中国教 ...

Core Insights - The emergence of generative AI marks a shift from digital technology as merely a "connecting tool" to a core component in judgment, decision-making, and action within organizations [3] - Companies now face the critical question of how to effectively integrate AI to create real value rather than merely adopting it as a trend [3][12] Group 1: Industry Transformation - Digital transformation has historically focused on "connection," with a race to establish networks and data integration across various sectors [2] - The introduction of AI challenges traditional notions of efficiency, emphasizing the need for stability, risk control, and long-term certainty in complex systems [5][13] - AI's true value is becoming evident in industries with low penetration and minimal tolerance for error, such as manufacturing and agriculture [6][10] Group 2: AI in Manufacturing - In manufacturing, the example of Jianghuai Zunji Super Factory illustrates how AI can manage increasing complexity and maintain quality standards through digital twin technology and flexible production [8][9] - AI acts as a stabilizer in manufacturing systems, helping to ensure consistent decision-making amidst rising complexity [9] Group 3: AI in Agriculture - In agriculture, COFCO's smart farming initiatives address systemic risks by transforming traditional practices into a continuously monitored and predictable system [10][12] - AI enables proactive risk management, shifting from reactive to preventive measures in large-scale farming operations [12] Group 4: Challenges in AI Integration - The integration of AI into industries is not merely about technological advancement but requires clear pathways for implementation [15][23] - High-complexity sectors like healthcare and education face unique challenges that necessitate tailored approaches rather than generic solutions [16][21] Group 5: Pathways to Successful AI Deployment - Huawei's ACT framework emphasizes assessing high-value scenarios, calibrating industry data, and deploying AI at scale, positioning AI as a structural variable rather than an add-on [24][25] - This cautious approach is essential in complex industries to ensure that AI is effectively embedded within existing industry logic [25] Group 6: Infrastructure and Ecosystem Development - The acceleration of industry digitalization is linked to advancements in foundational technologies such as wireless communication and data networks [27][28] - Huawei's focus on enhancing computing, networking, and storage capabilities aims to create a robust infrastructure that supports widespread AI adoption [29][30] Group 7: Bridging the Gap for SMEs - Huawei's solutions for small and medium enterprises (SMEs) aim to lower the barriers to AI adoption by providing pre-integrated, validated solutions that reduce the complexity and cost of implementation [30][31] - This approach transforms AI from a high-barrier investment into a replicable and affordable capability for various industries [31][32] Group 8: Long-term Vision for AI Integration - The transition to AI as a foundational capability requires a continuous and methodical approach, emphasizing the importance of replicable certainty in an uncertain environment [32][33] - Huawei's role in facilitating this transition reflects a broader responsibility to guide industries through the complexities of AI integration [33]