大/小/微模型赋能先进制造： 实 践与思考 Large/Small/Miero Al Models for Manufacturing (Al4M): App licatiansandlnsights 宋学官 大连理工大学机 械工程学院 2 一、 Al4M的背景意义 二、 Al4M的基础知识 三、 Al4M的研究进展 四、 Al4M的案例展示 五、 Al4M的瓶颈所在 六、 Al4M的科学问题 七、 Al4M的发展方向 八、 思 考与总结 汇 报 提纲 一、Al4M的背景意义 二、AI4M的基础知识 三、AJ4M的研究进展 四、AI4M的案例溪示 五、A14M的范颈所在 六、AJ4M的科学问题 七、AI4M的发展方向 八、思考与总结 Al4M的背景意义 5 口先进制造是指采用高新技术和先进设备来改善制造业过程和生产效率的统称，是 衡量一个国家科技发展水平的重要标志，关乎国民经济发展和国防安全建设。 Al4M的背景意义 6 口《中国制造2025》:加快推进制造业转型升级，到2035年整体达到世界制造强国中等水平 □2022年10月，美国发布《国家先进制造业战略》,先进制造业是美国经济和国家安全引擎 美国：Ind ...

Group 1 - The core viewpoint is that large language models (LLMs) are transforming vast amounts of unstructured text into quantifiable Alpha factors, fundamentally expanding the information boundaries of traditional investment research [1] - AI technology is deeply reconstructing asset allocation theory and practice across three levels: information foundation, decision-making mechanisms, and system architecture [1] - LLMs enhance the understanding of financial reports and policies, while deep reinforcement learning (DRL) shifts decision frameworks from static optimization to dynamic adaptability [1] Group 2 - The practical application of AI investment research systems relies on a modular collaboration mechanism rather than the performance of a single model [2] - The architecture of AI investment systems, as demonstrated by BlackRock's AlphaAgents, involves model division of labor, enhancing decision robustness and interpretability [2] - This modular approach creates a replicable technology stack from signal generation to portfolio execution, laying a solid foundation for building practical investment agents [2] Group 3 - Leading institutions are elevating competition to an "AI-native" strategy, focusing on building proprietary, trustworthy AI core technology stacks capable of managing complex systems [3] - JPMorgan's strategy emphasizes proprietary technology layout across three pillars: trustworthy AI and foundational models, simulation and automated decision-making, and alternative data [3] - This approach creates complex barriers that are difficult for competitors to overcome in the short term [3] Group 4 - For domestic asset management institutions, the path to breakthrough lies in strategic restructuring and organizational transformation, focusing on differentiated and targeted technology implementation [4] - Institutions should prioritize the practical and efficient "human-machine collaboration" system, leveraging LLMs to explore unique policy and text Alpha in the A-share market [4] - It is essential to break down departmental barriers and cultivate cross-disciplinary teams that integrate investment and technology, embedding risk management throughout the AI governance lifecycle [4]