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大模型的逻辑增强与人工智能驱动的行业创新

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本文探讨通过大模型等人工智能技术推动制造业创新发展的重大意义,以及面临的挑战和机遇.分析大模型技术体系的构成,阐释其工程学基本概念,介绍关联度预测的一种科学解释——类LC 理论.基于该理论,分析大模型的奇异表现的深层原因与重要后果,形成对大模型技术更全面、更深入的理解.在此基础上,梳理制造业细分行业对人工智能技术的三项基本要求——专业性、逻辑可靠性和知识能力;分析大模型技术与人工智能强力法技术相集成的核心难点;提出构建行业人工智能系统的封闭化方案,由该方案构建的行业人工智能系统满足三项基本要求,并具有可解释性和可控性.最后,简要讨论制造业高质量发展中从"新技术的行业应用"向"新技术驱动的行业创新"转变的新趋势.
Logical Enhancement of Large Language Models and Industrial Innovation Driven by Artificial Intelligence Technologies
The significance,challenges and opportunities of driving innovative development in manufacturing industry with artificial intelligence technologies including large language models were explored.The technological system of large language models was analyzed,its basic engineering concepts were clarified,and the pan-LC theory—a scientific explanation for next token prediction—was presented.Based on the theory,the causes and consequences of some weird behaviors of large language models were explained,giving a more comprehensive and in-depth understanding of large language models.On the basis,three main requirements for artificial intelligence technologies in manufacturing industry were sorted out,and the core difficulties in the integration of large language models and the artificial intelligence brute-force technology were revealed.A closedness-based solution is proposed for the construction of artificial intelligence systems in manufacturing sectors,such that these systems satisfy the main requirements of specialization,logical validity and knowledge ability,as well as explainability and controllability.Finally,the trend of shifting from"industrial application of new technologies"to"sector innovation driven by new technologies"in the high-quality development of manufacturing industry is discussed briefly.

large language modelsartificial intelligenceexplainabilitymanufacturing industrysector-oriented artificial intelligencesector innovation

陈小平

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中国科学技术大学计算机学院,合肥 230026

广东省科学院智能制造所,广州 510070

大模型 人工智能 可解释性 制造业 行业人工智能 行业创新

2024

技术经济
中国技术经济学会

技术经济

CSTPCDCHSSCD北大核心
影响因子:0.896
ISSN:1002-980X
年,卷(期):2024.43(12)