SPECIAL TOPIC: AI-Enabled Information Resource Management

From General AI to Intelligence AI: Background, Framework, and Key Technologies

  • Li Guangjian ,
  • Wu Xuan ,
  • Pan Jiali
Expand
  • Department of Information Management, Peking University, Beijing 100871
Li Guangjian, Professor, PhD, Doctoral Supervisor; Wu Xuan, Doctoral Candidate, Corresponding Author, E-mail: wuxuan1999@stu.pku.edu.cn; Pan Jiali, Doctoral Candidate.

Received date: 2025-03-24

  Revised date: 2025-05-08

  Online published: 2025-09-29

Supported by

This work is supported by the major project of the National Social Science Fund of China, titled “Research on the Application of Key Technologies of Intelligence in the Context of Digital Transformation”(Grant No. 23&ZD228).

Abstract

[Purpose/Significance] This study conducts an in-depth discussion of the key technologies of AI-enabled intelligence work, with a view to providing new theoretical basis and methodological guidance for the intelligent transformation of intelligence work in the era of digital intelligence. [Method/Process] This study discussed the main challenges facing intelligence work in the digital age, explained the necessity of developing “AI for intelligence” from generalized AI, and put forward its technical framework based on intelligence agents, as well as the key technologies in the demand-awareness layer, intelligence-awareness layer, intelligence-analysis layer, and intelligence-service layer covered by the framework. [Result/Conclusion] User demand simulation technology, reliability screening technology, thought chain technology, and scenario-based service technology, can be used to solve the challenges faced by intelligence work in the era of digital intelligence, such as the specialization of user demands, the multimodality and credibility of information, the computationalization of intelligence work and scenario-based intelligence services, and they enhance the quality and effectiveness of intelligence work.

Cite this article

Li Guangjian , Wu Xuan , Pan Jiali . From General AI to Intelligence AI: Background, Framework, and Key Technologies[J]. Library and Information Service, 2025 , 69(20) : 3 -15 . DOI: 10.13266/j.issn.0252-3116.2025.20.001

References

[1] 刘细文, 付芸, 韦华楠. 情报智能体——面向“十五五”的科技情报工作新范式[J]. 农业图书情报学报, 2024, 36(12): 20-34. (LIU X W, FU Y, WEI H N. DIS agent: new paradigm of S & T documentation and information service for the fifteenth five-year plan[J]. Journal of library and information science in agriculture, 2024, 36(12): 20-34.)
[2] 刘细文. 情报学范式变革与数据驱动型情报工作发展趋势[J]. 图书情报工作, 2021, 65(1): 4-11. (LIU X W. Paradigm transformation of library and information science and trends of data-driven information services[J]. Library and information service, 2021, 65(1): 4-11.)
[3] 王晋, 李辉, 尹明理, 等. 科技情报工作的目标定位、核心与时代工作重点[J]. 情报理论与实践, 2018, 41(4): 13-15, 12. (WANG J, LI H, YIN M L, et al. Scientific and technical intelligence service: objective and orientation, core, and focus of the times[J]. Information studies: theory & application, 2018, 41(4): 13-15, 12.)
[4] 张智雄, 于改红, 刘熠, 等. ChatGPT对文献情报工作的影响[J]. 数据分析与知识发现, 2023, 7(3): 36-42. (ZHANG Z X, YU H G, LIU Y, et al. The influence of ChatGPT on library & information services[J]. Data analysis and knowledge discovery, 2023, 7(3): 36-42.)
[5] 白如江, 鞠孜涵, 张玉洁, 等. 面向情报感知的多源多模态数据融合方法研究[J]. 情报杂志, 2023, 42(10): 124-131. (BAI R J, JU Z H, ZHANG Y J, et al. Research on multi-source and multi-mode data fusion for intelligence perception[J]. Journal of intelligence, 2023, 42(10): 124-131.)
[6] 刘细文, 付芸, 孙蒙鸽. 驱动情报工作范式变革的情报智能体技术解构[J]. 图书情报工作, 2025, 69(1): 4-15. (LIU X W, FU Y, SUN M G. Technological deconstruction of DIS agent driving the paradigm shift in documentation and information service work[J]. Library and information service, 2025, 69(1): 4-15.)
[7] 韩雪雯, 车尚锟, 杨梦晴, 等. 多模态数据驱动的AI智能体模式设计[J]. 图书情报工作, 2024, 68(24): 27-37. (HAN X W, CHE S K, YANG M Q, et al. Design of AI agent models driven by multimodal data[J]. Library and information service, 2024, 68(24): 27-37.)
[8] 王延飞, 赵柯然, 陈美华, 等. 情报感知的研究解析[J]. 情报理论与实践, 2018, 41(8): 1-4. (WANG Y F, ZHAO K Y, CHEN M H, et al. Analysis on the research of information awareness[J]. Information studies: theory & application, 2018, 41(8): 1-4.)
[9] 王益成, 王萍. 基于用户动态画像的科技情报服务推荐模型构建研究[J]. 情报理论与实践, 2019, 42(4): 83-88. (WANG Y C, WANG P. Research on the recommendation model of science and technology intelligence service based on user dynamic portrait[J]. Information studies: theory & application, 2019, 42(4): 83-88.)
[10] 杨晓刚, 姜毅, 张璠, 等. 基于大数据技术的用户小数据管理[J]. 情报理论与实践, 2018, 41(3): 29-33, 15. (YANG X G, JIANG Y, ZHANG F, et al. Research on the balance of user data application and privacy protection from the perspective of smart library[J]. Information studies: theory & application, 2018, 41(3): 29-33, 15.)
[11] 周群, 化柏林. 基于多源数据融合的科技决策需求主题识别研究[J]. 情报理论与实践, 2019, 42(3): 107-113. (ZHOU Q, HUA B L. Topic Identification of scientific and technical decision-making demands based on multi-source data fusion[J]. Information studies: theory & application, 2019, 42(3): 107-113.)
[12] 王金凤, 仵轩, 冯立杰, 等. 用户偏好-制造商偏好双重视阈下的产品创新机会识别路径研究[J]. 计算机集成制造系统, 2024, 30(4): 1433-1445. (WANG J F, WU X, FENG L J, et al. Product innovation opportunity identification path under dual perspective of user preference and manufacturer preference[J]. Computer integrated manufacturing systems, 2024, 30(4): 1433-1445.)
[13] 牛莉霞, 李肖萌. 5G时代面向情报需求的智慧矿山应急管理新模式研究[J]. 灾害学, 2024, 39(3): 228-234. (NIU L X, LI X M. Research on the new model of intelligent mine emergency management oriented to intelligence needs in the 5G Era[J]. Journal of catastrophology, 2024, 39(3): 228-234.)
[14] 刘宗明, 陈旭辉, 张丁方. 基于多模态感观需求下的智能产品评价发展与解析[J/OL]. 计算机集成制造系统, 1-24[2025-07-04]. https://doi.org/10.13196/j.cims.2024.0312.(LIU Z M, CHEN X H, ZHANG D F. Application and optimization research of multimodal microsensory technology in smart product evaluation[J/OL]. Computer integrated manufacturing systems,1-24[2025-07-04]. https://doi.org/10.13196/j.cims.2024.0312.)
[15] 张艳丰, 欧志梅. 数字孪生技术驱动下智慧图书馆场景化服务模式研究[J]. 情报理论与实践, 2022, 45(8): 47-53. (ZHANG Y F, OU Z M. Research on the scenario-based service mode of smart libraries driven by digital twin technology[J]. Information studies: theory & application, 2022, 45(8): 47-53.)
[16] 张国标, 李洁. 融合多模态内容语义一致性的社交媒体虚假新闻检测[J]. 数据分析与知识发现, 2021, 5(5): 21-29. (ZHANG G B, LI J. Detecting social media fake news with semantic consistency between multi-model contents[J]. Data analysis and knowledge discovery, 2021, 5(5): 21-29.)
[17] 易侃, 徐欣. 面向深度伪造的情报可信分析机理[J]. 指挥信息系统与技术, 2023, 14(1): 48-56. (YI K, XU X. Intelligence credibility analysis scheme for deepfake detection[J]. Command information system and technology, 2023, 14(1): 48-56.)
[18] 吴正清, 曹晖. 基于GAN的中文虚假评论数据集生成方法[J]. 云南大学学报(自然科学版), 2023, 45(5): 1033-1042. (WU Z Q, CAO H. Chinese fake review dataset generation method based onadversarial generation network[J]. Journal of Yunnan University (natural sciences edition), 2023, 45(5): 1033-1042.)
[19] 周林兴, 王帅. 数据投毒语境的LLM黑化情报感知方法研究[J].数据分析与知识发现, 2025, 9(4): 14-31. (ZHOU L X, WANG S. Research on LLM blackening intelligence perception method in the context of data poisoning[J]. Data analysis and knowledge discovery, 2025, 9(4): 14-31.)
[20] 施运梅, 袁博, 张乐, 等. IMTS: 融合图像与文本语义的虚假评论检测方法[J]. 数据分析与知识发现, 2022, 6(8): 84-96. (SHI Y M, YUAN B, ZHANG L, et al. IMTS: detecting fake reviews with image and text semantics[J]. Data analysis and knowledge discovery, 2022, 6(8): 84-96.)
[21] 张亚男, 王克平, 王艺, 等. 基于区块链的竞争情报联盟协作平台模型研究[J]. 图书情报知识, 2021, 38(6): 100-109, 122. (ZHANG Y N, WANG K P, WANG Y, et al. Cooperative platform model of competitive inteligence aliance based on blockchain[J]. Documentation, information & knowledge, 2021, 38(6): 100-109, 122.)
[22] 李广建, 罗立群. 计算型情报分析的进展[J]. 中国图书馆学报, 2019, 45(4): 29-43. (LI G J, LUO L Q. Progress in computational intelligence analysis[J]. Journal of library science in China, 2019, 45(4): 29-43.)
[23] 罗立群, 李广建, 徐敏. 面向计算的情报分析模型研究[J]. 情报科学, 2021, 39(10): 185-193. (LUO L Q, LI G J, XU M. Computing-oriented intelligence analysis model[J]. Information science, 2021, 39(10): 185-193.)
[24] 汪雅君, 石进, 李明, 等. 基于构件的情报分析模型研究[J]. 图书情报工作, 2022, 66(8): 92-101. (WANG Y J, SHI J, LI M, et al. Research on the intelligence analysis model based on the component[J]. Library and information service, 2022, 66(8): 92-101.)
[25] 张志豪, 刘思航, 马天骥, 等.大模型驱动下基于企业画像的专利交易个性化推荐研究[J].情报理论与实践, 2025, 48(5): 177-186. (ZHANG Z H, LIU S H, MA T J, et al. Personalized patent transaction recommendation based on enterprise profiles driven by large language model[J]. Information studies: theory & application, 2025, 48(5): 177-186.)
[26] 彭佳儿, 于菲菲, 赵月华. 从患者生成健康数据到用药建议生成:基于图检索增强生成的患者用药信息问答模型[J/OL]. 现代情报, 1-22[2025-07-04]. http://kns.cnki.net/kcms/detail/22.1182.G3.20250218.0942.002.html. (PENG J E, YU F F, ZHAO Y H. From patient-generated health data to medication recommendations: a graph retrieval-augmented generation model for patient medication information Q&A[J/OL]. Journal of modern information, 1-22[2025-07-04]. http://kns.cnki.net/kcms/detail/22.1182.G3.20250218.0942.002.html.)
[27] 海佳丽, 汪润, 袁良志, 等.基于检索增强的中医药标准知识问答系统构建探索与实践[J]. 数据分析与知识发现, 2025, 9(7): 165-174. (HAI J L, WANG R, YUAN L Z. Exploration and practice in constructing a retrieval-augmented question-answering system for traditional chinese medicine standards[J]. Data analysis and knowledge discovery, 2025, 9(7): 165-174.)
[28] 孔凡芃, 刘旭红, 刘秀磊, 等. 基于BERT模型的航天科技开源情报分类[J]. 北京信息科技大学学报(自然科学版), 2021, 36(1): 28-33. (KONG F P, LIU X H, LIU X L, et al. Classification of open source intelligence of aerospace science and technology based on BERT Model[J]. Journal of Beijing Information Science & Technology University (science and technology edition), 2021, 36(1): 28-33.)
[29] 王婧怡, 陈喆, 罗敏, 等. 用户需求视角下高校图书馆全场景学科服务模型构建[J]. 图书馆学研究, 2024(2): 75-84. (WANG J Y, CHEN Z, LUO M, et al. Construction of full-scene subject service model of university library from the perspective of users needs[J]. Research on library science, 2024(2): 75-84.)
[30] 郭亚军, 周家华, 庞义伟, 等. ChatGPT赋能信息检索:原理、测评、场景与进路[J]. 情报理论与实践, 2025, 48(1): 83-92. (GUO Y J, ZHOU J H, PANG Y W, et al. ChatGPT empowers information retrieval: principles, evaluations, scenarios and approaches[J]. Information studies: theory & application, 2025, 48(1): 83-92.)
Outlines

/

〈 〉