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| An Analysis of the Industrialization Path of Key Core Technologies of Artificial Intelligence from the Perspective of Energy Conversion |
| Sun Liwen1,Li Shaoshuai1,Sun Yang2 |
| （1.School of Economics and Management，Hebei University of Technology, Tianjin 300401，China；2.Jinnan Research Institute, Nankai University, Tianjin 300350，China） |
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Abstract It is of strategic significance to explore the industrialization path of key core technologies of artificial intelligence for developing the digital economy, promoting digital transformation and building an innovative country in the background of new round of scientific and technological revolution and industrial revolution. Artificial intelligence technology has made great progress and some key core technologies have gradually broken through application threshold and entered the stage of industrial application. However, industrialization of artificial intelligence technology is still in the early stage of development, so the industrialization path is not completely clear, and the commercial value of artificial intelligence is difficult to be fully released. The academic circles have conducted a series of discussions on technology industrialization using patent data, literature data, and R&D data from the perspective of technology transfer and transformation of scientific and technological achievements, but the existing research can′t meet the actual needs of the highly complex and interrelated artificial intelligence technology industrialization. This paper classifies the complex energy flow process of ecosystem operation from the perspective of energy conversion, hoping to provide new perspective for research on the industrialization path of key core technologies of artificial intelligence.#br#This paper selects data from 2000 to 2020, and a total of 7 811 patent documents related to artificial intelligence were retrieved and sorted out in the DII database. Based on the specific co-occurrence situation with reference to the standard system framework in the White Paper on Artificial Intelligence Standardization (2018), the 239 technical keywords of artificial intelligence are further extracted, which is adapted to the existing clustering results, and finally this paper draws the artificial intelligence core technology network map.#br#The results show that the industrialization path of artificial intelligence core technology has characteristics of energy conversion. Technological innovation energy, catalytic energy, commercial conversion energy and business shape energy constitute the core link of energy conversion, which determines specific process of the path of technology industrialization. The key core technologies of artificial intelligence include eight technical fields such as machine learning, computer vision, and natural language processing. The core technologies of different attributes constitutes the corresponding technology clusters, forming a technological industrialization path with the themes of "identification", "interaction" and "execution". The attribute of technology cluster is a key factor that affects the path of technology industrialization.#br#This paper reveals that the industrialization of artificial intelligence core technology is not equal to a simple technology transfer and routine replication, but a cumulative practice process of a series of complementary innovations and specialized technology systems, as well as a process of energy gathering and transformation. Managers should focus on technology clusters composed of multiple core technologies, and promote collaborative innovation among technologies by innovating R&D models and optimizing internal structures. It is necessary to keep exploring and cultivating powerful technological innovation energy, catalytic breeding energy, commercial transformation energy and business format shaping energy, so as to support the implementation of industrialization path of core artificial intelligence technologies. Managers should expand energy conversion channels from multiple perspectives to improve energy conversion efficiency. It is necessary to explore more novel and reasonable energy conversion methods such as technology incubation and technology empowerment from the perspective of technological innovation to maximize the overall value. It is also essential to improve the rationality of value matching and innovative human-machine collaboration methods from the perspective of scene application.#br#
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Received: 31 August 2021
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| [1] 袁野,汪书悦,陶于祥.人工智能关键核心技术创新能力测度体系构建:基于创新生态系统视角[J].科技进步与对策，2021,38（18）：84-93.[2] 温芳芳.基于专利许可关系网络的技术转移现状及规律研究[J].情报科学,2014,32(11):24-29.[3] 吴菲菲,张辉,黄鲁成.基于文献计量的技术转移问题研究主题分析[J].科技管理研究,2015,35(8):83-89，109.[4] 罗思平,于永达.技术转移、“海归”与企业技术创新——基于中国光伏产业的实证研究[J].管理世界,2012，38(11):124-132.[5] 余江,陈凤,张越,等.铸造强国重器:关键核心技术突破的规律探索与体系构建[J].中国科学院院刊,2019,34(3):339-343.[6] 王海军.关键核心技术创新的理论探究及中国情景下的突破路径[J].当代经济管理,2021,43(6):43-50.[7] SHIYOMI M, AKIYAMA T, TAKAHASHI S. Modelling of energy flows and conversion efficiencies in a grassland ecosystem[J]. Ecological Modelling, 1986, 32(1):119-135.[8] KAI W, CARMONA L G, CARRASCO A. The resource service cascade: a conceptual framework for the integration of ecosystem, energy and material services[J]. Environmental Development, 2021(6):100647.[9] 李文华.中国当代生态学研究[M].北京:科学出版社，2013:37-39.[10] HOWARD T, ODUM. Primary production in flowing waters1[J]. Limnology and Oceanography, 2003, 1(2):102-117.[11] 陈军,张韵君,王健.基于专利分析的中美人工智能产业发展比较研究[J].情报杂志,2019,38(1):41-47.[12] 赵蓉英,李新来,李丹阳.专利引证视角下的核心专利研究——以人工智能领域为例[J].情报理论与实践,2019,42(3):78-84.[13] 陈悦,谭建国,王智琦,等.专利视角下工业机器人领域的技术机会分析[J].科研管理,2018,39(4):144-156.[14] GOLDBERG D E. Genetic algorithm in search, optimization, and machine learning[M]// Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley Pub. Co, 1989.[15] FRAN, COIS-LAVET V, HENDERSON P, et al. An introduction to deep reinforcement learning[J]. Foundations Trends Mach Learn, 2018, 11: 3-4.[16] 陈悦,刘则渊.悄然兴起的科学知识图谱[J].科学学研究,2005，23(2):149-154.[17] 袁保宗,阮秋琦,王延江,等.新一代(第四代)人机交互的概念框架特征及关键技术[J].电子学报,2003，42(S1):1945-1954.[18] 谭民,王硕.机器人技术研究进展[J].自动化学报,2013,39(7):963-972. |
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