Article(id=1242140178492498114, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242140171227968084, articleNumber=null, orderNo=4, doi=10.3981/j.issn.1000-7857.2022.22.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1628870400000, receivedDateStr=2021-08-14, revisedDate=1638806400000, revisedDateStr=2021-12-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1670897420020, onlineDateStr=2022-12-13, pubDate=1669564800000, pubDateStr=2022-11-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1670897420020, onlineIssueDateStr=2022-12-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078896397, creator=sys-migrate, updateTime=1774078896397, updator=sys-migrate, issue=Issue{id=1242140171227968084, tenantId=1146029695717560320, journalId=1146031591421210625, year='2022', volume='40', issue='22', pageStart='1', pageEnd='112', issueExtLink='null', onlineDate='null', pubDate='1669564800000', pubDateStr='2022-11-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078894665, creator='sys-migrate', updateTime=1774078894665, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=12, endPage=19, ext={EN=ArticleExt(id=1242140182145741457, articleId=1242140178492498114, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Life cycle dynamic comparison and evolutionary path survey of the core technologies in artificial intelligence industry of China and the USA, columnId=1242139460859671165, journalTitle=Science & Technology Review, columnName=Suggestions, runingTitle=null, highlight=null, articleAbstract=By using Logistic model, the core technology life cycle characteristics in the artificial intelligence industry were dynamically compared between China and the USA. The evolution trend of the core technologies in artificial intelligence industry of China and the USA was discussed by integrating technology life cycle and RTA Index. It discovers that:(1) 2025-2030 will be the key period for the development and breakthrough of core technologies in artificial intelligence industry in China, and China should firmly grasp the critical period to achieve catching-up. (2) During the evolution of the core technologies in artificial intelligence industry, China had technological advantages in computer vision and intelligent adaptive learning, rather than comprehensive technical fields such as cross-media analysis and reasoning and swarm intelligence. Due to the brain-computer interface and smart chip not entering the mature period, China should be aware of the core technique lock-in and defense strategies of the first-mover. Relying on the broad markets and the wide range of application scenarios, natural language processing and autonomous unmanned system were developed rapidly., authors=YUAN Ye, WU Chaonan, LI Jingying, TAO Yuxiang, authorsList=YUAN Ye, WU Chaonan, LI Jingying, TAO Yuxiang, authorCompany=School of Economics and Management, Chongqing University of Post and Telecommunications, Chongqing 400065, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=Gv6s97jL02sFfWRTINpicw==, pdfFileSize=1012644, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242140181302686346, articleId=1242140178492498114, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=中美人工智能产业核心技术生命周期的动态比较与演进路径, columnId=1242116825887080764, journalTitle=科技导报, columnName=科技工作者建议, runingTitle=null, highlight=null, articleAbstract=采用Logistic模型，动态对比中美人工智能产业核心技术生命周期特征；综合技术生命周期和RTA指数，论述了中美人工智能产业核心技术演化趋势。研究结果表明：（1）2025—2030年将是中国人工智能产业核心技术发展突破的关键时期，应该牢牢抓住这一关键期以实现后发追赶。（2）中国在人工智能产业核心技术演化过程中，计算机视觉和智适应学习技术具有技术领先优势；在跨媒体分析推理和群体智能等综合性技术领域并不具备技术优势，即将面临着后发追赶的压力；脑机接口和智能芯片等技术密集型技术并未进入成熟期，要谨防先发国家的“技术锁定”和防御策略；而在自然语言处理和自主无人系统技术领域，依托自身广阔的市场和广泛的应用场景发展迅速。, authors=袁野, 吴超楠, 李晶莹, 陶于祥, authorsList=袁野, 吴超楠, 李晶莹, 陶于祥, authorCompany=重庆邮电大学经济管理学院, 重庆 400065, correspAuthors=null, authorNote=袁野,副教授,研究方向为人工智能产业与技术创新,电子信箱:yuanye@cqupt.edu.cn, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=PPzxQBPbz0eRCsaasmk02Q==, pdfFileSize=1012644, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=国家社会科学基金青年项目（20CGL004）)}, authors=[Author(id=1280041881929237323, tenantId=1146029695717560320, journalId=null, articleId=1242140178492498114, orderNo=null, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=null, email=null, emailSecond=null, emailThird=null, correspondingAuthor=null, authorType=null, ext={EN=AuthorExt(id=null, tenantId=null, journalId=1146031591421210625, articleId=1242140178492498114, authorId=1280041881929237323, language=EN, stringName=YUAN Ye, WU Chaonan, LI Jingying, TAO Yuxiang, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=null, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=null, tenantId=null, journalId=1146031591421210625, 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