Article(id=1242141240280555647, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242141225407558554, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2024.05.00531, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1715616000000, receivedDateStr=2024-05-14, revisedDate=1716825600000, revisedDateStr=2024-05-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1720454400000, onlineDateStr=2024-07-09, pubDate=1719504000000, pubDateStr=2024-06-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1720454400000, onlineIssueDateStr=2024-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774079149549, creator=sys-migrate, updateTime=1774079149549, updator=sys-migrate, issue=Issue{id=1242141225407558554, tenantId=1146029695717560320, journalId=1146031591421210625, year='2024', volume='42', issue='12', pageStart='1', pageEnd='200', issueExtLink='null', onlineDate='null', pubDate='1719504000000', pubDateStr='2024-06-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=null, issueType=-1, specialIssue=null, createTime=1774079145994, creator='sys-migrate', updateTime=1774079145994, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=44, endPage=50, ext={EN=ArticleExt(id=1242141243203990491, articleId=1242141240280555647, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Evolution of general large models, columnId=1242141235033481325, journalTitle=Science & Technology Review, columnName=Exclusive：Foster New Quality Productive Forces to Strengthen High-Level Science and Technology Self-Reliance, runingTitle=null, highlight=null, articleAbstract=With the rapid development of artificial intelligence (AI) technology, general large models (GLMs) have become a significant research focus in the AI field. GLMs typically possess an extensive number of parameters, are trained on massive datasets and exhibit robust learning and reasoning capabilities. These models demonstrate outstanding performance in various tasks, including natural language processing, image recognition, and code generation. This paper reviews the evolution of GLMs and the key technology nodes, from the early rule-based systems and traditional machine learning models to the rise of deep learning, the introduction of the Transformer architecture, and the advancements in the GPT series and other GLMS over the world. Despite the significant progress, GLMs face numerous challenges, such as high computational resource demands, data bias, ethical issues, and model interpretability and transparency. This paper analyzes these challenges and explores five key future development directions for GLMs: model optimization, multimodal learning, emotionally intelligent models, data and knowledge dual-driven models, and ethical and societal impacts. By adopting these strategies, GLMs are expected to achieve broader and deeper applications, driving continuous progress in AI technology., authors=REN Fuji^1,2, ZHANG Yanru^1,2, authorsList=REN Fuji, ZHANG Yanru, authorCompany=1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
2. Shenzhen Institute for Advanced study, UESTC, Shenzhen 518110, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, 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=1242141242570650575, articleId=1242141240280555647, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=通用大模型演进路线, columnId=1242141234844737642, journalTitle=科技导报, columnName=专题：培育新质生产力 助力高水平科技自立自强, runingTitle=null, highlight=null, articleAbstract=随着人工智能技术的飞速发展，通用大模型（GLMs）已经成为人工智能领域的重要研究方向。通用大模型拥有超大规模参数，通过大规模数据进行训练，具备强大的学习和推理能力。这些模型在自然语言处理、图像识别、代码生成等多种任务中展现出卓越的能力。回顾了通用大模型的发展历程，梳理关键技术节点，从早期基于规则的系统和传统机器学习模型，到深度学习的崛起，再到Transformer架构，以及GPT系列及国内外通用大模型的进展。尽管GLMs在多个领域取得了显著进展，但其发展也面临诸多挑战，包括计算资源需求、数据偏见与伦理问题及模型的解释性与透明性。分析了这些挑战，并探讨了GLMs未来发展的5个关键方向：模型优化、多模态学习、具情感大模型、数据与知识双驱动以及伦理与社会影响。通过这些策略，通用大模型有望在未来实现更广泛和深入的应用，推动人工智能技术的持续进步。, authors=任福继^1,2, 张彦如^1,2, authorsList=任福继, 张彦如, authorCompany=1. 电子科技大学计算机科学与工程学院, 成都 611731;
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