{"title":"Generative AI: A systematic review using topic modelling techniques","authors":"Priyanka Gupta , Bosheng Ding , Chong Guan , Ding Ding","doi":"10.1016/j.dim.2024.100066","DOIUrl":null,"url":null,"abstract":"<div><p>Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of applications. In this paper, a thorough examination of the research landscape in GAI is presented, encompassing a comprehensive overview of the prevailing themes and topics within the field. The study analyzes a corpus of 1319 records from Scopus spanning from 1985 to 2023 and comprises journal articles, books, book chapters, conference papers, and selected working papers.</p><p>The analysis revealed seven distinct clusters of topics in GAI research: image processing and content analysis, content generation, emerging use cases, engineering, cognitive inference and planning, data privacy and security, and Generative Pre-Trained Transformer (GPT) academic applications. The paper discusses the findings of the analysis and identifies some of the key challenges and opportunities in GAI research.</p><p>The paper concludes by calling for further research in GAI, particularly in the areas of explainability, robustness, cross-modal and multi-modal generation, and interactive co-creation. The paper also highlights the importance of addressing the challenges of data privacy and security in GAI and responsible use of GAI.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"8 2","pages":"Article 100066"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2543925124000020/pdfft?md5=dbdf97fdc7e10603e5fe8ff706294d18&pid=1-s2.0-S2543925124000020-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data and information management","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2543925124000020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of applications. In this paper, a thorough examination of the research landscape in GAI is presented, encompassing a comprehensive overview of the prevailing themes and topics within the field. The study analyzes a corpus of 1319 records from Scopus spanning from 1985 to 2023 and comprises journal articles, books, book chapters, conference papers, and selected working papers.
The analysis revealed seven distinct clusters of topics in GAI research: image processing and content analysis, content generation, emerging use cases, engineering, cognitive inference and planning, data privacy and security, and Generative Pre-Trained Transformer (GPT) academic applications. The paper discusses the findings of the analysis and identifies some of the key challenges and opportunities in GAI research.
The paper concludes by calling for further research in GAI, particularly in the areas of explainability, robustness, cross-modal and multi-modal generation, and interactive co-creation. The paper also highlights the importance of addressing the challenges of data privacy and security in GAI and responsible use of GAI.
生成人工智能（GAI）是一个发展迅速、应用广泛的领域。本文对 GAI 的研究现状进行了深入研究，全面概述了该领域的流行主题和话题。研究分析了 Scopus 中从 1985 年到 2023 年 1319 条记录的语料库，其中包括期刊论文、书籍、书籍章节、会议论文和部分工作论文。分析揭示了 GAI 研究中七个不同的主题集群：图像处理和内容分析、内容生成、新兴用例、工程、认知推理和规划、数据隐私和安全以及生成预训练变换器 (GPT) 学术应用。论文讨论了分析结果，并指出了 GAI 研究中的一些关键挑战和机遇。论文最后呼吁进一步开展 GAI 研究，特别是在可解释性、稳健性、跨模态和多模态生成以及交互式共同创造等领域。本文还强调了解决 GAI 中数据隐私和安全挑战以及负责任地使用 GAI 的重要性。
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