{"title":"Massive data language models and conversational artificial intelligence: Emerging issues","authors":"Daniel E. O’Leary","doi":"10.1002/isaf.1522","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Google’s LaMDA, Open AI’s GPT-3, and Meta’s BlenderBot are artificial intelligence (AI)-based chatbots, that have been trained on billions of documents creating the notion of “massive data.” These systems use human-generated documents to capture words and relationships between words that people use when they communicate. This paper examines some of the similarities of these systems and the emerging issues regarding these massive data language models, including whether they are sentient, the use and impact of scale, information use and ownership, and explanations of discussions and answers. This paper also directly investigates some artifacts of Google’s LaMDA and compares them with Meta’s BlenderBot. Finally, this paper examines emerging issues and questions deriving from our analysis.</p>\n </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"29 3","pages":"182-198"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Systems in Accounting, Finance and Management","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/isaf.1522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
引用次数: 9
Abstract
Google’s LaMDA, Open AI’s GPT-3, and Meta’s BlenderBot are artificial intelligence (AI)-based chatbots, that have been trained on billions of documents creating the notion of “massive data.” These systems use human-generated documents to capture words and relationships between words that people use when they communicate. This paper examines some of the similarities of these systems and the emerging issues regarding these massive data language models, including whether they are sentient, the use and impact of scale, information use and ownership, and explanations of discussions and answers. This paper also directly investigates some artifacts of Google’s LaMDA and compares them with Meta’s BlenderBot. Finally, this paper examines emerging issues and questions deriving from our analysis.
期刊介绍：
Intelligent Systems in Accounting, Finance and Management is a quarterly international journal which publishes original, high quality material dealing with all aspects of intelligent systems as they relate to the fields of accounting, economics, finance, marketing and management. In addition, the journal also is concerned with related emerging technologies, including big data, business intelligence, social media and other technologies. It encourages the development of novel technologies, and the embedding of new and existing technologies into applications of real, practical value. Therefore, implementation issues are of as much concern as development issues. The journal is designed to appeal to academics in the intelligent systems, emerging technologies and business fields, as well as to advanced practitioners who wish to improve the effectiveness, efficiency, or economy of their working practices. A special feature of the journal is the use of two groups of reviewers, those who specialize in intelligent systems work, and also those who specialize in applications areas. Reviewers are asked to address issues of originality and actual or potential impact on research, teaching, or practice in the accounting, finance, or management fields. Authors working on conceptual developments or on laboratory-based explorations of data sets therefore need to address the issue of potential impact at some level in submissions to the journal.
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