Towards a personalized AI assistant to learn machine learning

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Nature Machine Intelligence Pub Date : 2024-12-05 DOI:10.1038/s42256-024-00953-0
Pascal Wallisch, Ibrahim Sheikh
{"title":"Towards a personalized AI assistant to learn machine learning","authors":"Pascal Wallisch, Ibrahim Sheikh","doi":"10.1038/s42256-024-00953-0","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 12","pages":"1413-1414"},"PeriodicalIF":18.8000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Machine Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.nature.com/articles/s42256-024-00953-0","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
朝着个性化的人工智能助手学习机器学习
2022年末,OpenAI的ChatGPT等生成式人工智能工具的引入和迅速普及,极大地影响了教育领域。许多学生——也许是大多数——现在经常在课程作业中使用ChatGPT和类似的模型。在我们在纽约大学(NYU)教授的课程中,超过90%的本科生和硕士生在2023年报告使用大型语言模型(llm),到2024年上升到95%以上。例如,法学硕士课程在私人助理领域的广泛应用,有望为学习带来巨大的好处。然而,它们也可能被证明是有害的。一个担忧是,这些人工智能工具可能会取代学习。例如,生成式人工智能工具可以为学生编写代码或论文,而不是学生自己学习如何编写代码或论文。如果生成式人工智能工具通过减少学生花在学习材料上的时间来减少勤奋,那么由此产生的学习效果可能会大幅下降。使用基于llm的工具进行学习的另一个关键问题是,它们是在庞大的、有时是不可靠的文本语料库上训练的,并且以概率方式进行响应。这使得“幻觉”——矛盾的、不准确的或错误的输出——不可避免。由于学生们可能受到法学硕士课程自信基调的影响,往往只从表面上接受这些输出,幻觉对学习本身的完整性构成了威胁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
期刊最新文献
A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens Machine learning solutions looking for PDE problems Evolutionary optimization of model merging recipes Moving towards genome-wide data integration for patient stratification with Integrate Any Omics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1