宣传人工智能在农业和环境研究中的应用

IF 2.3 4区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Aaron Lee M. Daigh, Samira H. Daroub, Peter M. Kyveryga, Mark E. Sorrells, Nithya Rajan, James A. Ippolito, Endy Kailer, Christine S. Booth, Umesh Acharya, Deepak Ghimire, Saurav Das, Bijesh Maharjan, Yufeng Ge
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引用次数: 0

摘要

人工智能(AI)等变革性技术使艰巨的任务变得更加容易和便捷。2018年以来,人工智能在科研领域的应用急剧增加,年发表率是2017年前的3-5倍。目前,>每年在科学和工程领域发表的使用人工智能的稿件达10万篇,>其中2万篇属于农业和环境领域。鉴于人工智能的使用规模之大,就如何使用人工智能以及人工智能如何帮助推动科学知识的发展进行清晰的交流至关重要。与以往的技术相比,人工智能可能更需要清晰的沟通,这是因为人工智能的用途广泛而灵活,深度学习算法具有 "黑箱 "性质,而且关于人工智能的预测能力与第一原理机械论和基于过程的理论和模型知识之间的争论仍在继续。在这篇评论中,我们为科学界提供了指导原则和讨论要点,以确保在农业和环境研究出版物中对人工智能研究进行透明、有效的交流。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Communicating the use of artificial intelligence in agricultural and environmental research

Communicating the use of artificial intelligence in agricultural and environmental research

Transformative technologies such as artificial intelligence (AI) make difficult tasks more accessible and convenient. Since 2018, the use of AI in research has increased drastically, with annual publication rates of 3–5 times higher than pre-2017. Currently, >100,000 manuscripts using AI are published annually within science and engineering, and >20,000 of these belong to the agricultural and environmental fields. Given the magnitude of use, clear communication on how AI is used and how it helps advance scientific knowledge is essential. Clear communication is perhaps more necessary with AI than previous technologies due to its broad and flexible spectrum of uses, the “black-box” nature of deep-learning algorithms, and ongoing debates regarding AI's predictive power versus knowledge of first-principles mechanistic and process-based theories and models. In this commentary, we provide guidelines and discussion points to the scientific community to ensure transparent and effective communication of AI research in agricultural and environmental research publications.

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来源期刊
CiteScore
3.70
自引率
3.80%
发文量
28
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