Machine Learning (ML) Platforms Can Contradict Dairy Scientists and Feed Firm Websites Regarding Dairy Cattle Performance from Feeding Seaweed Supplements

• Published in: Choices (Ames, Iowa) Iowa), 2024-07, Vol.39 (3), p.1-5
• Main Authors: O’Keefe, Siobhan, Welsh, Rick, Oppong, Mercy, Fitzgerald, Ryan, Conner, David, Tynan, Michelle, Price, Nichole, Quigle, Charlotte
• Format: Article
• Language: English
• Subjects: Agricultural research; Agriculture and environment; Climate; Climate Change; Environmental Economics and Policy; Research and Development/Tech Change/Emerging Technologies
• ISSN: 0886-5558
• DOI: 10.22004/ag.econ.344751

Artificial intelligence through machine learning applications (hereafter ML) is emerging as a tool in evaluating, comparing, and going beyond human capabilities and knowledge. Despite the potential benefits of ML as a resource for answering scientific questions, such as those included in our analysis, some characteristics of ML-generated responses limit the interpretations of these results—such as ML “hallucinations”—of which researchers should be aware (McIntosh et al., 2023). Nonetheless, ML is quickly becoming a source for authoritative and trusted information on many topics (Knight, 2024; McIntosh et al., 2024), as university-based and other more rigorous research may be behind paywalls or otherwise difficult to access and as pay-to-play journals proliferate. Therefore, it is useful to conduct analyses comparing ML-generated information to traditionally trusted information sources, such as scientists’ observations, and to self-interested commercial information available to the public.