Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Industry 4.0.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Cybersecurity impacts for artificial intelligence use within Industry 4.0|Cybersecurity impacts for artificial intelligence use within Industry 4.0]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


In today’s modern digital manufacturing landscape, new and emerging technologies can shape how an organization can compete, while others will view those technologies as a necessity to survive, as manufacturing has been identified as a critical infrastructure. Universities struggle to hire university professors that are adequately trained or willing to enter academia due to competitive salary offers in the industry. Meanwhile, the demand for people knowledgeable in fields such as [[artificial intelligence]], [[Informatics (academic field)|data science]], and [[cybersecurity]] continuously rises, with no foreseeable drop in demand in the next several years. This results in organizations deploying technologies with a staff that inadequately understands what new cybersecurity risks they are introducing into the company. This work examines how organizations can potentially mitigate some of the risk associated with integrating these new technologies and developing their workforce to be better prepared for looming changes in technological skill need. ('''[[Journal:Cybersecurity impacts for artificial intelligence use within Industry 4.0|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
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Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
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