TY - JOUR
T1 - Special report
T2 - AI Institute for next generation food systems (AIFS)
AU - Tagkopoulos, Ilias
AU - Brown, Stephen F.
AU - Liu, Xin
AU - Zhao, Qing
AU - Zohdi, Tarek I.
AU - Mason Earles, J.
AU - Nitin, Nitin
AU - Runcie, Daniel E.
AU - Lemay, Danielle G.
AU - Smith, Aaron D.
AU - Ronald, Pamela C.
AU - Feng, Hao
AU - David Youtsey, Gabriel
N1 - Funding Information:
This work is supported by AFRI Competitive Grant no. 2020–67021-32855/project accession no. 1,024,262 from the USDA National Institute of Food and Agriculture. This grant is being administered through AIFS: the AI Institute for Next Generation Food Systems. https://aifs.ucdavis.edu .
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/5
Y1 - 2022/5
N2 - Artificial Intelligence (AI) has the potential to transform US food systems by targeting its biggest challenges: improving food yield, quality, and nutrition, decreasing resource consumption, increasing safety and traceability, and eliminating food waste. Despite big leaps in AI capacity, food systems present several challenges for the application and adoption of AI: (1) Food systems are highly diverse and biologically complex, (2) ground-truth data is sparse, costly, and privately held, and (3) human decisions and preferences are intricately linked to every stage of food system supply chains. To address these challenges and transform U.S. food systems, the AI Institute for Next Generation Food Systems (AIFS) aims to develop AI technologies and nurture the next generation of talent to produce and distribute more high-quality nutritious food with fewer resources. AIFS has six research clusters, including two Foundational Research Areas (Use-Inspired and Foundational AI, and Socioeconomics and Ethics) and four Application Research Areas spanning the entire food supply chain: Molecular Breeding, Agricultural Production, Food Processing and Distribution, and Nutrition. AIFS is developing generalizable, data efficient, and trustworthy AI solutions based on a knowledge-driven and human-in-the-loop learning paradigm designed to handle food system diversity and biological complexity, efficiently capture, and utilize food system data, and garner user trust via explainability, safety, privacy, and fairness.
AB - Artificial Intelligence (AI) has the potential to transform US food systems by targeting its biggest challenges: improving food yield, quality, and nutrition, decreasing resource consumption, increasing safety and traceability, and eliminating food waste. Despite big leaps in AI capacity, food systems present several challenges for the application and adoption of AI: (1) Food systems are highly diverse and biologically complex, (2) ground-truth data is sparse, costly, and privately held, and (3) human decisions and preferences are intricately linked to every stage of food system supply chains. To address these challenges and transform U.S. food systems, the AI Institute for Next Generation Food Systems (AIFS) aims to develop AI technologies and nurture the next generation of talent to produce and distribute more high-quality nutritious food with fewer resources. AIFS has six research clusters, including two Foundational Research Areas (Use-Inspired and Foundational AI, and Socioeconomics and Ethics) and four Application Research Areas spanning the entire food supply chain: Molecular Breeding, Agricultural Production, Food Processing and Distribution, and Nutrition. AIFS is developing generalizable, data efficient, and trustworthy AI solutions based on a knowledge-driven and human-in-the-loop learning paradigm designed to handle food system diversity and biological complexity, efficiently capture, and utilize food system data, and garner user trust via explainability, safety, privacy, and fairness.
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U2 - 10.1016/j.compag.2022.106819
DO - 10.1016/j.compag.2022.106819
M3 - Article
AN - SCOPUS:85126512895
SN - 0168-1699
VL - 196
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 106819
ER -