Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?

Jared Streich; Jonathon Romero; João Gabriel Felipe Machado Gazolla; David Kainer; Ashley Cliff; Erica Teixeira Prates; James B. Brown; Sacha Khoury; Gerald A. Tuskan; Michael Garvin; Daniel Jacobson; Antoine L. Harfouche

doi:10.1016/j.copbio.2020.01.010

Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?

Jared Streich, Jonathon Romero, João Gabriel Felipe Machado Gazolla, David Kainer, Ashley Cliff, Erica Teixeira Prates, James B. Brown, Sacha Khoury, Gerald A. Tuskan, Michael Garvin, Daniel Jacobson, Antoine L. Harfouche

Research output: Contribution to journal › Review article › peer-review

42 Scopus citations

Abstract

Human population growth and accelerated climate change necessitate agricultural improvements using designer crop ideotypes (idealized plants that can grow in niche environments). Diverse and highly skilled research groups must integrate efforts to bridge the gaps needed to achieve international goals toward sustainable agriculture. Given the scale of global agricultural needs and the breadth of multiple types of omics data needed to optimize these efforts, explainable artificial intelligence (AI with a decipherable decision making process that provides a meaningful explanation to humans) and exascale computing (computers that can perform 10¹⁸ floating-point operations per second, or exaflops) are crucial. Accurate phenotyping and daily-resolution climatype associations are equally important for refining ideotype production to specific environments at various levels of granularity. We review advances toward tackling technological hurdles to solve multiple United Nations Sustainable Development Goals and discuss a vision to overcome gaps between research and policy.

Original language	English
Pages (from-to)	217-225
Number of pages	9
Journal	Current Opinion in Biotechnology
Volume	61
DOIs	https://doi.org/10.1016/j.copbio.2020.01.010
State	Published - Feb 2020

Funding

Completion of this manuscript was supported, in part, by funding from the EU 7th Framework Programme – Development of improved perennial non-food biomass and bioproduct crops for water stressed environments WATBIO, grant no 311929 ; the Center for Bioenergy Innovation, a U.S. Department of Energy DOE Bioenergy Research Center, the Plant Microbe Interface Scientific Focus Area and the Feedstock Genomics program, all three supported by the Biological and Environmental Research in the DOE Office of Science; and the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. Funding was also provided by the DOE, Laboratory Directed Research and Development funding ORNL AI Initiative ProjectID 9613 at the Oak Ridge National Laboratory.

Funders	Funder number
DOE Office of Science
U.S. Department of Energy DOE Bioenergy Research Center, the Plant Microbe Interface Scientific Focus Area
U.S. Department of Energy
Office of Science	DE-AC05-00OR22725
Office of Science
Biological and Environmental Research
Oak Ridge National Laboratory
Laboratory Directed Research and Development
Seventh Framework Programme	311929
Seventh Framework Programme
Center for Bioenergy Innovation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.copbio.2020.01.010

Cite this

Streich, J., Romero, J., Gazolla, J. G. F. M., Kainer, D., Cliff, A., Prates, E. T., Brown, J. B., Khoury, S., Tuskan, G. A., Garvin, M., Jacobson, D., & Harfouche, A. L. (2020). Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals? Current Opinion in Biotechnology, 61, 217-225. https://doi.org/10.1016/j.copbio.2020.01.010

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title = "Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?",

abstract = "Human population growth and accelerated climate change necessitate agricultural improvements using designer crop ideotypes (idealized plants that can grow in niche environments). Diverse and highly skilled research groups must integrate efforts to bridge the gaps needed to achieve international goals toward sustainable agriculture. Given the scale of global agricultural needs and the breadth of multiple types of omics data needed to optimize these efforts, explainable artificial intelligence (AI with a decipherable decision making process that provides a meaningful explanation to humans) and exascale computing (computers that can perform 1018 floating-point operations per second, or exaflops) are crucial. Accurate phenotyping and daily-resolution climatype associations are equally important for refining ideotype production to specific environments at various levels of granularity. We review advances toward tackling technological hurdles to solve multiple United Nations Sustainable Development Goals and discuss a vision to overcome gaps between research and policy.",

author = "Jared Streich and Jonathon Romero and Gazolla, {Jo{\~a}o Gabriel Felipe Machado} and David Kainer and Ashley Cliff and Prates, {Erica Teixeira} and Brown, {James B.} and Sacha Khoury and Tuskan, {Gerald A.} and Michael Garvin and Daniel Jacobson and Harfouche, {Antoine L.}",

note = "Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",

year = "2020",

month = feb,

doi = "10.1016/j.copbio.2020.01.010",

language = "English",

volume = "61",

pages = "217--225",

journal = "Current Opinion in Biotechnology",

issn = "0958-1669",

publisher = "Elsevier",

}