Streich J. et al., 2020. Current Opinion in Biotechnology
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, and Antoine L. Harfouche
18-February-2020, Current Opinion in Biotechnology 61: 217-225; https://doi.org/10.1016/j.copbio.2020.01.010
AbstractHuman 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.
Streich, Jared, Romero, Jonathon, Gazolla, João Gabriel Felipe Machado, Kainer, David, Cliff, Ashley, Prates, Erica Teixeira, Brown, James B., Khoury, Sacha, Tuskan, Gerald A., Garvin, Michael, Jacobson, Daniel and Harfouche, Antoine 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