Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?. (February 2020)

• Record Type: Journal Article
• Authors: 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; Harfouche, Antoine L
• Is Part Of: Current opinion in biotechnology. Volume 61(2020)
• Page Start: 217
• Page End: 225
• Publication Date: 2020-02
• Subjects: Biotechnology -- Periodicals; 660.6
• DOI: 10.1016/j.copbio.2020.01.010
• Languages: English
• ISSNs: 0958-1669

Abstract

Graphical abstract: Fusing explainable artificial intelligence (X-AI, AI with decipherable decision making process) and exascale computing ― 10 18, or a quintillion, floating-point operations per second (flops) level of performance ― can help plant and computational biologists achieve breakthroughs in designing multi-criteria crop ideotypes (i.e. crops with the optimal combination of traits for a given environment), mapping global climatypes, revealing the underlying biologically relevant interactions (e.g. SNP correlation network, 3D-interactome network) and, consequently, accelerating food and energy plant breeding programs widely recognized as critical to achieving the United Nations Sustainable Development Goals. Highlights: Plant population-scale multi-omics research is key to developing sustainable agriculture. Explainable artificial intelligence is increasingly applied to advance multi-omics data analytics. Explainable artificial intelligence, exascale computing and climatype associations are essential for designing crop ideotypes. Aligning research with policy is a promising avenue for advancement. 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 10 18 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.