Conference: Frontiers in artificial intelligence and machine learning for ecology: 17-20 October 2022

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Ecological and artificial intelligence theories are both about understanding how complex systems work and defining system predictability. This award will support a workshop convening experts at the nexus of data science, artificial intelligence, computer science, and ecology. The aim is to identify aligned research frontiers, refine a common vocabulary, and support cross-fertilization supporting artificial intelligence (AI) and machine learning (ML) development to target wicked challenges in leveraging ecological theory and data assimilation to generate predictive understanding of complex system properties such as ecosystem resilience and pathogen emergence Artificial intelligence and machine learning have been heralded as revolutionary approaches for harnessing big data and generating hypotheses and predictions for complex systems. Traditional theory and process-based approaches in ecology can be powerful tools for learning but are often limited to low-dimensional analyses. Yet ecological systems are complex and multi-dimensional in space, in time, and in networks of interactions - and current directions in ML and AI development are exploring approaches to understand predictability in such complex systems.Despite burgeoning use of machine learning approaches to describe pattern, process, and predictions in ecological systems, there are a paucity of resources for critically evaluating how these approaches can align with ecological theory development and process understanding. Likewise, there are limited opportunities for ecologists to interact with the computer and data scientists developing AI and ML, and these interactions are a critical opportunity for cross-fertilization to explore beyond current practices and refine shared targets. This workshop will produce two published deliverables in widely known forms for both communities that will highlight convergent opportunities for AI and ML development for ecological, ecosystems, computer, and data science audiences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.