Combining Machine Learning & Reasoning for Biodiversity Data Intelligence
Keywords:Environmental Sustainability, Natural Sciences, Agriculture/Food
AbstractThe current crisis in global natural resource management makes it imperative that we better leverage the vast data sources associated with taxonomic entities (such as recognized species of plants and animals), which are known collectively as biodiversity data. However, these data pose considerable challenges for artificial intelligence: while growing rapidly in volume, they remain highly incomplete for many taxonomic groups, often show conflicting signals from different sources, and are multi-modal and therefore constantly changing in structure. In this paper, we motivate, describe, and present a novel workflow combining machine learning and automated reasoning, to discover patterns of taxonomic identity and change - i.e. “taxonomic intelligence” - leading to scalable and broadly impactful AI solutions within the bio-data realm.
How to Cite
Sen, A., Sterner, B., Franz, N., Powel, C., & Upham, N. (2021). Combining Machine Learning & Reasoning for Biodiversity Data Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14911-14919. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17750
AAAI Special Track on AI for Social Impact