AI now being trained by nature to spot climate crisis clues
An international team of academics hopes this could help track and protect species considered to be the most over the coming years.
Researchers at the University of Edinburgh, University College London [UCL], UMass Amherst, iNaturalist and the Massachusetts Institute of Technology [MIT] have collaborated on the INQUIRE image tool. According to an official statement, the technology can conduct millions of individual analyses of picture content, and is capable of focusing on key pointers as to changes in diet, migratory patterns, population health, interspecies interactions and relationships.
By assessing records kept on databases fed by citizen science submissions, the INQUIRE tool will have access to an almost infinite archive of images of the natural world. Assessing these for specific ‘clues’, scientists believe they will be better placed to monitor the effects of the climate crisis, and more extreme weather conditions, on the natural world.
‘The thousands of wildlife photos uploaded to the internet each day provide scientists with valuable insights into where different species can be found on Earth. However, knowing what species is in a photo is just the tip of the iceberg,’ said Dr Oisin Mac Aodha, Reader in Machine Learning. ‘These images are potentially a hugely rich resource that remains largely untapped. Being able to quickly and accurately comb through the wealth of information they contain could offer vital clues about how species are responding to multi-faceted challenges like climate change.’
‘This careful curation of data, with a focus on capturing real examples of scientific inquiries across research areas in ecology and environmental science, has proven vital to expanding our understanding of the current capabilities of current AI methods in these potentially impactful scientific settings,’ added Dr Sarah Beery, Assistant Professor at MIT. ‘It has also outlined gaps in current research that we can now work to address, particularly for complex compositional queries, technical terminology, and the fine-grained, subtle differences that delineate categories of interest for our collaborators.’
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Image: Henry Be via Unsplash
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