Our perspective on deployment-centric, multimodal AI beyond vision and language is now out in Nature Machine Intelligence!

www.nature.com/articles/s42...

Can LLMs do our 'digital dishes'? Or in other words, what are tedious, small tasks that researchers often face and could be automated by LLMs? We've worked on one such task: extracting dataset metadata and converting these to a single format to build a dataset knowledge base. [1/5]

Great read from Michael Pocock: Linking remote sensing, citizen science data and AI could transform environmental monitoring | UK Centre for Ecology & Hydrology www.ceh.ac.uk/news-and-med...

Published last week at the CVPR FGVC Workshop; our paper on "Predicting butterfly species presence from satellite imagery using soft contrastive regularisation".

PDF (with links to data/code) available here:
openaccess.thecvf.com/content/CVPR...

In our latest perspective article, we outline how ML can overcome 4 current obstacles for large-scale, high-resolution monitoring of protected areas.

doi.org/10.1002/2688...

Hope this stimulates the conversation and provides a pathway of how ML research can be applied for monitoring PAs at scale.

🎯 How can we empower scientific discovery in millions of nature photos?

Introducing INQUIRE: A benchmark testing if AI vision-language models can help scientists find biodiversity patterns- from disease symptoms to rare behaviors- hidden in vast image collections.

Thread👇🧵

Using computer vision and #MapReader software, we analysed the loss of field boundaries across the #PeakDistrict Since the 1950s, the White Peak has seen a 12% reduction-551 km lost from an original 4,814 km. A stark reminder of landscape change 📊🌿 storymaps.arcgis.com/stories/5c89... #maps #GIS