Google to bring AI for biodiversity research to TensorFlow Hub

Machine learning algorithms abound in biodiversity research, but sometimes without the proper attribution or oversight. In an effort to raise the academic bar, Google says it will release an AI workflow for institutions, developed in collaboration with Global Biodiversity Information Facility (GBIF), iNaturalist, and Visipedia. Researchers at the tech giant say the workflow will support data aggregation and collaboration across teams while ensuring corpora follow standardized licensing terms, use compatible file formats, and provide fair and sufficient data coverage for the task at hand. "The promise of machine learning for species identification is coming to fruition, revealing its transformative potential in biodiversity research," wrote visiting faculty Serge Belongie and Google Research engineering director Hartwig Adam in a blog post published to coincide with the Biodiversity Next conference in Leiden, Netherlands. "International workshops … feature competitions to develop top performing classification algorithms for everything from wildlife camera trap images to pressed flower specimens on herbarium sheets. The encouraging results that have emerged from these competitions inspired us to expand the availability of biodiversity datasets and ML models from workshop-scale to global-scale."