The DeepMind Science Team works to leverage AI to tackle key scientific challenges that impact the world. We’ve developed a robust model for detecting and analysing animal populations in field data, and have helped to consolidate data to enable the growing machine learning community in Africa to build AI systems for conservation which, we hope, will scale to other parks. We’ll next be validating our models by deploying them in the field and tracking their progress. Our hope is to contribute towards making AI research more inclusive–both in terms of the kinds of domains we apply it to, and the people developing it. Hence, participating in meetings like Indaba are key for helping build a global team of AI practitioners who can deploy machine learning for diverse projects.
Project credits:
Jean-baptiste Alayrac, Sam Blackwell, Joao Carreira, Reena Chopra, Sander Dieleman, Brian McWilliams, Sofia Miñano, Sanjana Narayanan, Meredith Palmer, Ulrich Paquet, Stig Petersen, Roman Werpachowski, Michal Zielinski.
Additional Credits:
Razia Ahamed, Andrea Banino, Pushmeet Kohli, Drew Purves, Andrew Zisserman
This work was made possible by data from Snapshot Serengeti. Images are available via a Creative Commons Attribution 4.0 International License and can be found here. Please contact Dr Meredith Palmer with data inquiries.
Swanson AB, Kosmala M, Lintott CJ, Simpson RJ, Smith A, Packer C (2015) Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Scientific Data 2: 150026
Source: https://deepmind.com/blog/article/using-machine-learning-to-accelerate-ecological-research
