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Stefano Ermon (Stanford): Measuring Progress Towards Sustainable Development Goals with Machine Learning
Stefano Ermon

Fri Dec 08 03:30 PM -- 04:00 PM (PST) @

Recent technological developments are creating new spatio-temporal data streams that contain a wealth of information relevant to sustainable development goals. Modern AI techniques have the potential to yield accurate, inexpensive, and highly scalable models to inform research and policy. As a first example, I will present a machine learning method we developed to predict and map poverty in developing countries. Our method can reliably predict economic well-being using only high-resolution satellite imagery. Because images are passively collected in every corner of the world, our method can provide timely and accurate measurements in a very scalable end economic way, and could revolutionize efforts towards global poverty eradication. As a second example, I will present some ongoing work on monitoring food security outcomes.

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Stefano Ermon (Stanford)

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