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The Conversation: How AI ‘sees’ the world – what happened when we trained a deep learning model to identify poverty

To most effectively deliver aid to alleviate poverty, you have to know where the people most in need are. In many countries, this is often done with household surveys. But these are usually infrequent and cover limited locations.

Recent advances in artificial intelligence (AI) have created a step change in how to measure poverty and other human development indicators. Our team has used a type of AI known as a deep convolutional neural network (DCNN) to study satellite imagery and identify some types of poverty with a level of accuracy close to that of household surveys.

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