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. The use of this AI technology could help, for example, in developing countries where there has been a rapid change of land use. The AI could monitor via satellite and potentially spot areas that are in need of aid. This would be much quicker than relying on ground surveys. Plus, the dreamy images our deep learning model has produced give us a unique insight into how AI visualises the world. Two villages with different wealth ratings as seen from …