The new way scientists are tracking global poverty

A group of Stanford researchers is using satellite imagery and artificial intelligence to track poverty reduction efforts around the world. By combining those satellite images with machine learning -- the ability for machines to learn things without being programmed to -- scientists hope to collect data that could help the U.N. achieve its 2030 goal to eradicate poverty in a cheap and efficient manner. Collecting that data in the past has been difficult for a number of reasons. "Most countries don't collect much data, and scaling up traditional household survey-based data collection efforts would be expensive," Stanford researchers explain in a short video explaining the project. But the researchers suggest that by using "less conventional data sources," such as algorithms and satellite imagery, they can put together an "accurate, inexpensive and scalable method for estimating consumption expenditure and asset wealth."