The researchers say they can use that knowledge to determine the political leanings of a given neighborhood just by looking at the cars on the streets.
Li is an expert in computer vision and deep learning, a type of artificial intelligence in which computers teach themselves to recognize three-dimensional objects in two-dimensional images – computers that see, as she describes them.
The algorithms were trained – or more accurately, they trained themselves – to recognize the make, model and year of every car produced since 1990 in each of more than 50 million Google Street View images from 200 American cities.
The team first had to build by hand an image database of all cars since 1990 – year, make, model, trim packages – and then teach a computer to recognize the subtle differences between cars in partially obscured and odd-angle images.
The algorithm worked fast, taking just two weeks to sort the cars in all 50 million images into 2,657 categories by make, model and year.
Gebru adds, many Street View images are taken in the early morning hours specifically to avoid traffic, providing some consistency to the time of day when the images were taken.
“If you walk around a neighborhood looking at cars, the density of traffic sometimes tells you things as valuable as the types of cars you see on the streets,” Gebru said.