Scientist use Artificial Intelligence Algorithm to determine neighborhood’s political leanings by its cars

Stanford Artificial Intelligence Lab and the Stanford Vision Lab has come up with a new computer vision AI algorithm to examine millions of Google Street View images to measure whether and how urban areas are changing and their political leanings.

The US spends more than $250 million each year on (American Community Survey), a labor-intensive door-to-door study that measures statistics relating to  education, occupation, race, gender, unemployment, and other demographic factors

Fei-Fei Li, an associate professor of computer science at Stanford said this Artifical Intellgience algorthm could save billions of dollars for US government on census spend in next six years.

“Using easily obtainable visual data, we can learn so much about our communities, on par with some information that takes billions of dollars to obtain via census surveys. More importantly, this research opens up more possibilities of virtually continuous study of our society using sometimes cheaply available visual data,”– he said.

The Artifical Intelligence Algorithm was designed to train themselves in recognizing the manufacturer, model and year of every car produced since 1990 in each of more than 50 million Google Street View images from 200 American Cities.

Scientists say they can use that knowledge to determine the socioeconomic statistics and political leanings of a given neighborhood just by looking at the cars on the streets.

” If the number of sedans in a neighborhood is greater than the number of pickups, there is an 88 percent chance that the precinct will vote Democratic. Transpose those numbers to have more pickups than sedans and there is an 82 percent chance a precinct will vote Republican”– li said.

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“We successfully detected 22 million distinct vehicles, comprising 32{4c121b525f261b6e9cd1def8a3985dbe850ad058af7b7776b0968f5bd69c65ba} of all of the vehicles in the 200 cities we studied and 8{4c121b525f261b6e9cd1def8a3985dbe850ad058af7b7776b0968f5bd69c65ba}8{4c121b525f261b6e9cd1def8a3985dbe850ad058af7b7776b0968f5bd69c65ba} of all vehicles in the United States. After localizing each vehicle, we deployed CNN (13, 14), the most successful deep learning algorithm to date for object classification, to determine the make, model, body type, and year of each vehicle” —  he added.

Li also said this technology will be very helpful in understanding the society and nation-building.

“It can help us understand how our society works, the things people need and how we can improve lives. There is great potential to use computer vision technology in a constructive and benevolent way.” — he added.

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