How do you vote? 50 million Google images give a clue

For computers, as for humans, reading and observation are two distinct ways to understand the world, said a researcher

What vehicle is most strongly associated with Republican voting districts? Extended-cab pickup trucks. For Democratic districts? Sedans.
 
Those conclusions may not be particularly surprising. After all, market researchers and political analysts have studied such things for decades.
 
But what is surprising is how researchers working on an ambitious project based at Stanford University reached those conclusions: by analysing 50 million images and location data from Google Street View, the street-scene feature of the online giant’s mapping service. For the first time, helped by recent advances in artificial intelligence, researchers are able to analyse large quantities of images, pulling out data that can be sorted and mined to predict things like income, political leanings and buying habits. In the Stanford study, computers collected details about cars in the millions of images it processed, including makes and models.

 

“All of a sudden we can do the same kind of analysis on images that we have been able to do on text,” said Erez Lieberman Aiden, a computer scientist who heads a genomic research centre at the Baylor School of Medicine. He provided advice on one aspect of the Stanford project.
 
For computers, as for humans, reading and observation are two distinct ways to understand the world, Lieberman Aiden said. In that sense, he said, “computers don’t have one hand tied behind their backs anymore.” Text has been easier for A.I. to handle, because words have discrete characters — 26 letters, in the case of English. That makes it much closer to the natural language of computers than the freehand chaos of imagery. But image recognition technology, much of it developed by major technology companies, has improved greatly in recent years.

 
The Stanford project gives a glimpse at the potential. By pulling the vehicles’ makes, models and years from the images, and then linking that information with other data sources, the project was able to predict factors like pollution and voting patterns at the neighborhood level.  ©2017 The New York Times News Service