How Do You Vote? 50 Million Google Images Give a Clue

["Dave Farber" <farber () gmail com>]

Begin forwarded message:

> From: Dewayne Hendricks <dewayne () warpspeed com>
> Date: January 2, 2018 at 12:34:54 PM EST
> To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com>
> Subject: [Dewayne-Net] How Do You Vote? 50 Million Google Images Give a Clue
> Reply-To: dewayne-net () warpspeed com
>
> [Note:  This item comes from friend Judi Clark.  DLH]
>
> How Do You Vote? 50 Million Google Images Give a Clue
> By STEVE LOHR
> Dec 31 2017
> <https://www.nytimes.com/2017/12/31/technology/google-images-voters.html>
>
> 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 analyzing 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 analyze 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 center 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, Mr. 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.
>
> “This kind of social analysis using image data is a new tool to draw insights,” said Timnit Gebru, who led the 
> Stanford research effort. The research has been published in stages, the most recent in late November in the 
> Proceedings of the National Academy of Sciences.
>
> In the end, the car-image project involved 50 million images of street scenes gathered from Google Street View. In 
> them, 22 million cars were identified, and then classified into more than 2,600 categories like their make and model, 
> located in more than 3,000 ZIP codes and 39,000 voting districts.
>
> But first, a database curated by humans had to train the A.I. software to understand the images.
>
> The researchers recruited hundreds of people to pick out and classify cars in a sample of millions of pictures. Some 
> of the online contractors did simple tasks like identifying the cars in images. Others were car experts who knew 
> nuances like the subtle difference in the taillights on the 2007 and 2008 Honda Accords.
>
> “Collecting and labeling a large data set is the most painful thing you can do in our field,” said Ms. Gebru, who 
> received her Ph.D. from Stanford in September and now works for Microsoft Research.
>
> But without experiencing that data-wrangling work, she added, “you don’t understand what is impeding progress in A.I. 
> in the real world.”
>
> [snip]
>
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