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January 2018
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Using AI To Identify Car Models In 50 Million Google Street Views Reveals A Wide Range Of Demographic Information

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Google Street View is a great resource for taking a look at distant locations before travelling, or for visualizing a nearby address before driving there. But Street View images are much more than vivid versions of otherwise flat maps: they are slices of modern life, conveniently sorted by geolocation. That means they can provide all kinds of insights into how society operates, and what the differences are geographically. The tricky part is extracting that information. An article in the New York Times reports on how researchers at Stanford University have applied artificial intelligence (AI) techniques to 50 million Google Street View images taken in 200 US cities. Since analyzing images of people directly is hard and fraught with privacy concerns, the researchers concentrated on a proxy: cars. As an academic paper published by the Stanford team notes (pdf):

Ninety five percent of American households own automobiles, and as shown by prior work cars are a reflection of their owners' characteristics providing significant personal information.
First, the AI system had to be trained to find cars in the Google Street Map images. That's something that's easy for humans to do, but hard for computers, while the next stage of the work -- identifying car models -- is much easier using AI. As another paper reporting on the research (pdf) explains:
the fine-grained object recognition task we perform here is one that few people could accomplish for even a handful of images. Differences between cars can be imperceptible to an untrained person; for instance, some car models can have subtle changes in tail lights (e.g., 2007 Honda Accord vs. 2008 Honda Accord) or grilles (e.g., 2001 Ford F-150 Supercrew LL vs. 2011 Ford F-150 Supercrew SVT). Nevertheless, our system is able to classify automobiles into one of 2,657 categories, taking 0.2 s per vehicle image to do so. While it classified the automobiles in 50 million images in 2 wk, a human expert, assuming 10 s per image, would take more than 15 y to perform the same task.
The difference between the two weeks taken by the AI software, and the 15 years a human would need, means that it is possible to analyze much larger data collections than before, and to extract new kinds of information. This is done by using existing datasets, for example the American Community Survey, which is performed by the US Census Bureau each year, to train the AI system to spot correlations between cars and demographics. The New York Times article lists some of the results that emerge from mining and analyzing the Google Street Map images, and adding in metadata from other sources:
The system was able to accurately predict income, race, education and voting patterns at the ZIP code and precinct level in cities across the country.Car attributes (including miles-per-gallon ratings) found that the greenest city in America is Burlington, Vt., while Casper, Wyo., has the largest per-capita carbon footprint.Chicago is the city with the highest level of income segregation, with large clusters of expensive and cheap cars in different neighborhoods; Jacksonville, Fla., is the least segregated by income.New York is the city with the most expensive cars. El Paso has the highest percentage of Hummers. San Francisco has the highest percentage of foreign cars.
The researchers point out that the rise of self-driving cars with on-board cameras will produce even more street images that could be fed into AI systems for analysis. They also note that walking around a neighborhood with a camera -- for example, in a smartphone -- would allow image data to be gathered very simply and cheaply. And as AI systems become more powerful, it will be possible to extract even more demographic information from apparently innocuous street views. Although that may be good news for academic researchers, datamining offline activities clearly creates new privacy problems at a time when people are already worried about what can be gleaned from datamining their online activities.Follow me @glynmoody on Twitter or identi.ca, and +glynmoody on Google+

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Community Backlash Leads Adult Diaper Company To Drop Its Trademark Application for 'ABDL'

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When a company goes down a wrong or abusive road regarding trademark rights, the public has a lot of tools for remedy. Legal disputes between interested parties can often times correct a company attempting to secure trademark rights it ought not have. Invalidating a trademark that never should have been granted is another tool. But often times, the best and quickest remedies can come from the public itself in the form of a good old fashioned backlash.The likelihood of such a backlash is necessarily a function of the devotion of a particular fanbase. The craft beer industry has had to learn this lesson several times, with a portion of the public devoted to seeing the industry thrive also being unwilling to let stand aggressive trademark bullying that threatens that same industry. We saw another of these backlash instances cause a company to reverse course recently and I struggle to think of a more potentially devoted fanbase to an industry than those among us whose fetish is role-playing as adult babies.

A company that makes diapers for the adult baby/diaper lover fetish community (known as ABDL) gave up on its attempt to trademark the term “ABDL” on Thursday after message boards for the community exploded in anger last week.Rearz, a Canadian-based supplier of adult diapers with cutesy patterns and other adult baby accessories, like pacifiers, told BuzzFeed News, “we had no malicious or strange intentions in trying to register it, but obviously it struck a nerve with people. This is a community we love and serve, and we don't want to make people feel less valuable.”
It will be both tempting and facile for our comments section to devolve into opinions about this specific fetish, but that is entirely besides the point. The real story here is that a company attempted to register a trademark that is essentially the identity of an entire community which it serves and was immediately slapped around by that same community. It seems that many of the same folks that enjoy wearing diapers as adults for any reason other than necessity were also perfectly willing to let Rearz know that trademarking their communal identity would not be tolerated. Boycotts were threatened with promises to patronize other makers of these products, which, yes, this is an industry with multiple players.As is typical, Reddit communities led the way.
Rearz filed to trademark “ABDL” in October 2017, but it was only this week that someone in the community noticed. At this discovery, the /r/ABDL subreddit filled with angry threads about Rearz’s trademark filings. “This is scummy. Period,” wrote one user. In another thread, angry ABDL redditors planned to ruin Rearz’s standing on Facebook by rating it one star on its business page. On a forum for adult babies called ADISC.org, one adult baby said, “Rearz is now off my shopping list.” People even made memes about the scandal.
In rescinding its trademark application, Rearz went on to post its reasoning for applying for in the first place on its blog. That reasoning had mostly to do with the company's complaints about certain online ads and online payments not being accepted due to the products' stigmitized status in popular culture. What a trademark for "ABDL" would do to correct any of that is a question nobody seems interested in answering, but Rearz's claim that it would not enforce its trademark against competition if it had received it doesn't pass the smell test. Even if that were true, it would mean losing the trademark to genericide.But, in the end, the community Rearz served did all of that work long before the legal system had a chance to swing the bat. If nothing else, this ought to show the rest of the public what a good old fashioned backlash can do to correct poor trademark behavior.

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