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Deep Learning Machine Uses Mobile Photos To Track Pollution

There are days when you can’t wait to leave the office and get some air, thinking it will clear your thoughts and give you a bit of energy. Unknowingly, you could do more harm than good. Unless you live in China or India, it’s difficult to get a sense of the quantity of air pollution you’re exposing yourself to. Project AirTick plans to change all that by using your mobile phone and artificial intelligence.

AirTick mobile app can pinpoint if you’re living in a danger zone by taking meta data from your smartphone’s pictures (time and location). From that point on, it can cross reference with official state data and estimate what you’re dealing with. In time, they expect a Deep Boltzmann machine to aquire sufficient data to build an algorithm that could trace air pollution in real time, just by using user photos.

The project’s founders are confident this is a viable solution, since air sensors are too expensive for the average citizen. If you think it over, at least China and India are in desperate need of an alternative to bottled Canadian air for their people. It’s outrageous to think that they’re forced to leave their country or resort to imported healthy air because pollution is getting worse by the day.  Last year, in December, schools in northeastern China were closed due to toxic smog that threatened the lives of millions for three consecutive days.

Believe it or not, a 2015 study showed there are 4.400 deaths daily with one common denominator – air pollution.

 

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