Well, except for the sentient, evil part. The comparison has already been made-and given the networks’ superhuman skills, it’s pretty apt. If all of this reminds you of The Terminator’s evil Skynet system, which was designed to locate military hardware before it went sentient and destroyed all of humanity, you’re not alone. In contrast, the humans subjects’ wrong guesses were over 1,400 miles off. 56 percent of the time, PlaNet guessed better than humans-and its wrong guesses were only a median of about 702 miles away from the real location of the images. They pitted it against people to see how well it compared to their best attempts to guess a location. Though not perfect, that’s pretty good for a computer.įinally, researchers put their network to the ultimate test. PlaNet figured out where 3.6 percent were taken with “street-level accuracy,” another 10.1 percent at the city level, 28.4 percent at country level and 48 percent at continent level. Then things got serious: The researchers put in another 2.3 million images with tags and put the network to the test. Some of the images were used to teach the network to figure out where an image fell on the grid of cells, and others were used to validate the initial images. To train the neural network, researchers divided Earth into thousands of geographic “cells,” then input over 100 million geotagged images into the network. It’s called PlaNet, and it uses a photo’s pixels to determine where it was taken. As Julie Morgenstern reports for the MIT Technology Review, a new neural network developed by Google can outguess humans almost every time-even with photos taken indoors. But those days may be drawing to a close. Where was that picture taken? Computers were once at a disadvantage to humans in their ability to use context and memory to deduce an image's location.

Google outguess