2016: The Year That Deep Learning Took Over the Internet
On the West coast of Australia, Amanda Hodgson is launching drones out towards the Indian Ocean so that they can photograph the water from above. The photos are a way of locating dugongs, or sea cows, in the bay near Perth - part of an effort to prevent the extinction of these endangered marine mammals. The trouble is that Hodgson and her team don’t have the time needed to examine all those aerial photos. There are too many of them - about 45,000 - and spotting the dugongs is far too difficult for the untrained eye. So she’s giving the job to a deep neural network.
Neural networks are the machine learning models that identify faces in the photos posted to your Facebook news feed. They also recognize the questions you ask your Android phone, and they help run the Google search engine. Modeled loosely on the network of neurons in the human brain, these sweeping mathematical models learn all these things by analyzing vast troves of digital data. Now, Hodgson, a marine biologist at Murdoch University in Perth, is using this same technique to find dugongs in thousands of photos of open water, running her neural network on the same open-source software, TensorFlow, that underpins the machine learning services inside Google.