In the 1990s work on machine learning shifted from a knowledge-driven approach to a data-driven approach. Scientists began creating programs for computers to analyze large amounts of data and draw conclusions — or “learn” — from the results.
And in 1997, IBM’s Deep Blue shocked the world by beating the world champion at chess.
The term “deep learning” was coined in 2006 by Geoffrey Hinton to explain new algorithms that let computers “see” and distinguish objects and text in images and videos.
Four years later, in 2010 Microsoft revealed their Kinect technology could track 20 human features at a rate of 30 times per second, allowing people to interact with the computer via movements and gestures. The follow year IBM’s Watson beat its human competitors at Jeopardy.
Google Brain was developed in 2011 and its deep neural network could learn to discover and categorize objects much the way a cat does. The following year, the tech giant’s X Lab developed a machine learning algorithm that is able to autonomously browse YouTube videos to identify the videos that contain cats.
In 2014, Facebook developed DeepFace, a software algorithm that is able to recognize or verify individuals on photos to the same level as humans can.
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