The Missing Link of Artificial Intelligence – Unsupervised Learning

1087

Google’s cat detector was in some ways a dead end. The recent successes of deep learning are built on software that needs human help to learn—something that limits how far artificial intelligence can go.

In 2012 the world learned of a surprising research project inside Google’s secretive X lab. A giant simulation of three million neurons learned to recognize cats and people in pictures, without human help, just by looking at images taken from YouTube.

Yet Google’s cat detector was in some ways a dead end. The recent successes of deep learning are built on software that needs human help to learn—something that limits how far artificial intelligence can go.

Google’s experiment used an approach known as unsupervised learning, in which software is fed raw data and must figure things out for itself without human help. But although it learned to recognize cats, faces, and other objects, it wasn’t accurate enough to be useful. The boom in research into deep learning and products built on it rests on supervised learning, where the software is provided with data labeled by humans—for example, images tagged with the names of the objects they depict.

Read the source article at MIT Technology Review