In the quest for ever-smarter artificial intelligence, it's easy to let hype get ahead of performance.
Perhaps this is not precisely what Turing had in mind.
But in what is being hailed as a triumph in machine learning, Google researchers turned 16,000 processors loose on 10 million thumbnails from YouTube videos to see what they could (machine) learn. This is a vast data set that's an order of magnitude larger than what had been attempted before, according to The New York Times.
What they found was that even without humans training the computers to know certain objects ("This is a cat"), the machines were able to teach themselves the features of a cat face, as you can dimly see above, among many other objects. As one of the researchers told The Times, "[The system] basically invented the concept of a cat" by looking at all those photos and looking for patterns.
It's an impressive feat, but this is a field that moves slower than its hype (even though its achievements are very real and significant). If we look at the Google researchers' paper, we find that if you show their system a random picture from a database of images, its accuracy is about 16 percent. That's a 70 percent improvement over the state of the art, but it's worth considering what that says about the state of the art.