The revolution of Deep Learning

Artificial Intelligence (AI) is so hot right now. It’s what everyone is talking about. Because it’s going to change the world soon.  Joscha Bach says “We are, right now, in the middle of a revolution known as “the revolution of Deep Learning.”

Computer models allow us to understand minds as causal systems, and to test these models by building systems that begin to make sense of the world on their own.

Deep Learning systems are rapidly improving: Last year, they became actually better than humans in recognizing images from the ImageNet database, and they beat humans at playing PacMan. Earlier this year, a combination of Monte Carlo methods and Deep Learning beat humans at Go, a game that was thought to be out of reach of computers for quite some years to come.

Right now, we are in the middle of a technological revolution, known as “Deep Learning”. Working artificial neural networks have been around since the late 1950ies, and progress on them has been steady and slow. But in the spring of 2012, something extraordinary happened. A team of researchers from Google and Stanford University, lead by Andrew Ng, built a neural network running on 16000 computer cores, and trained it with 10 million randomly selected frames from Youtube.

The network did not have any prior knowledge about what it was looking at, and did not receive any feedback on its results. The network was only looking for structure, for any kind of regularity. Ten million images — that is perhaps ten times the amount of data that a human baby gets to see in the first six months of his life. After three days of training, the researchers could show the system an arbitrary image from the database ImageNet, which contains 22000 different types or objects. In 15.8 percent of the cases, the network would guess the correct image, despite having had no human supervision during training. Its result was 70% better than anything that had existed before, better than any sophisticated handcrafted or learned image recognition software.

The system also received instant internet fame, because it could recognize images of cats with 75% accuracy.

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