You probably use machine learning technology dozens of times a day without knowing it—it’s a way of training computers on real-world data, and it enables high-quality speech recognition, practical computer vision, email spam blocking and even self-driving cars. But it’s far from perfect—you’ve probably chuckled at poorly transcribed text, a bad translation or a misidentified image. We believe machine learning could be far more accurate, and that smarter computers could make everyday tasks much easier. So our research team has been working on some new approaches to large-scale machine learning.

Today’s machine learning technology takes significant work to adapt to new uses. For example, say we’re trying to build a system that can distinguish between pictures of cars and motorcycles. In the standard machine learning approach, we first have to collect tens of thousands of pictures that have already been labeled as “car” or “motorcycle”—what we call labeled data—to train the system. But labeling takes a lot of work, and there’s comparatively little labeled data out there.

Fortunately, recent research on self-taught learning (PDF) and deep learning suggests we might be able to rely instead on unlabeled data—such as random images fetched off the web or out of YouTube videos. These algorithms work by building artificial neural networks, which loosely simulate neuronal (i.e., the brain’s) learning processes.

Neural networks are very computationally costly, so to date, most networks used in machine learning have used only 1 to 10 million connections. But we suspected that by training much larger networks, we might achieve significantly better accuracy. So we developed a distributed computing infrastructure for training large-scale neural networks. Then, we took an artificial neural network and spread the computation across 16,000 of our CPU cores (in our data centers), and trained models with more than 1 billion connections.

via Using large-scale brain simulations for machine learning and A.I. | Official Google Blog.