Platonic ideal: This composite image represents the ideal stimulus that Googles neural network recognizes as a cat face. Credit: Google

This summer Google set a new landmark in the field of artificial intelligence with software that learned how to recognize cats, people, and other things simply by watching YouTube videos (see "Self-Taught Software"). That technology, modeled on how brain cells operate, is now being put to work making Google's products smarter, with speech recognition being the first service to benefit.

Google's learning software is based on simulating groups of connected brain cells that communicate and influence one another. When such a neural network, as it's called, is exposed to data, the relationships between different neurons can change. That causes the network to develop the ability to react in certain ways to incoming data of a particular kindand the network is said to have learned something.

Neural networks have been used for decades in areas where machine learning is applied, such as chess-playing software or face detection. Google's engineers have found ways to put more computing power behind the approach than was previously possible, creating neural networks that can learn without human assistance and are robust enough to be used commercially, not just as research demonstrations.

The company's neural networks decide for themselves which features of data to pay attention to, and which patterns matter, rather than having humans decide that, say, colors and particular shapes are of interest to software trying to identify objects.

Google is now using these neural networks to recognize speech more accurately, a technology increasingly important to Google's smartphone operating system, Android, as well as the search app it makes available for Apple devices (see "Google's Answer to Siri Thinks Ahead"). "We got between 20 and 25 percent improvement in terms of words that are wrong," says Vincent Vanhoucke, a leader of Google's speech-recognition efforts. "That means that many more people will have a perfect experience without errors." The neural net is so far only working on U.S. English, and Vanhoucke says similar improvements should be possible when it is introduced for other dialects and languages.

Other Google products will likely improve over time with help from the new learning software. The company's image search tools, for example, could become better able to understand what's in a photo without relying on surrounding text. And Google's self-driving cars (see "Look, No Hands") and mobile computer built into a pair of glasses (see "You Will Want Google's Goggles") could benefit from software better able to make sense of more real-world data.

The new technology grabbed headlines back in June of this year, when Google engineers published results of an experiment that threw 10 million images grabbed from YouTube videos at their simulated brain cells, running 16,000 processors across a thousand computers for 10 days without pause.

Average features: This composite image represents the ideal stimulus for Google's software to recognize a human face. Credit: Google

Read the original:

Google Puts Its Virtual Brain Technology to Work