You Don't Have to Be Google to Build an Artificial Brain

When Google used 16,000 machines to build a simulated brain that could correctly identify cats in YouTube videos, it signaled a turning point in the art of artificial intelligence. Applying its massive cluster of computers to an emerging breed of AI algorithm known as "deep learning," the so-called Google brain was twice as accurate as any previous system in recognizing objects pictured in digital images, and it was hailed as another triumph for the mega data centers erected by the kings of the web. "The research is representative of a new generation of computer science that is exploiting the falling cost of computing and the availability of huge clusters of computers in giant data centers," The New York Times wrote in 2012, "leading to significant advances in areas as diverse as machine vision and perception, speech recognition, and language translation." Indeed, in the two years since, Microsoft released a Skype service that uses deep learning to instantly translate conversions from one language to another, Facebook hired one of the leading experts in the field to boost image recognition and other tools on its service, and everyone from Twitter to Yahoo snapped up their own deep learning startups. But in the middle of this revolution, a researcher named Alex Krizhevsky showed that you don't need a massive computer cluster to benefit from this technology's unique ability to "train itself" as it analyzes digital data.