Google’s machine-learning research efforts traveled down a long road before it was able to pass Go, according to Jeff Dean of Google.
Dean, senior fellow at Google and architect of much of its machine-learning strategy, took attendees at Structure Data 2016 through a short history of Google’s machine-learning program that recently bested the South Korean world champion in the ancient game of Go, thought to be the most complex human game mastered by computers. Machine learning and neural networks started off as pure research for Google back in 2012, but quickly found their way into products such as speech recognition, Dean said. Other groups started to add machine-learning capabilities to their products as they realized the capabilities of the technology, especially in image-related areas.
With the release of Tensorflow last year, Google allowed others outside of the exclusive machine-learning expert community to start playing around with these technologies at different levels, depending on their familiarity with the technology. These capabilities will also become more widely available through Google’s cloud services over time, he said.
Check out the rest of our Structure Data 2016 coverage here, and a video embed of the session follows below: