Google’s office in Zurich will be home to a new dedicated research group focused on advancing the company’s ambitions in the machine-intelligence arena.
The group will work on finding ways to improve the company’s machine-learning infrastructure and developing products that put the technology to practical use. As part of the machine-learning research, the group will work on improving Google’s capabilities in areas like language, speech, translation and visual processing.
In addition, the group will also focus on natural language processing and “machine perception,” a term Google applies to the task of teaching machines how to make sense of images, music, video and sounds. Features like the content-based search in Google Photos and the support for natural handwriting interfaces in Android are a result of the company’s machine-perception efforts.
“Europe is home to some of the world’s premier technical universities, making it an ideal place to build a top-notch research team,” Emmanuel Mogenet, head of Google Research, Europe, said in a blog post. Google Research, Europe, will enable software engineers to conduct research that aligns with the company’s wider efforts around machine intelligence, Mogenet said.
The term “machine learning” refers to the ability for computers to learn new things for themselves from past behavior and data.
Google is betting that this will play a central role in the technologies and services it delivers in future.
Many of its most used applications, including Google Translate, Photo Search and Smart Reply for Inbox, are already based on machine-learning techniques.
In comments to shareholders earlier this year, Google CEO Sundar Pichai described a strategic vision for the company that relies substantially on machine-intelligence technologies. Pichai touted the AlphaGo system from Google DeepMind, which beat one of the world’s best players at the Chinese board game of Go this March as one example of the company evolving smarts around machine intelligence.
Google’s investments in machine learning have already made the company’s search, translation, spam-filtering and other services much more powerful and user-friendly, Pichai noted at the time. The goal is to build on these capabilities to eventually make Google Search more like a smart digital assistant capable of providing richer and more contextual information, Pichai said.
As in other areas, Google is by no means alone in its pursuit of more sophisticated machine-learning skills.
Social media giant Facebook, for instance, relies heavily on machine-learning models for tasks like ranking and personalizing stories on its News Feed, identifying trending topics, and weeding out offensive and illicit content. Earlier this year, the company announced a new platform dubbed FBLearner Flow that it says will make things much easier for its software engineers to develop sophisticated machine-learning models using algorithms and techniques already developed by others in the company.
IBM’s Watson chess-playing super computer and Microsoft’s Azure Machine Learning Studio are two other examples of how technology vendors have begun leveraging advances in machine learning to deliver new products and services.