Google recently established its research group facility in Zurich, Switzerland as a primary location for studies that will explore the future of Machine Learning (ML) technology.
The Zurich facility was the previous leading innovator behind Knowledge Graph, a tech that further improved search queries made on Google, and the Google Assistant in messaging app Allo, an AI used to "keep conversations flowing" by suggesting quick replies based on the conversation's context.
Through its latest initiative, Google plans to create a centralized venue for software engineers and machine language researchers to collaborate and develop ideas that further enhance the available technology today.
In addition to the current projects the European-based research group is working on, ML topics will be enlisted as its top priority, focusing primarily on three areas: Machine Intelligence, Natural Language Processing and Understanding, and Machine Perception.
Machine Intelligence explores data sets that contribute to a machine's learning processes in terms of "deep learning and classical algorithms." As the digital environment is constantly evolving and rapidly changing with each second, machines have to take in great amounts of varying data that require a certain amount of "computation capacity."
Google hopes to contribute to a larger academic community by offering its own studies and publications acquired from "combinations of techniques" in theories and application, placing "learning systems" at the "core of interactive services."
Natural Language Processing and Understanding research, on the other hand, delves into the basic building blocks of language across domains to help machines understand human speech on a wider scale. Google's systems are employed in "numerous ways," and thus, require the company's algorithms to adapt efficiently on larger scales.
"Recent work has focused on incorporating multiple sources of knowledge and information to aid with analysis of text, as well as applying frame semantics at the noun phrase, sentence, and document level," the company's publication writes.
Lastly, research in Machine Perception tackles an AI's ability to understand the different underlying symbols in an image, sound, music or video. The machine has to understand the objective and subjective meanings of these media for applications in scenarios such as "content-based search in Google Photos and Image Search, natural handwriting interfaces for Android, optical character recognition for Google Drive documents, and recommendation systems that understand music and YouTube videos."
Google's European Research Group for Machine Learning "will actively research ways in which to improve [machine learning] infrastructure, broadly facilitating research for the community, and enabling it to be put to practical use."
Photo: Dudley Carr | Flickr
