We do this through deep learning research, a subfield of machine learning, focusing on building highly flexible models that learn their own features, end-to-end, and make efficient use of data and computation. This is practically useful for the world: we’ve already deployed our deep learning models across many Google products, and are exploring applying this approach to many different problems, including in spaces like healthcare. Our expertise in systems also allows us to build tools to accelerate ML research and unlock its practical value for everyone.
Researchers on the Brain team have the freedom to set their own research agendas and determine their own level of engagement with existing products, choosing between doing more basic, methodological research or more applied research as necessary to produce the most compelling results. Because many of the advances we develop today may take years to become useful, the team as a whole maintains a portfolio of projects across this spectrum. Our philosophy is that making substantive progress on hard applications can help drive and sharpen the research questions we study, and in turn, scientific breakthroughs can spawn entirely new applications that would be unimaginable today.