[placeholder - tbd] A white paper discussing concrete issues where governments can partner with industry and academia to clarify expectations about AI’s application on a context-specific basis.
Through research, engineering, and initiatives to build the AI ecosystem, we’re working to use AI to address societal challenges.
Machine learning-based forecasts may one day help deploy emergency services and inform evacuation plans for areas at risk of an aftershock.
Introducing Dataset Search, a new tool that enables scientists, data journalists, data geeks, or anyone else to easily find datasets stored in thousands of repositories across the web.
We’re excited to announce the public alpha of Cirq, an open-source quantum framework for building and experimenting with noisy intermediate scale quantum (NISQ) algorithms on near-term quantum processors.
We released a research framework for fast prototyping of reinforcement learning algorithms, which we hope will empower researchers to explore new ideas.
Google Duplex is a new technology for conducting natural conversations to carry out “real-world” tasks over the phone.
By applying deep learning to de-identified electronic health records, our research shows that we can make a broad set of predictions relevant to hospitalized patients.
Learn more about and how companies, nonprofits, researchers and developers are using our open-source machine learning library to solve all kinds of problems.
Recent research publications
We publish hundreds of research papers each year and present our work in a wide range of venues.
Nature Communications, vol. 9 (2018), pp. 4812
The Astronomical Journal, vol. 155 (2018), pp. 94
We’re fostering a collaborative ecosystem with open-source tools, public datasets, and APIs that allow all of us to make the most of machine learning.