Make machines intelligent. Improve people’s lives.
About the team
Research Freedom
Google Brain team members set their own research agenda, with the team as a whole maintaining a portfolio of projects across different time horizons and levels of risk.
Google Scale
As part of Google and Alphabet, the team has resources and access to projects impossible to find elsewhere. Our broad and fundamental research goals allow us to actively collaborate with, and contribute uniquely to, many other teams across Alphabet who deploy our cutting edge technology into products.
Open Culture
We believe that openly disseminating research is critical to a healthy exchange of ideas, leading to rapid progress in the field. As such, we publish our research regularly at top academic conferences and release our tools, such as TensorFlow, as open source projects.
Some Brain papers accepted to NeurIPS, 2019
NeurIPS Spotlight (2019) (to appear)
Advances in Neural Information Processing Systems (NeurIPS) 32, Curran Associates, Inc. (2019), pp. 6861-6871
Our work
Make machines intelligent and improve people’s lives through advancement in the fundamental theory and understanding of machine learning, and through research in the service of product.
Go behind the scenes and meet some of the people on the Google Brain team who are helping shape machine learning itself.
Take a look at our 2017 Reddit AMA, where we talk about creating machines that learn how to learn, enabling people to explore deep learning right in their browsers, Google's custom machine learning TPU chips, and much more.
How Google used artificial intelligence to transform Google Translate, one of its more popular services — and how machine learning is poised to reinvent computing itself.
Research areas
Focus areas
Machine learning algorithms and techniques
The Google Brain team focuses on conducting fundamental research to further advance key areas in machine intelligence and to create a better theoretical understanding of deep learning.
Natural language understanding
We focus on developing learning algorithms that are capable of understanding language to enable machines to translate text, answer questions, summarize documents, or conversationally interact with humans.
Computer systems for machine learning
Key to the success of deep learning in the past few years is that we finally reached a point where we had interesting real-world datasets and enough computational resources to actually train large, powerful models on these datasets. One fruitful way to accelerate machine learning research is to have rapid turnaround time on machine learning experiments, and we have strived to build systems that enable this. Our group has built multiple generations of machine learning software platforms to enable research and production uses of our research.
Machine learning for perception
The goal of the Google Brain team's machine perception efforts is to improve a machine's ability to hear and see so that machines may naturally interact with humans by focusing on building deep learning systems to advance the state of the art and apply ideas to real products. Our long term goal is to make human perception a seamless component of future software systems including mobile devices, robotics and healthcare.
Some of our people
I really enjoy working with colleagues who have a broad range of expertise on cutting-edge machine learning research problems that have the potential of improving the lives of billions of people.
After many years working in academia, it's incredibly exhilarating to see the Brain team transforming Google by combining curiosity-driven research on neural networks with world class engineering.