We create useful solutions to fundamental computational problems with impact on Google’s products and scientific progress.
AI Fundamentals & Applications
Our team works on finding solutions to computational problems in theory and algorithms, machine learning, journalism, speech, and other data-driven disciplines, with impact on Google’s products and scientific progress.
To achieve this double objective, we focus on two tools: software libraries to vehicle the research findings to products and services, and publications to make the work known to the community.
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar
Natalia Ponomareva, Thomas Colthurst, Gilbert Hendry, Salem Haykal, Soroush Radpour
CoRR, vol. abs/1506.03425 (2015)
Proceedings of the 34th International Conference on Machine Learning (ICML 2017). Sydney, Australia, August 2017. (2017)
International Conference on Machine Learning (2017)
NIPS 2017 Workshop: Machine Learning on the Phone
Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, JMLR.org, pp. 344-353
Sashank J. Reddi, Satyen Kale, Sanjiv Kumar
Immanuel Bayer, Xiangnan He, Bhargav Kanagal, Steffen Rendle
Proceedings of 11th ACM International Conference on Web Search and Data Mining (WSDM) (2018)
ACM International Conference on Information and Knowledge Management (2017) (to appear)
Yesterday, we announced the launch of Android Wear 2.0, along with brand new wearable devices, that will run Google's first entirely “on-device” ML technology for powering smart messaging.
Today we are announcing tf.Transform, a library for TensorFlow that allows users to define preprocessing pipelines and run these using large scale data processing frameworks.
That’s why we developed algorithms for Explore in Docs, a collaboration between the Coauthor and Apps teams that uses powerful Google infrastructure, best-in-class information retrieval, machine learning, and machine translation technologies.
We’re making the Fact Check label in Google News available everywhere, and expanding it into Search globally in all languages.
Collaborative Machine Learning without Centralized Training Data
We recently provided many exciting improvements to Gboard for Android, working towards our vision of creating an intelligent mechanism that enables faster input while offering suggestions and correcting mistakes, in any language you choose.
Today we present TensorFlow Lattice, a set of prebuilt TensorFlow Estimators that are easy to use, and TensorFlow operators to build your own lattice models.
To provide better discovery and rich content for books, movies, events, recipes, reviews and a number of other content categories with Google Search, we rely on structured data that content providers embed in their sites using schema.org vocabulary.
Some of our people
Machine learning has already transformed our computational solutions. The future is even more exciting: tackling more complex and more challenging learning problems with modern theoretical and algorithmic advances.