Google Research, 2022 & Beyond: Language, Vision and Generative Models

Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the Google Research community Today we kick off a series of blog posts about exciting new developments from Google Research. Please keep your eye on this space and look for the title “Google Research, 2022 & Beyond” for more articles in […]

Who Said What? Recorder’s On-device Solution for Labeling Speakers

Posted by Quan Wang, Senior Staff Software Engineer, and Fan Zhang, Staff Software Engineer, Google In 2019 we launched Recorder, an audio recording app for Pixel phones that helps users create, manage, and edit audio recordings. It leverages recent developments in on-device machine learning to transcribe speech, recognize audio events, suggest tags for titles, and […]

RT-1: Robotics Transformer for Real-World Control at Scale

Posted Keerthana Gopalakrishnan and Kanishka Rao, Google Research, Robotics at Google Major recent advances in multiple subfields of machine learning (ML) research, such as computer vision and natural language processing, have been enabled by a shared common approach that leverages large, diverse datasets and expressive models that can absorb all of the data effectively. Although […]

Accelerating Text Generation with Confident Adaptive Language Modeling (CALM)

Posted by Tal Schuster, Research Scientist, Google Research Language models (LMs) are the driving force behind many recent breakthroughs in natural language processing. Models like T5, LaMDA, GPT-3, and PaLM have demonstrated impressive performance on various language tasks. While multiple factors can contribute to improving the performance of LMs, some recent studies suggest that scaling […]

EHR-Safe: Generating High-Fidelity and Privacy-Preserving Synthetic Electronic Health Records

Posted by Jinsung Yoon and Sercan O. Arik, Research Scientists, Google Research, Cloud AI Team Analysis of Electronic Health Records (EHR) has a tremendous potential for enhancing patient care, quantitatively measuring performance of clinical practices, and facilitating clinical research. Statistical estimation and machine learning (ML) models trained on EHR data can be used to predict […]

Differential Privacy Accounting by Connecting the Dots

Posted by Pritish Kamath and Pasin Manurangsi, Research Scientists, Google Research Differential privacy (DP) is an approach that enables data analytics and machine learning (ML) with a mathematical guarantee on the privacy of user data. DP quantifies the “privacy cost” of an algorithm, i.e., the level of guarantee that the algorithm’s output distribution for a […]

Formation of Robust Bound States of Interacting Photons

Posted by Alexis Morvan and Trond Andersen, Research Scientists, Google Quantum AI When quantum computers were first proposed, they were hoped to be a way to better understand the quantum world. With a so-called “quantum simulator,” one could engineer a quantum computer to investigate how various quantum phenomena arise, including those that are intractable to […]

Private Ads Prediction with DP-SGD

Posted by Krishna Giri Narra, Software Engineer, Google, and Chiyuan Zhang, Research Scientist, Google Research Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. With increasing focus on online privacy, there’s an opportunity to identify ML algorithms […]

Google at EMNLP 2022

Posted by Malaya Jules, Program Manager, Google This week, the premier conference on Empirical Methods in Natural Language Processing (EMNLP 2022) is being held in Abu Dhabi, United Arab Emirates. We are proud to be a Diamond Sponsor of EMNLP 2022, with Google researchers contributing at all levels. This year we are presenting over 50 […]

Will You Find These Shortcuts?

Posted by Katja Filippova, Research Scientist, and Sebastian Ebert, Software Engineer, Google Research, Brain team Modern machine learning models that learn to solve a task by going through many examples can achieve stellar performance when evaluated on a test set, but sometimes they are right for the “wrong” reasons: they make correct predictions but use […]