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The latest research from Google

Private Ads Prediction with DP-SGD

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 that have better privacy-utility trade-offs. Differential privacy (DP) has emerged as a popular framework for developing ML algorithms responsibly with provable privacy guarantees. It has been extensively studied in the privacy literature, deployed in industrial applications and employed by the U.S. Census. Intuitively, the DP framework enables ML models to learn population-wide properties, while protecting user-level information.

Google at EMNLP 2022

Will You Find These Shortcuts?

Talking to Robots in Real Time

Making a Traversable Wormhole with a Quantum Computer

Better Language Models Without Massive Compute

Google at NeurIPS 2022

Conversation Summaries in Google Chat

The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation

Mixture-of-Experts with Expert Choice Routing

Characterizing Emergent Phenomena in Large Language Models