Skip to main content

The latest research from Google

Data-centric ML benchmarking: Announcing DataPerf’s 2023 challenges

Machine learning (ML) offers tremendous potential, from diagnosing cancer to engineering safe self-driving cars to amplifying human productivity. To realize this potential, however, organizations need ML solutions to be reliable with ML solution development that is predictable and tractable. The key to both is a deeper understanding of ML data — how to engineer training datasets that produce high quality models and test datasets that deliver accurate indicators of how close we are to solving the target problem.

Leveraging transfer learning for large scale differentially private image classification

PRESTO – A multilingual dataset for parsing realistic task-oriented dialogues

Detecting novel systemic biomarkers in external eye photos

Visual language maps for robot navigation

Vid2Seq: a pretrained visual language model for describing multi-event videos

Responsible AI at Google Research: The Impact Lab

Learning from deep learning: a case study of feature discovery and validation in pathology

PaLM-E: An embodied multimodal language model

The BirdCLEF 2023 Challenge: Pushing the frontiers of biodiversity monitoring

Announcing the ICDAR 2023 Competition on Hierarchical Text Detection and Recognition