Responsible AI at Google Research: PAIR
Posted by Lucas Dixon and Michael Terry, co-leads, PAIR, Google Research PAIR (People + AI Research) first launched in 2017 with the belief that “AI can go much further —...
Using reinforcement learning for dynamic planning in open-ended conversations
Posted by Deborah Cohen, Staff Research Scientist, and Craig Boutilier, Principal Scientist, Google Research As virtual assistants become ubiquitous, users increasingly interact with them to learn about new topics or...
Larger language models do in-context learning differently
Posted by Jerry Wei, Student Researcher, and Denny Zhou, Principal Scientist, Google Research There have recently been tremendous advances in language models, partly because they can perform tasks with strong...
Consensus and subjectivity of skin tone annotation for ML fairness
Posted by Candice Schumann, Software Engineer, and Gbolahan O. Olanubi, User Experience Researcher, Google Research Skin tone is an observable characteristic that is subjective, perceived differently by individuals (e.g., depending...
Leveraging transfer learning for large scale differentially private image classification
Posted by Harsh Mehta, Software Engineer, and Walid Krichene, Research Scientist, Google Research Large deep learning models are becoming the workhorse of a variety of critical machine learning (ML) tasks....
Data-centric ML benchmarking: Announcing DataPerf’s 2023 challenges
Posted by Peter Mattson, Senior Staff Engineer, ML Performance, and Praveen Paritosh, Senior Research Scientist, Google Research, Brain Team Machine learning (ML) offers tremendous potential, from diagnosing cancer to engineering...
Scaling vision transformers to 22 billion parameters
Posted by Piotr Padlewski and Josip Djolonga, Software Engineers, Google Research Large Language Models (LLMs) like PaLM or GPT-3 showed that scaling transformers to hundreds of billions of parameters improves...
Pre-trained Gaussian processes for Bayesian optimization
Posted by Zi Wang and Kevin Swersky, Research Scientists, Google Research, Brain Team Bayesian optimization (BayesOpt) is a powerful tool widely used for global optimization tasks, such as hyperparameter tuning,...
How Project Starline improves remote communication
Posted by Greg Blascovich and Eric Gomez, User Researchers, Google As companies settle into a new normal of hybrid and distributed work, remote communication technology remains critical for connecting and...
