Grammar checking at Google Search scale

Favorite Posted by Eric Malmi, Senior Research Scientist, and Jakub Adamek, Senior Software Engineer, Google, Bard Team Many people with questions about grammar turn to Google Search for guidance. While existing features, such as “Did you mean”, already handle simple typo corrections, more complex grammatical error correction (GEC) is beyond

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Shared by Google AI Technology October 25, 2023

Answering billions of reporting queries each day with low latency

Favorite Posted by Jagan Sankaranarayanan, Senior Staff Software Engineer, and Indrajit Roy, Head of Napa Product, Google Google Ads infrastructure runs on an internal data warehouse called Napa. Billions of reporting queries, which power critical dashboards used by advertising clients to measure campaign performance, run on tables stored in Napa.

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Shared by Google AI Technology October 20, 2023

English learners can now practice speaking on Search

Favorite Posted by Christian Plagemann, Director, and Katya Cox, Product Manager, Google Research Learning a language can open up new opportunities in a person’s life. It can help people connect with those from different cultures, travel the world, and advance their career. English alone is estimated to have 1.5 billion

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Shared by Google AI Technology October 19, 2023

Improving traffic evacuations: A case study

Favorite Posted by Damien Pierce, Software Engineer, and John Anderson, Senior Research Director, Google Research Some cities or communities develop an evacuation plan to be used in case of an emergency. There are a number of reasons why city officials might enact their plan, a primary one being a natural

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Shared by Google AI Technology October 16, 2023

Batch calibration: Rethinking calibration for in-context learning and prompt engineering

Favorite Posted by Han Zhou, Student Researcher, and Subhrajit Roy, Senior Research Scientist, Google Research Prompting large language models (LLMs) has become an efficient learning paradigm for adapting LLMs to a new task by conditioning on human-designed instructions. The remarkable in-context learning (ICL) ability of LLMs also leads to efficient

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Shared by Google AI Technology October 13, 2023

Developing industrial use cases for physical simulation on future error-corrected quantum computers

Favorite Posted by Nicholas Rubin, Senior Research Scientist, and Ryan Babbush, Head of Quantum Algorithms, Quantum AI Team If you’ve paid attention to the quantum computing space, you’ve heard the claim that in the future, quantum computers will solve certain problems exponentially more efficiently than classical computers can. They have

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Shared by Google AI Technology October 12, 2023