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

New ways to get inspired with generative AI in Search

Favorite We’re testing new ways to get started on something you need to do — like creating an image that can bring an idea to life, or a written draft when you need a starting po… View Original Source (blog.google/technology/ai/) Here.

Demand more from social with AI-powered ads

Favorite Learn how Demand Gen campaigns can help you drive better results across YouTube and Google. See new case studies, videos, tips, and more. View Original Source (blog.google/technology/ai/) Here.

SANPO: A Scene understanding, Accessibility, Navigation, Pathfinding, & Obstacle avoidance dataset

Favorite Posted by Sagar M. Waghmare, Senior Software Engineer, and Kimberly Wilber, Software Engineer, Google Research, Perception Team As most people navigate their everyday world, they process visual input from the environment using an eye-level perspective. Unlike robots and self-driving cars, people don’t have any “out-of-body” sensors to help guide

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Shared by Google AI Technology October 9, 2023

Scalable spherical CNNs for scientific applications

Favorite Posted by Carlos Esteves and Ameesh Makadia, Research Scientists, Google Research, Athena Team Typical deep learning models for computer vision, like convolutional neural networks (CNNs) and vision transformers (ViT), process signals assuming planar (flat) spaces. For example, digital images are represented as a grid of pixels on a plane.

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Shared by Google AI Technology October 4, 2023