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Responsible AI at Google Research: AI for Social Good

Posted by Jimmy Tobin and Katrin Tomanek, Software Engineers, Google Research, AI for Social Good Google’s AI for Social Good team consists of researchers, engineers, volunteers, and others with a shared focus on positive social impact. Our mission is to demonstrate AI’s societal benefit by enabling real-world value, with projects spanning work in public health, …

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Use the AWS CDK to deploy Amazon SageMaker Studio lifecycle configurations

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). Studio provides a single web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. Lifecycle configurations are shell scripts triggered by Studio lifecycle events, such as starting …

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RNA-Seq and protein structure prediction with BigQuery and Vertex AI

RNA-Seq and protein structure prediction are essential tools in modern biological research, facilitating insights into the molecular mechanisms of diseases and the development of potential therapies. RNA-Seq is a technique for profiling gene expression, enabling researchers to better understand gene regulation and complex interactions between genes. Protein structure prediction, on the other hand, provides information …

Generating Diagrams with ChatGPT

A large language model trained with appropriate content can generate responses more than just English text. ChatGPT, for example, is known to be able to generate code in many programming languages. Indeed, you can make ChatGPT generate other content as well, such as pictures. In this post, you will learn How to make ChatGPT to …

RoboCat: A self-improving robotic agent

Robots are quickly becoming part of our everyday lives, but they’re often only programmed to perform specific tasks well. While harnessing recent advances in AI could lead to robots that could help in many more ways, progress in building general-purpose robots is slower in part because of the time needed to collect real-world training data. Our …

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps

Optimal transport (OT) theory focuses, among all maps that can morph a probability measure onto another, on those that are the “thriftiest”, i.e. such that the averaged cost between and its image be as small as possible. Many computational approaches have been proposed to estimate such Monge maps when is the distance, e.g., using entropic …