Mineralogy meets zero-shot computer vision

Identifying minerals is a complex and time-consuming problem for geologists, often taking anywhere from 30 minutes to several days per sample. Further complicating the situation is the fact that a sufficient portion of minerals remain inadequately researched, leaving us with just a few hundred comprehensively characterized out of the 6,000 currently identified minerals.

It’s All About What’s New in Back-to-School Marketing

Back to school is a time of new beginnings. But, what back-to-school marketing campaigns inspire the most sales? It’s a lucrative shopping season as students stock up on new clothes, school supplies, dorm room accessories, and more. According to the National Retail Federation (NRF), back-to-school (K-12) spending is expected to hit $41.5 billion in 2023. …

2018 Call for Code Winner Project OWL advances its natural disaster communication network

For disaster-prone areas, fragile connectivity remains a major problem, often going offline in critical moments. Aerospace enterprises face a similar challenge when trying to run consistent high-altitude connectivity while operating in remote locations, which can also be very expensive. This is where Project OWL comes into play: developing new technologies to help address these challenges. Formed …

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How to compare a noisy quantum processor to a classical computer

Posted by Sergio Boixo and Vadim Smelyanskiy, Principal Scientists, Google Quantum AI Team A full-scale error-corrected quantum computer will be able to solve some problems that are impossible for classical computers, but building such a device is a huge endeavor. We are proud of the milestones that we have achieved toward a fully error-corrected quantum …

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Announcing the Preview of Amazon SageMaker Profiler: Track and visualize detailed hardware performance data for your model training workloads

Today, we’re pleased to announce the preview of Amazon SageMaker Profiler, a capability of Amazon SageMaker that provides a detailed view into the AWS compute resources provisioned during training deep learning models on SageMaker. With SageMaker Profiler, you can track all activities on CPUs and GPUs, such as CPU and GPU utilizations, kernel runs on …

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Cloud Storage FUSE is now optimized for GKE and AI workloads

Google Cloud’s Cloud Storage is home to reams of training data, models and checkpoints that you need to train and serve AI workloads, delivering the scale, performance, simplicity and cost-effectiveness that are the hallmarks of a cloud storage system. But when it comes time for an AI workload to actually access that data, it isn’t …