Category: FAANG

Posted by Gabriel Barcik and Duc-Hieu Tran, Research Engineers, Google Research In today’s digital age, smartphones and desktop web browsers serve as the primary tools for accessing news and information. However, the proliferation of website clutter — encompassing complex layouts, navigation elements, and extraneous links — significantly impairs both the reading experience and article navigation. …

Read more “On-device content distillation with graph neural networks”

“Data locked away in text, audio, social media, and other unstructured sources can be a competitive advantage for firms that figure out how to use it“ Only 18% of organizations in a 2019 survey by Deloitte reported being able to take advantage of unstructured data. The majority of data, between 80% and 90%, is unstructured …

Read more “Build a classification pipeline with Amazon Comprehend custom classification (Part I)”

Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be to use word counting or other basic analysis to parse documents, but with the power of Amazon AI and machine learning (ML) tools, we can gather deeper understanding of the content. …

Read more “Visualize an Amazon Comprehend analysis with a word cloud in Amazon QuickSight”

Document AI helps businesses structure document data to automate processes, improve SLAs, reduce costs, and unlock insights by making analysis across documents possible. A critical part of document processing is the ability to customize results for enterprise-specific documents. That’s why we launched Document AI Workbench. Document AI Workbench enables users to customize models for document …

Read more “Document AI Workbench is now powered by generative AI to structure document data faster”

Posted by Matt Barnes, Software Engineer, Google Research Routing in Google Maps remains one of our most helpful and frequently used features. Determining the best route from A to B requires making complex trade-offs between factors including the estimated time of arrival (ETA), tolls, directness, surface conditions (e.g., paved, unpaved roads), and user preferences, which …

Read more “World scale inverse reinforcement learning in Google Maps”

Posted by Matt Barnes, Software Engineer, Google Research Routing in Google Maps remains one of our most helpful and frequently used features. Determining the best route from A to B requires making complex trade-offs between factors including the estimated time of arrival (ETA), tolls, directness, surface conditions (e.g., paved, unpaved roads), and user preferences, which …

Read more “World scale inverse reinforcement learning in Google Maps”

Today, we are excited to announce the simplified Quick setup experience in Amazon SageMaker. With this new capability, individual users can launch Amazon SageMaker Studio with default presets in minutes. SageMaker Studio is an integrated development environment (IDE) for machine learning (ML). ML practitioners can perform all ML development steps—from preparing their data to building, …

Read more “Amazon SageMaker simplifies the Amazon SageMaker Studio setup for individual users”