Category: Artificial Intelligence

This walkthrough explores how to leverage Amazon Forecast to derive valuable business insights in satellite communications use-cases. Operations teams can quickly see accurate satellite capacity forecasts on a per beam basis. The benefits include lower cost via provisioning just the right amount of bandwidth, and a more streamlined customer experience since users will be less impacted by weather or surge events.

Since wildfires can double in size and intensity every three to five minutes, early detection and reduced response times are essential. Cloud technologies, including artificial intelligence (AI) and machine learning (ML), can help with this. Learn a high-level architecture to create a solution with AWS that uses AI to identify and classify wildfire smoke imagery and then rapidly alert and inform first responders about the location and condition of a fire incident.

GraphQL is a query language and server-side runtime system for application programming interfaces (APIs) that prioritizes giving clients exactly the information they request and no more. GraphQL can help public sector customers focus on their data and provide ways to explore the data in their APIs. Learn a reference architecture using serverless technologies that you can use to build GraphQL-enabled solutions in the AWS GovCloud (US) Regions to unify data access in real-time and simplify operations.

As the daily volumes of document submissions increases for government organizations, intelligent document processing (IDP) solution architectures must absorb spikes in requests without creating delays or other impact for the users. In cases where the processing volume exceeds the limits of the resources available in an AWS Region, organizations can distribute the workloads across multiple regions to increase the document processing throughput. This post presents high-level architecture guidance built around Amazon Comprehend to create a distributed document processing workload that can overcome the challenges of unpredictable request patterns.

What if paper forms could be processed at the same speed as digital forms? What if their contents could be automatically entered in the same database as the digital forms? Medicaid agencies could analyze data in near real time and drive actionable insights on a single dashboard. By using artificial intelligence (AI) and machine learning (ML) services from AWS, Medicaid agencies can create this streamlined solution. In this walkthrough, learn how to extract, analyze, and interpret relevant information from paper-based Medicaid claims forms.

Swindon Borough Council serves a multicultural community of 230,000 citizens in the south of England. We continuously review emerging and evolving technologies, seeking to leverage them to reimagine and improve services, lower costs, and enhance efficiency. The success of a recent translation project that harnesses the power of artificial intelligence (AI) and cloud services is a prime example of this.

In the second episode of the AWS Behind the Cloud series, Dominic Delmolino, vice president of field technology and engineering for AWS worldwide public sector (WWPS) speaks on the big opportunities for generative AI, the importance of customer obsession in building long-term solutions for the public sector, and why it’s important, above all else, to make sure technology is used for good.

This quarter, AWS released 36 new or updated datasets. As July 16 is Artificial Intelligence (AI) Appreciation Day, the AWS Open Data team is highlighting three unique datasets that are analysis-ready for AI. What will you build with these datasets?

Artificial intelligence (AI) and machine learning (ML) technologies are transforming many industries. But although public sector organizations are realizing the benefits of these technologies, there are many remaining challenges, including biases and a lack of transparency, that limit the wider adoption to unlock the full potential of AI and ML. In this post, learn a high-level framework for how AWS can help you address these challenges and provide better outcomes for constituents.

The NYUMets team, led by Dr. Eric Oermann at NYU Langone Medical Center, is collaborating with AWS Open Data, NVIDIA, and Medical Open Network for Artificial Intelligence (MONAI), to develop an open science approach to support researchers to help as many patients with metastatic cancer as possible. With support from the AWS Open Data Sponsorship Program, the NYUMets: Brain dataset is now openly available at no cost to researchers around the world.