Category: Amazon Comprehend

Amazon Web Services (AWS) offers a variety of options for building chat-based assistants with generative artificial intelligence (AI) capabilities. The goal of this post is to present in simple words some of these options and what to keep in mind to decide which to use and how to get started.

Federal agencies typically collect, manage, use, and distribute a wide array of documents. Storing and distributing federal agency documents is often a complicated process; documents can range from structured formats to free-flowing documentation with personal identifiable information (PII) that needs careful redaction. And because federal agencies cover a wide breadth of domains, it is challenging to develop a one-size-fits-all approach for document processing. In this post, we explore an example of how a federal agency can use Amazon Web Services (AWS) to design and deploy a solution that addresses this document processing challenge.

In Modernizing tax systems with AWS, we briefly touched upon infrastructure and application modernization using microservices and serverless architectures. We hear from multiple tax and labor agencies about their desire to move to API-based architectures and adopt new technologies. In this post, we dive deeper into these areas and discuss benefits, approaches, and best practices for building modern tax and unemployment insurance (UI) applications using microservices.

Like many healthcare providers, University Hospitals Coventry and Warwickshire (UHCW) NHS Trust, which manages two major hospitals and serves a population of more than one million, has operated with legacy technology that relies heavily on phone calls and manual processes for contacting patients. Recognizing an opportunity to modernize, the Trust linked up with IBM Consulting for an innovative pilot project to digitize patient engagement channels using Amazon Web Services (AWS). Read this post to learn more.

As a teacher with the KIPP charter school network in Houston, Texas, Dave Wakefield knew there had to be a better way for educators to gain insightful feedback on their instruction. Traditionally, educators who wanted feedback on their teaching either had to have someone visit their classroom or film themselves and then send that video to a mentor or peer for review. In 2013, Wakefield founded education technology (EdTech) company Sibme as a way to use technology, powered by Amazon Web Services (AWS), to help educators access quicker and more reliable feedback.

Government agencies are increasingly using large language models (LLMs) powered by generative artificial intelligence (AI) to extract valuable insights from their data in the Amazon Web Services (AWS) GovCloud (US) Regions. In this guide, we walk you through the process of hosting LLMs on Amazon Elastic Compute Cloud (Amazon EC2) instances, using the Hugging Face Text Generation Inference (TGI) Container (TGI) for serving custom LLMs.

The University of Pittsburgh, the University of Illinois Urbana-Champaign (UIUC), the University of California Davis Health Cloud Innovation Center (UCDH CIC)—powered by Amazon Web Services (AWS)—and the AWS Digital Innovation (DI) team have built a prototype that uses machine learning (ML) and generative artificial intelligence (AI) to transform the public health communications landscape by giving officials the tools they need to fight medical misinformation, disinformation, and malinformation.

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.

This fall, artist Suchi Reddy and Amazon Web Services (AWS), in collaboration with the Smithsonian FUTURES Exhibition, debuted me+you in Washington, DC, which embodies the collective answers to the question, “What do you want your future to look like?” me+you is an interactive work of art powered by artificial intelligence (AI) and machine learning (ML) and is the centerpiece of the Smithsonian FUTURES exhibition.

Since 2003, the US federal government has made approximately $1.7 trillion in improper payments, with an estimated $206 billion made in FY 2020 alone. Improper payments are now anticipated to increase proportionally to new levels of federal spending. How can agencies fight improper payments at this scale? And what tools can agencies use to address fraud, erroneous data submission and other causes of this problem? Agencies can use AWS to solve the multi-sided issues of payment integrity.