Category: Artificial Intelligence

Generative artificial intelligence (AI) is revolutionizing public agencies by streamlining services and providing valuable insights from large datasets. However, adding generative AI to your agency is not a simple process. SMX, an Amazon Web Services (AWS) Premier Tier Services Partner, helped one nonprofit agency build a robust architecture in the AWS Cloud that provided them the foundation for building and implementing generative AI tools. In this guest post, experts from SMX explain five best practices they used to help this agency prepare for generative AI.

Citizens are increasingly expecting government to provide modern digital experiences for conducting online transactions. Market research tells us 63 percent of consumers see personalization as the standard level of service. This post offers various architectural patterns for improving customer experience for the public sector for a wide range of use cases. The aim of the post is to help public sector organizations create customer experience solutions on the Amazon Web Services (AWS) Cloud using AWS artificial intelligence (AI) services and AWS purpose-built data analytics services.

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 adapting LLMs to specific domains with parameter efficient fine-tuning techniques made accessible through Amazon SageMaker integrations with Hugging Face.

In this post, we will explore the use of generative artificial intelligence (AI) chatbots as a natural language alternative to the service catalog approach. We will present an Amazon Web Services (AWS) architecture pattern to deploy an AI chatbot that can understand user requests in natural language and provide interactive responses to user requests, directing them to the specific systems or services they are looking for. Chatbots simplify the content navigation and discovery process while improving the customer experience.

In this post, we explore the complexities of data privacy and controls on Amazon Web Services (AWS), examine how creating a landing zone within which to contain such data is important, and highlight the differences between creating a landing zone from scratch compared with using the AWS Landing Zone Accelerator (LZA) for Healthcare. To aid explanation, we use a simple healthcare workload as an example. We also explain how LZA for Healthcare codifies HIPAA controls and AWS Security Best Practices to accelerate the creation of an environment to run protective health information workloads in AWS.

The vision of Singapore’s National Library Board goes beyond making information accessible; the NLB is focused on bringing the richness of the nation’s cultural heritage right to the palm of your hand. As part of its LAB25 (Libraries and Archives Blueprint 2025) Singapore Storytellers initiative, the NLB seeks to nurture a stronger appreciation and understanding of the Singapore experience. To support this, Amazon Web Services (AWS) collaborated with Singapore-based NCS, a leading technology services firm, to create an interactive augmented reality (AR) web application prototype on the AWS Cloud.

The Natural History Museum in London is a world-class visitor attraction and a leading science research center. NHM and Amazon Web Services (AWS) have worked together to transform and accelerate scientific research by bringing together a broad range of UK biodiversity and environmental data types in one place for the first time. In this post, the first in a two-part series, we provide an overview of the NHM-AWS project and the potential research benefits.

Amazon SageMaker is a fully managed machine learning (ML) service that provides various capabilities, including Jupyter Notebook instances. While RStudio, a popular integrated development environment (IDE) for R, is available as a managed service in Amazon Web Services (AWS) commercial Regions, it’s currently not offered in AWS GovCloud (US) Regions. Read this post, however, to learn how you can use SageMaker notebook instances with the R kernel to perform data analytics tasks in AWS GovCloud (US) Regions.

The US Federal Government has endless opportunity to lead the way as well as adopt best practices for how the latest technologies, including generative artificial intelligence (AI), can be leveraged to fulfill its various missions. Yvette Cesario, director of national security sales at Amazon Web Services (AWS) Worldwide Public Sector (WWPS), was the guest on the seventh episode of the AWS Behind the Cloud vodcast and shared her insights about how generative AI has endless opportunities for the US Federal Government to leverage to transform its operations and missions.

In this post, we cover an Amazon Web Services (AWS) Cloud infrastructure with a modular architecture that enables you to explore and take advantage of the benefits from different Retrieval-Augmented Generation (RAG)-based generative AI resources in a flexible way. This solution provides several benefits, along with faster time-to-market and shorter development cycles.