Category: HAQM Titan

When natural disasters and conflicts create survival situations, the rapid response of the world’s hundreds of humanitarian organizations saves lives. Those organizations can save even more lives when they acquire information in pace with fast-evolving situations on the ground. HAQM is investing $25,000 in AWS credits and providing hands-on engineering support to help the United Nations’ emergency reporting service, ReliefWeb, get up-to-date crisis information to everyone who needs it—and make sure that no situation is forgotten.

Personalized therapy for diseases such as cancer utilizes an individual’s unique genomic profile to guide treatment decisions. However, the effect and clinical significance of most genetic variants are uncertain. Accurate classification of the clinical significance of novel genetic variants requires extensive curation of peer-reviewed biomedical literature. In recent years, generative AI has demonstrated promising results in information extraction and text summarization. In this post, we explore how various AWS-native solutions can be used to create a secure, retrieval-augmented, and cost-effective biomedical chatbot designed to facilitate biocuration.

HAQM Bedrock is now generally available in the HAQM Web Services (AWS) Top Secret cloud. With authority to operate in the AWS Top Secret Cloud, HAQM Bedrock is providing U.S. Government customers with secure access to foundation models (FMs) and generative artificial intelligence (AI) capabilities within AWS’s classified environment.

In this post, we explore how generative artificial intelligence (AI), powered by services such as HAQM Bedrock and HAQM SageMaker, can be harnessed to meet the unique challenges of AWS GovCloud (US). We highlight use cases that demonstrate the potential of generative AI to enhance efficiency, automate workflows, and extract insights—all within a secure, compliant framework.

As Australia moves forward with a national framework for the assurance of artificial intelligence (AI) in government, HAQM Web Services (AWS) is committed to helping our customers implement AI solutions that align with Australia’s AI Ethics Principles. This post outlines how AWS tools and services can support government agencies in adhering to Australia’s AI Ethics Principles when developing AI and machine learning (ML) solutions. The post includes a focus on implementation to help Australian governments responsibly innovate whilst maintaining cloud-based agility.

Machine learning (ML) and artificial intelligence (AI) are transformative technologies, enabling organizations of all sizes to further their mission in ways not previously possible. But, it is critical to think responsibly about these technologies so that all users are treated fairly, data is appropriately protected, and individuals can make informed choices about consent. In this post, we discuss responsible AI and how you should think about your workloads. This approach will help ensure your AI systems are fair, transparent, and secure.

Applications and user experiences are poised to be reinvented with generative artificial intelligence (AI), and the public sector is no exception. Governments, education institutions, nonprofits, and health systems must constantly adapt and innovate to meet the changing needs of their constituents, students, beneficiaries, and patients. If used responsibly, this powerful technology can open doors to endless possibilities to increase creativity, productivity, and progress.

The University of Pittsburgh, the University of Illinois Urbana-Champaign (UIUC), the University of California Davis Health Cloud Innovation Center (UCDH CIC)—powered by HAQM 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.

To improve safety and convenience, transportation agencies amass a substantial volume of data. However, these organizations encounter challenges in data accuracy validation due to issues related to data quality and occasional missing information. With the incorporation of new artificial intelligence and machine learning capabilities from HAQM Web Services (AWS), they can take advantage of no-code solutions to identify and address data gaps.