Tag: Machine Learning

As Australia moves forward with a national framework for the assurance of artificial intelligence (AI) in government, Amazon 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.

A flexible architecture is a crucial factor in unlocking the full potential of generative artificial intelligence (AI) solutions. 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 open source foundation models in a flexible way. This solution provides several benefits.

Amazon Web Services (AWS) advanced technologies can help reimagine the way healthcare entities deliver person-centered care. Technologies such as generative artificial intelligence (AI) and the ability to manage petabytes (PB) of data help companies achieve actionable insights that improve care. They can drive the ability to build new care models, improve the human experience in their health and care process, and enable clinical professionals to practice at the top of their licenses. In this blog post, I describe some ways AWS helps companies reimagine the delivery of health and care.

SoftwareOne, an Amazon Web Services (AWS) Premier Tier Services Partner and a global provider of end-to-end software and cloud technology solutions, addresses code review challenges with its automated solution, SummarAIze, powered by the generative artificial intelligence (generative AI) capabilities of Amazon Bedrock. This post provides an overview of SoftwareOne’s approach to building SummarAIze and its impact on the software development process.

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.

The Amazon Web Services (AWS) Open Data Sponsorship Program makes high-value, cloud-optimized datasets publicly available on AWS. Through this program, customers are making more than 100 petabytes (PB) of high-value, cloud-optimized data available for public use. This past quarter, AWS released 21 new or updated datasets. What will you build with these datasets?

Amazon Web Services (AWS) is investing $4 million to support the work of the HALO Trust and trial the use of artificial intelligence (AI) with drone imagery to locate minefields and other explosive remnants of war in Ukraine. Innovating with AWS will enable HALO to make wider use of the high-resolution drone footage it collects, including testing machine learning (ML) models for identifying mines.

The Amazon Web Services (AWS) commitment to safe, transparent, and responsible artificial intelligence (AI)—including generative AI—is reflected in our endorsement of the White House Voluntary AI Commitments, our participation in the UK AI Safety Summit, and our dedication to providing customers with features that address specific challenges in this space. In this post, we explore how AWS can help agencies address the governance requirements outlined in the Office of Management and Budget (OMB) memo M-2410 as public sector entities look to build internal capacity for 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.

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.