Tag: Machine Learning

Amazon Web Services (AWS) is proud to be a healthcare and technology partner for the Cancer AI Alliance (CAIA), a new initiative announced today. The CAIA is a consortium of four National Cancer Institute (NCI)-designated cancer centers, led by the Fred Hutch Cancer Center, and leading technology and service companies. AWS is contributing a total of $10 million towards the more than $40 million in funding that helps establish the CAIA.

Companies across all industries face a multitude of challenges in their operations, many of which can be addressed through innovative uses of technology. By implementing use cases tailored to their specific needs, organizations can drive efficiencies, mitigate risks, and create competitive advantages. This comprehensive post explores some high-impact use cases with the corresponding Amazon Web Services (AWS) solution that can transform public sector industries.

The Natural History Museum (NHM) in London is a world-class visitor attraction and a leading science research center. NHM and Amazon Web Services (AWS) have partnered up to transform and accelerate scientific research by bringing together a broad range of biodiversity and environmental data types in one place for the first time. In an earlier post, we discussed NHM’s overall vision for using open data in combination with large-scale compute, data systems, and machine learning (ML) to create the Planetary Knowledge Base (PKB), a knowledge graph of global biodiversity. In this post, we focus on the underlying services and architecture that comprise the PKB.

Earlier this month, for the first time, the IMAGINE: Nonprofit conference was hosted in Australia in Melbourne, Sydney, and Brisbane. This event brought together leaders, technologists, and mission-driven innovators in the nonprofit sector to learn, connect, and inspire. Over the three days, attendees explored the ways technology helps nonprofits make a positive impact around the world. Read this post to learn about some of the top moments from IMAGINE: Nonprofit Australia.

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?