Category: Artificial Intelligence
Cybersecurity analytics is a systematic methodology designed to collect, ingest, process, aggregate, and analyze security events. This methodology empowers organizations to proactively perform security investigations, powered by advanced analytics and machine learning (ML), which help mitigate cyber issues more effectively and efficiently at scale. Learn about the core components of a cybersecurity analytics framework and how organizations can use AWS to design a cybersecurity analytics platform with analytics and ML services.
Jacaranda Health, a Kenya-based nonprofit organization, is on a mission to end preventable maternal and newborn deaths by deploying low-cost, sustainable solutions that improve the quality of care in government health systems. Jacaranda Health, a recipient of the 2021 AWS IMAGINE Grant award, uses AWS to power a health platform that uses artificial intelligence (AI) to connect mothers with timely information about pregnancy care, as well as potentially lifesaving advice and referrals to care facilities when it matters most.
Nara Space Technology is a South Korea-based startup that builds nano satellite constellations and provides satellite data services to let customers quickly identify and address issues like changing climate conditions and disaster recovery to improve life on Earth. Nara Space provides solutions for nano satellite and small spacecraft system design, integration, development, and testing; enables satellite data analytics based on deep learning; and improves the visual quality of standard satellite imagery with its Super Resolution core technology. To do this, Nara Space uses AWS for secure, flexible, scalable, and cost-efficient cloud solutions.
The cloud is changing the way we do research—accelerating the pace of innovation, democratizing access to data, and allowing researchers and scientists to scale, work collaboratively, and make new discoveries from which we may all benefit. Researchers from around the world look to the AWS Cloud for customer-focused, pioneering, and secure solutions for their toughest challenges. Discover how customers in Latin America and Canada use AWS for research.
The browser-based computational notebook tool, Jupyter, provides students and educators with an interactive learning environment to accelerate programming learning. But setting up collaborative Jupyter notebooks at the classroom and institutional level can be time-consuming and costly. Amazon SageMaker Studio Lab is a no-cost service built on Jupyter notebooks that takes care of the configuration and security of setting up multi-user Jupyter notebook environments – so educators can focus on teaching and learners can accelerate their journey in ML.
The Amazon re:MARS 2022 conference brought together thought leaders, technical experts, and groundbreaking companies and organizations that are transforming what’s possible in machine learning (ML), automation, robotics, and space. Advancements in these fields are the engines that will drive innovation for the next 100 years. Read on to learn about announcements from re:MARS related to the public sector, plus some of the innovative organizations and companies that were onsite to inspire guests with breakthrough technologies and ideas.
AWS announces simpler access to sustainability data and launches hackathon to accelerate innovation for sustainability
Artificial intelligence (AI) and machine learning (ML) are critical tools being used in healthcare research, autonomous applications, predictive maintenance, and also a key tool used to advance sustainability solutions. However, to use AI and ML to solve sustainability problems, innovators need specific datasets that are prepared for analysis and training of the models. To help create and accelerate sustainability solutions, the Amazon Sustainability Data Initiative (ASDI) today announced easier identification of sustainability datasets with integration in AWS Data Exchange and the launch of a sustainability hackathon.
To mitigate synthetic fraud, government agencies should consider complementing their rules-based improper payment detection systems with machine learning (ML) techniques. By using ML on a large number of disparate but related data sources, including social media, agencies can formulate a more comprehensive risk score for each individual or transaction to help investigators identify improper payments efficiently. In this blog post, we provide a foundational reference architecture for an ML-powered improper payment detection solution using AWS ML services.
Can you see sounds? Using open data, you can. To celebrate this year’s World Oceans Day, an artist and sustainability application architect at Amazon Web Services (AWS), created an artwork titled Can You See the Sound of the Ocean. To create the art, she drew inspiration from the Pacific Ocean Sound Recordings from the Monterey Bay Aquarium Research Institute (MBARI), available through the Amazon Sustainability Data Initiative (ASDI). Learn more about the dataset and the art work.
Nursing shortages are not a new phenomenon, but the pandemic has exacerbated the problem. The situation has forced hospitals to think creatively about their staffing models and has also brought clinical teams, operators, and IT departments together in an effort to seek new ways to use technology. One healthcare system is using robotics powered by cloud technology, including artificial intelligence (AI) and machine learning (ML), to help nurses spend more time with patients.