Category: Artificial Intelligence

In the last few years, many state motor vehicle departments agencies quickly transformed their processes and adopted new procedures to accommodate changes caused by the COVID-19 pandemic, like social distancing, contactless interactions, decreased staffing, and other constraints. Now, agencies can build upon these changes by modernizing their systems with intelligent automation—transitioning from reactive to proactive engagements with their citizens. Learn how to use AWS to connect and retrieve data either from an enterprise on-premises database or other third-party integration that allows for both a modernized outreach or an inbound customer experience.

In 2021, the U.S. Department of Defense (DoD) announced the creation of the Joint Warfighting Cloud Capability (JWCC) contract—a multi-vendor acquisition vehicle designed to make cloud services and capabilities available at all classification levels and across all security domains, from the enterprise to the tactical edge. JWCC will enable the DoD to fully leverage the capabilities of the cloud to meet current and future mission initiatives. Further, JWCC is key to enabling critical warfighter capabilities, such as the Joint All-Domain Command and Control (JADC2), and the DoD Artificial Intelligence and Data Acceleration Initiative (ADA). As the DoD continues to modernize the way it supports the warfighter and defends our national security, AWS is committed to supporting its critical mission.

Nonprofit leaders today have various technical products and solutions to consider. The addition of “smart technology” to your nonprofit’s technology conversations may seem intimidating or even unfamiliar to the human-centered work that your organization does. But smart technology can help make your nonprofit’s work more human – automating burdensome tasks for your teams and directing their creativity and bandwidth to what really matters: your mission. Learn how nonprofits can use AWS to develop smart tech to innovate for their communities.

To gain operational efficiencies and reduce workload burdens on employees, some state finance and tax agencies are leveraging robotic process automation (RPA) on AWS. RPA is a software tool that integrates with almost any system or application and performs manual, repetitive, time-consuming tasks. Tax agencies can use AI and ML to support the sheer size and scale of data they manage and to access and analyze all types of data with ease, including voice, video, and streaming data. Find out three ways AI and ML are creating measurable outcomes for tax agencies.

Diabetes is a major chronic disease that often results in hospital readmissions due to multiple factors. An estimated $25 billion is spent on preventable hospital readmissions that result from medical errors and complications, poor discharge procedures, and lack of integrated follow-up care. If hospitals can predict diabetic patient readmission, medical practitioners can provide additional and personalized care to their patients to pre-empt this possible readmission, thus possibly saving cost, time, and human life. In this blog post, learn how to use machine learning (ML) from AWS to create a solution that can predict hospital readmission – in this case, of diabetic patients – based on multiple data inputs.

According to the United States Constitution, public access to judicial proceedings is a right covered by the First and Sixth Amendments. To make hearings visible to the public, even when in-person attendance is limited, state and local governments are beginning to mandate many court proceedings be live streamed, often with a very short window to do so. The cloud-native media and asset management platform Nomad, which is built on AWS, helps governments implement scalable live streaming capabilities quickly and simply.

Citizens want to report issues to their local governments in a fast and simple manner and not have to worry about identifying the right government agency or phone number—for instance, if a fire hydrant is broken, or a road sign has fallen over. In this blog post, learn how to set up a solution with AWS deep learning services that creates a fluid experience for reporting and addressing these issues.

The Busan Cloud Innovation Center (CIC) teamed up with the World Food Programme (WFP), the Busan IT Promotion Agency, and technology startup Nuvilab to use cloud technology to address food waste in South Korea. Using Amazon’s “Working Backwards” approach with artificial intelligence (AI), machine learning (ML), and data lake solutions on AWS, the team developed Zero Waste Zero Hunger (ZWZH), a program that uses artificial intelligence AI to provide data about food consumption.

Healthcare providers and healthcare systems want to modernize their healthcare data exchanges so they can better analyze and gain more insight from their clinical data. In this walkthrough, learn how to use AWS to migrate legacy healthcare messaging data into Amazon HealthLake, which can use artificial intelligence (AI) and machine learning (ML) to discover meaningful and actionable healthcare information embedded in unstructured text.

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.