United States Air Force Research Laboratory AFRL
The Air Force Research Laboratory (AFRL) is utilizing Google Cloud’s state-of-the-art machine learning (ML) and artificial intelligence (AI) capabilities to address complex problems in a range of fields, from bioinformatics and materials science to optimizing human performance. To further its goal of creating and transferring cutting-edge technologies to the air, space, and cyberspace forces, AFRL, the U.S. Air Force and Space Force’s Center for Scientific Research and Development, embraces the revolutionary potential of artificial intelligence (AI) and cloud computing.
By showcasing game-changing technologies that enable technical superiority and assist the Air Force in adopting cutting-edge technologies as soon as they are released, this partnership not only strengthens AFRL’s research capabilities but also supports larger Department of Defense (DoD) initiatives to integrate AI into critical operations, support national security, and maintain technological advantage. The AFRL is fostering innovation and guaranteeing that the U.S. Air Force and Space Force stay at the vanguard of technological progress by utilizing Google Cloud’s scalable infrastructure, extensive generative AI solutions, and collaborative atmosphere.
Let’s examine some instances of how Google Cloud and the AFRL are working together to reap the rewards of cloud computing and artificial intelligence:
Breakthroughs in bioinformatics: Previously, laborious manual procedures and data bottlenecks hampered the AFRL’s bioinformatics research by delaying data sharing and transfer, gaining access to US-based tools, utilizing standard hardware and storage, and having the proper system communications and integrations across third-party infrastructure. Cross-team cooperation and experiment growth were thus severely constrained and ineffectively monitored.
Using Google Cloud’s infrastructure, including Cloud Workstations, Cloud Run, and Google Compute Engine, the team was able to build analytic pipelines that allowed them to test, store, and analyze data in an automated and efficient manner despite having very little prior experience with cloud computing. Further investigation and development of a hitherto unexplored use case were made possible by that data pipeline automation.
Efficiency of web apps for lab management: The AFRL‘s intricate lab equipment scheduling procedure made it difficult to give users in various labs scalable, secure access to crucial data and content. The team created a custom web application based on Google App Engine, integrated with Google Workspace and Apps Scripts, to alleviate these difficulties and make maintenance easier for lab staff and non-programmer researchers. This allowed them to automate administrative tasks that were detracting from research and collect usage metrics for future hardware investment decisions.
An improved, scalable design architecture with integrated SSO that helped streamline internal content for multiple labs, a range of self-service options for users to schedule time on equipment and request training, and a noticeably faster ability to make changes without administrator intervention were the outcomes.
Modeling insights into human performance: The AFRL‘s mission depends on comprehending and improving human performance. The FOCUS Mission Readiness App, which is based on Google Cloud, gathers and analyzes real-time data from wearables by integrating with the Garmin Connect APIs and using a variety of infrastructure services, including Cloud Run, Cloud SQL, and GKE.
This software offers tailored insights and suggestions for fatigue therapies and forecasts that help identify important improvement mechanisms in Airmen’s cognitive performance and general well-being by utilizing Google Cloud’s BigQuery and additional analytics technologies.
Vertex AI streamlined the process of developing AI models: The AFRL sought to mimic the capabilities of university HPC clusters, particularly because different users required different amounts of computation and not all users had received the necessary training. They desired an intuitive user interface and the ability to stay in touch with others so they could create AI models and confidently verify their findings.
To get a head start on building a pipeline that could be utilized for sharing, ingesting, and cleaning their code, they made use of Google Cloud’s Vertex AI and Jupyter Notebooks through Workbench, Compute Engine, Cloud Shell, Cloud Build, and many other tools. The availability of these tools facilitated the development of an adaptable environment that allowed researchers to construct and test models more quickly.
Researchers can quickly develop and implement creative solutions with the flexible and adaptive environment that cloud capabilities and AI/ML tools offer. It’s similar to having a toolbox full of potent AI building blocks that can be put together to address its particular research problems.
An example of how AI and cloud services may spur creativity, efficiency, and problem-solving across agencies is the AFRL‘s partnership with Google Cloud. Collaborations like this will be essential to maximizing the potential of cloud computing and AI as the government continues to invest in AI research and development. This will guarantee that agencies throughout the federal landscape can take advantage of these game-changing technologies to build a more secure, efficient, and effective future for everybody.
