GCP – How the Air Force Research Laboratory is Advancing Defense Research with AI

Through our collaboration, the Air Force Research Laboratory (AFRL) is leveraging Google Cloud’s cutting-edge artificial intelligence (AI) and machine learning (ML) capabilities to tackle complex challenges across various domains, from materials science and bioinformatics to human performance optimization. AFRL, the center for scientific research and development for the U.S. Air Force and Space Force, is embracing the transformative power of AI and cloud computing to accelerate its mission of developing and transitioning advanced technologies to the air, space, and cyberspace forces.

This collaboration not only enhances AFRL’s research capabilities, but also aligns with broader Department of Defense (DoD) initiatives to integrate AI into critical operations, bolster national security, and maintain technological advantage by demonstrating game-changing technologies that enable technical superiority and help the Air Force adopt to cutting edge technologies as soon as they are released. By harnessing Google Cloud’s scalable infrastructure, comprehensive generative AI offerings and collaborative environment, the AFRL is driving innovation and ensuring the U.S. Air Force and Space Force remain at the forefront of technological advancement.

Let’s delve into examples of how the AFRL and Google Cloud are collaborating to realize the benefits of AI and cloud services:

Bioinformatics breakthroughs: The AFRL’s bioinformatics research was once hindered by time-consuming manual processes and data bottlenecks, causing delays in moving and sharing data, getting access to US-based tools, using standard storage and hardware, and having the right system communications and integrations across third party infrastructure. Because of this, cross-team collaboration and experiment expansion was severely limited and inefficiently tracked. With very little cloud experience, the team was able to create a siloed environment where they used Google Cloud’s infrastructure, such as Google Compute Engine, Cloud Workstations, and Cloud Run to build analytic pipelines that helped them test, store, and analyze data in an automated and streamlined way. That data pipeline automation paved the way for further exploration and expansion on a use case that had never been done before.

Web app efficiency for lab management: The AFRL’s complex lab equipment scheduling process resulted in challenges in providing scalable, secure access to important content and information for users in different labs. To mitigate these challenges and ease maintenance for non-programmer researchers and lab staff, the team built a custom web application based on Google App Engine, integrated with Google Workspace and Apps Scripts, so that they could capture usage metrics for future hardware investment decisions and automate admin tasks that were taking time away from research. The result was significantly faster ability to make changes without administrator intervention, a variety of self-service options for users to schedule time on equipment and request training, and an enhanced, scalable design architecture with built-in SSO that helped streamline internal content for multiple labs.

Modeling insights into human performance: Understanding and optimizing human performance is critical for the AFRL’s mission. The FOCUS Mission Readiness App, built on Google Cloud utilizes various infrastructure services, such as Cloud Run, Cloud SQL, and GKE and integrates with the Garmin Connect APIs to collect and analyze real-time data from wearables.

By leveraging Google Cloud’s BigQuery and other analytics tools, this app provides personalized insights and recommendations for fatigue interventions and predictions that help capture valuable improvement mechanisms in cognitive effectiveness and overall well-being for Airmen.

Streamlined AI model development with Vertex AI:

The AFRL wanted to replicate the functionality of university HPC clusters, especially since there was a diversity of users that needed extra compute and not everyone was trained on how to use these tools. They wanted an easy GUI and to maintain active connections where they could develop AI models and test their research with confidence. They leveraged Google Cloud’s Vertex AI and Jupyter Notebooks through Workbench, Compute Engine, Cloud Shell, Cloud Build and much more to get a head start in creating a pipeline that could be used for sharing, ingesting, and cleaning their code. Having access to these resources helped create a flexible environment for researchers to do model development and testing in an accelerated manner.

The AFRL’s collaboration with Google Cloud exemplifies how AI and cloud services can be a driving force behind innovation, efficiency, and problem-solving across agencies. As the government continues to invest in AI research and development, collaborations like this will be crucial for unlocking the full potential of AI and cloud computing, ensuring that agencies across the federal landscape can leverage these transformative technologies to create a more efficient, effective, and secure future for all.

Learn more about how we’ve helped government agencies accelerate their mission and impact with AI.

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