Artificial Intelligence Engineering
To advance the DoW’s vision of creating viable, trusted, and extensible AI systems, the SEI leads the creation of a professional AI Engineering discipline to develop AI systems that are scalable, robust and secure, and human centered.
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From Reality to Virtual Reality: The Impact of 3DGS on Training, Education, and Beyond
This blog post describes cutting-edge method for creating digital models of the physical world called 3D Gaussian Splatting.
Read More•By Roxxanne White, Matt Walsh, Dominic A. Ross, Richard Laughlin
In Artificial Intelligence Engineering
My AI System Works…But Is It Safe to Use?
This blog post introduce System Theoretic Process Analysis (STPA), a hazard analysis technique uniquely suitable for dealing with the complexity of AI systems.
Read More•By David Schulker, Matt Walsh, Emil Mathew
In Artificial Intelligence Engineering
Artificial Intelligence in National Security: Acquisition and Integration
This blog post highlights practitioner insights from a recent AI Acquisition workshop, including challenges in differentiating AI systems, guidance on when to use AI, and matching AI tools to mission …
Read More•By Paige Rishel, Carol J. Smith, Brigid O'Hearn, Rita C. Creel
In Artificial Intelligence Engineering
Amplifying AI Readiness in the DoD Workforce
The SEI recently partnered with the Department of the Air Force Chief Data and AI Office to develop a strategy to identify and assess hidden workforce talent for data and …
Read More•By Eric Keylor, Robert W. Beveridge, Jonathan Frederick
In Artificial Intelligence Engineering
Out of Distribution Detection: Knowing When AI Doesn't Know
How do we know when an AI system is operating outside its intended knowledge boundaries?
Read More•By Eric Heim, Cole Frank
In Artificial Intelligence Engineering
10 Things Organizations Should Know About AI Workforce Development
This post outlines 10 recommendations developed in response to work with our mission partners in the Department of Defense.
Read More•By Jonathan Frederick, Dominic A. Ross, Eric Keylor, Cole Frank, Intae Nam
In Artificial Intelligence Engineering
DataOps: Towards More Reliable Machine Learning Systems
Decisions based on ML models can have significant consequences, and managing the raw material—data—in ML systems is a challenge. This post explains DataOps, an area that focuses on the management …
Read More•By Daniel DeCapria
In Artificial Intelligence Engineering
Evaluating LLMs for Text Summarization: An Introduction
Deploying LLMs without human supervision and evaluation can lead to significant errors. This post outlines the fundamentals of LLM evaluation for text summarization in high-stakes applications.
Read More•By Shannon Gallagher, Swati Rallapalli, Tyler Brooks
In Artificial Intelligence Engineering
The Essential Role of AISIRT in Flaw and Vulnerability Management
The SEI established the first Artificial Intelligence Security Incident Response Team (AISIRT) in 2023. This post discusses the role of AISIRT in coordinating flaws and vulnerabilities in AI systems.
Read More•By Lauren McIlvenny, Vijay S. Sarvepalli
In Artificial Intelligence Engineering
Enhancing Machine Learning Assurance with Portend
This post introduces Portend, a new open source toolset that simulates data drift in machine learning models and identifies the proper metrics to detect drift in production environments.
Read More•By Jeffrey Hansen, Sebastián Echeverría, Lena Pons, Gabriel Moreno, Grace Lewis, Lihan Zhan
In Artificial Intelligence Engineering
