Realizing the Promise of AI Solutions
Artificial intelligence (AI) holds incredible promise for transforming our software-driven world in general and DoD mission capabilities in particular. AI can compute more data more quickly than even automated software. That computational advantage allows us to make the most of human-machine teaming, freeing humans to focus their attention on the types of tasks they do best.
However, solutions to-date, while often brilliant, are difficult to replicate, verify, and validate. Practiced by virtuosos and talented amateurs, current AI solutions are created using intuition and brute force, with haphazard progress, casual transmission, and extravagant use of available materials.
Leading a movement for AI Engineering
The SEI knows that engineering practice for AI software and data is needed to capitalize on the promise of those capabilities for the nation’s military and government defense and security organizations. Further, the SEI has the experience, skills, and practical creativity to bridge the gap between inventing the possible and deploying the practical.
As a result, the SEI is leading a movement to establish and mature an engineering discipline for the data and software of AI systems. When your organization develops AI-enabled capabilities using AI Engineering practice, you will see
- Rapid, iterative delivery of robust AI-enabled capabilities
- Data management practices tuned for the needs in AI for evidence, traceability, and continuous innovation
- Interface designs for effective human-machine teaming in AI systems
- Continuous acceptance testing
- Transition of tools and critical skills for AI
- Ongoing monitoring and validation to counteract an AI-enabled capability’s enhanced attack surface
- Adaptable, flexible, and evolvable solutions
The xView 2 Challenge applied computer vision and machine learning to analyze electro-optical satellite imagery before and after natural disasters to assess building damage. The competition’s sponsor was the Department of Defense’s Defense Innovation Unit (DIU). This technology is being used to assess building damage from wildfires in Australia and the United States.
The SEI is working to study how quantum computing can serve as the next paradigm for optimization in fields like software verification and validation.
September 05, 2019 • Technical Report
This report suggests seven key questions that managers and decision makers should ask about machine learning tools to effectively use those tools to solve cybersecurity problems.read
March 22, 2019 • Video
This SEI Cyber Talk episode explains how inverse reinforcement learning can be effective for teaching agents to perform complex tasks with many states and actions.watch