AI Engineering
Publications and resources for the SEI's AI Engineering effort
Publisher:
Software Engineering Institute
Abstract
AI Engineering is an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts. This collection includes papers, videos, presentations, and other publications related to the SEI's AI Engineering work.
Collection Contents
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AI Engineering: 11 Foundational Practices
September 12, 2019 • White Paper
This initial set of recommendations can help organizations that are beginning to build, acquire, and integrate artificial intelligence capabilities into business and mission systems.
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AI Engineering for Defense and National Security: A Report from the October 2019 Community of Interest Workshop
October 29, 2020 • Special Report
Based on a workshop with thought leaders in the field, this report identifies recommended areas of focus for AI Engineering for Defense and National Security.
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Future Reach Conversation: Countering Adversarial Operations Made Possible by AI
November 22, 2019 • Video
Watch as Dr. Matt Gaston, Director of SEI Emerging Technology Center, moderates discussion on countering adversarial operations made possible by AI
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Defining AI Engineering
February 14, 2020 • Video
By Matt Gaston,
Watch Dr. Matt Gaston, Director of the SEI Emerging Technology Center, and Professor Martial Hebert, Dean of CMU School of Computer Science and recognized world expert in computer vision, discuss the emerging field of AI Engineering.
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Ethics in AI Engineering
December 15, 2020 • Video
By Carol J Smith
The presentation discusses how to reduce unintended/harmful bias and prevent the inevitable harm that comes from "unknowable" systems.
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AI at the SEI
January 29, 2019 • Fact Sheet
The SEI’s history as a federally funded research and development center (FFRDC) dedicated to software engineering means that we know what it takes to lay a foundation for confident, rapid adoption of AI for national defense and security.
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Train, but Verify: Towards Practical AI Robustness
November 3, 2020 • Presentation
By Nathan M. VanHoudnos, Jon Helland
This presentation describes efforts to train AI systems to enforce at least two security policies and verify security by testing against realistic threat models.
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Train, but Verify: Towards Practical AI Robustness
December 15, 2020 • Video
By Nathan M. VanHoudnos, Jon Helland
This presentation describes efforts to train AI systems to enforce at least two security policies and verify security by testing against realistic threat models.
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Poster - Train, but Verify: Towards Practical AI Robustness
November 3, 2020 • Poster
By Nathan M. VanHoudnos, Jon Helland
This presentation describes efforts to train AI systems to enforce at least two security policies and verify security by testing against realistic threat models.
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Designing Trustworthy AI
April 29, 2020 • Podcast
By Carol J Smith
Carol Smith discusses a framework that builds upon the importance of diverse teams and ethical standards to ensure that AI systems are trustworthy and able to effectively augment warfighters.
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Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development
December 10, 2019 • Conference Paper
By Carol J Smith
The Human-Machine Teaming (HMT) Framework for Designing Ethical AI Experiences, when used with a set of technical ethics, will guide AI development teams to create AI systems that are accountable, de-risked, respectful, secure, honest, and usable
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Learning by Observing via Inverse Reinforcement Learning
March 22, 2019 • Video
By Ritwik Gupta, Eric Heim
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.
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A Series of Unlikely Events: Learning Patterns by Observing Sequential Behavior (video)
November 11, 2019 • Video
By Eric Heim
Watch SEI principal investigator Eric Heim discuss research to develop novel Inverse Reinforcement Learning (IRL) techniques as efficient and effective means for DoD/IC to perform activity-based intelligence or to teach novices how to perform tasks.
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A Series of Unlikely Events: Learning Patterns by Observing Sequential Behavior
October 28, 2019 • Poster
By Eric Heim
This poster represents research to apply Inverse Reinforcement Learning techniques to model sequential behavior.
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