CMU SEI Research Review 2020 Day 1 Artifacts
• Collection
Publisher
Software Engineering Institute
Subjects
Abstract
This collection includes materials from day one of the 2020 SEI Research Review event. These materials include presentations and posters about a discipline for AI Engineering, mismatch in Machine Learning enabled systems, techniques for summarizing and searching video, quantum computing, ethics in AI Engineering, engineering AI systems in an uncertain world, recommendation systems, training AI systems, hardware-software co-optimization, and human decision making with AI support.
Collection Items
Poster - Co-Optimization for High-Performance Data-Intensive Computing in Resource-Constrained Environments
• Poster
By Scott McMillan
Spiral AI/ML helps developers build high-performance applications on leading-edge hardware architectures faster and cheaper, speeding new capabilities to serve national and tactical needs.
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Poster - Human Decision Making with AI Support
• Poster
By Rotem D. Guttman
This poster details the development of the Human-AI Decision Evaluation System (HADES), a test harness allowing the collection of human decision-making data on an arbitrarily large set of possible AI …
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Poster - Characterizing and Detecting Mismatch in ML-Enabled Systems
• Poster
By Grace Lewis
Descriptors for machine learning system elements make stakeholder assumptions explicit and prevent mismatch.
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Poster - Quantum Advantage Evaluation Framework
• Poster
By Jason Larkin
This presentation provides an overview of a framework to evaluate current and projected quantum computing advantage.
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Presentation - Video Summarization and Search
• Presentation
By Edwin J. Morris, Adam Harley (Carnegie Mellon University)
This presentation describes work to develop ML algorithms for detecting and better tracking objects, and recognizing patterns of objects and object interactions.
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Poster - Video Summarization and Search: Object Tracking
• Poster
By Edwin J. Morris, Adam Harley (Carnegie Mellon University)
Researchers developed machine learning algorithms for detecting objects, better tracking those objects, and recognizing patterns of objects and object interactions.
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Poster - A Series of Unlikely Events
• Poster
By Eric Heim
The poster summarizes learning from sequential behavior for activity-based intelligence and modeling human expertise.
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Train, but Verify: Towards Practical AI Robustness
• 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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Poster - Train, but Verify: Towards Practical AI Robustness
• 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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Knowing When You Don't Know: Engineering AI Systems in an Uncertain World
• Presentation
By Eric Heim
This presentation provides a view of new research about artificial intelligence (AI) system engineering and uncertainty.
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The Promise and Challenges of Recommendation Systems for the DoD
• Presentation
By John Wohlbier
This presentation describes recommendation systems, their potential use by the Department of Defense (DoD), and the impact of CMU SEI's research in this area.
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Ethics in AI
• Presentation
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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CMU SEI Research Review 2020 Artifacts