Presenter |
Short-Term (3–5 years) Capabilities |
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Peter Pirolli, Institute for Human and Machine Cognition |
- Multi-level models across cognitive, rational, and social bands of interactive phenomena for well-defined, “stationary” sensemaking tasks
- Sensemaking tasks (but still intelligence community-relevant)
- Explainable artificial intelligence for visual analytics and simulated drone operations
- Interactive task learning for usable soft bots for well-defined tasks (clear goal, well-defined constraints, clear operators)
- Improved visual analytics for machine learning programming
|
Chris Callison-Burch, University of Pennsylvania |
- Tighter integration of crowdsourcing with machine learning
- Correct/confirm output from models
- Active learning
- Domain adaptation
- New crowdsourcing platform for natural language processing
- Remove hassles of Mechanical Turk
- Cultivate groups of language experts
- Create standing pools of language workers
- Deployable on inside of the intelligence community
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Mark Riedl, Georgia Institute of Technology |
- Agents expected to start using commonsense knowledge and world knowledge to address human needs
- Conversational agents + expectation; need to engage in longer conversations and rely on computational imagination
- Agents expected to differentiate behavior based on cultural context
- Computational creativity as part of mainstream content creation
|
Panel on Evaluation of Machine-Generated Products; Anthony Hoogs, Moderator |
- Open source tools for efficient annotation
- Semi-automated labeling to efficiently fuse computed labels with manual adjudication
- Large-scale annotation of operationally representative data sets in domains of interest, made available to researchers, particularly multi-modal data sets
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NOTE: IARPA, Intelligence Advanced Research Projects Activity.