Implications of generative models in government

Matthew L. Dering, Conrad S. Tucker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This work introduces an En-GAN-eer, a cognitive system that is built upon a Generative Adversarial Network (or GAN). This cognitive system is used to generate visual ideas that can be used as a basis for communicating ideas from one entity in an organization to another. Clearly communicating an idea to others can be a difficult and time consuming task, which can involve hand drawings, textual descriptions, or specialized software requiring expertise. While drawing has the benefits of being language free, and requiring little expertise, many individuals are not well versed with this skill set. This work also explores how the assignment of intellectual contributions may evolve within teams that include both human decision makers and cognitive assistants such as an En-GAN-eer. From a government and policy perspective, novel legislation may be needed in order to create incentives that promote the acceptance and integration of cognitive systems into the workforce.

Original languageEnglish (US)
Title of host publicationFS-17-01
Subtitle of host publicationArtificial Intelligence for Human-Robot Interaction; FS-17-02: Cognitive Assistance in Government and Public Sector Applications; FS-17-03: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; FS-17-04: Human-Agent Groups: Studies, Algorithms and Challenges; FS-17-05: A Standard Model of the Mind
PublisherAI Access Foundation
Pages158-163
Number of pages6
VolumeFS-17-01 - FS-17-05
ISBN (Electronic)9781577357940
StatePublished - Jan 1 2017
Event2017 AAAI Fall Symposium - Arlington, United States
Duration: Nov 9 2017Nov 11 2017

Other

Other2017 AAAI Fall Symposium
CountryUnited States
CityArlington
Period11/9/1711/11/17

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Dering, M. L., & Tucker, C. S. (2017). Implications of generative models in government. In FS-17-01: Artificial Intelligence for Human-Robot Interaction; FS-17-02: Cognitive Assistance in Government and Public Sector Applications; FS-17-03: Deep Models and Artificial Intelligence for Military Applications: Potentials, Theories, Practices, Tools and Risks; FS-17-04: Human-Agent Groups: Studies, Algorithms and Challenges; FS-17-05: A Standard Model of the Mind (Vol. FS-17-01 - FS-17-05, pp. 158-163). AI Access Foundation.