Company Creates Flexible Computing and Data Environments for Artificial Intelligence Systems and Screening Processes
Cignal LLC, a small business that develops cutting-edge capabilities for the rapid training, evaluation, and deployment of advanced inspection and security systems, announced that it was awarded Phase 2 project funding on July 28, 2021, by the Department of Homeland Security (DHS) Science and Technology Directorate’s (S&T) Silicon Valley Innovation Program (SVIP).
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In collaboration with S&T’s Screening at Speed Program, Cignal will continue virtualizing security devices and screening processes for applications to artificial intelligence (AI) and simulation technology, with the goal of solving critical national security problems in aviation security and screening. Cignal also will refine and expand the capabilities of its training and evaluation workflow platform, which generates a virtually unlimited source of labeled training data for continuous, on-demand training of AI models for computed tomography and advanced technology X-ray screening and inspection systems.
“We are proud to continue our work with DHS S&T in Phase 2 of this project,” said Cignal CEO Jaclyn Fiterman. “As a technology startup, we are focused not only on developing advanced technologies but also in leveraging them, in combination with intelligence information, to respond to emerging threats and innovate in ways that make security more seamless. With its emphasis on new technology development, testing, and accelerated market transition, the SVIP empowers Cignal to support the DHS national security mission.”
AI models rely on large amounts of labeled training data to learn, and generating this data for screening applications currently is a labor-intensive, manual process. However, Cignal’s ability to create and blend real and synthetic data can be used to more rapidly and accurately train and test AI-enabled algorithms and processes. Cignal’s flexible computing and data environment also allows users to quickly train or test new detection models without the time or cost associated with conventional approaches.
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