Company Will Discuss Benefits of High-Fidelity Synthetic Training Data to Defense and National Security Artificial Intelligence Systems
Cignal LLC, a small business that develops cutting-edge capabilities for the rapid training and deployment of advanced inspection and security systems, announced that it was selected as a presenter at the 2021 AI Capabilities Center of the National Defense Industrial Association (NDIA) National Security AI Conference and Exhibition (NSAICE).
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During its presentation, Cignal will showcase its work in creating and leveraging high-fidelity synthetic volumetric data for computed tomography and advanced technology X-ray security and inspection screening systems. Through its training workflow product, Cignal Workbench, Cignal can generate a virtually unlimited source of labeled training data, leading to continuous, on-demand training of advanced artificial intelligence (AI) models for industrial inspection, defense, and national security applications.
“We are pleased to be presenting at this year’s NDIA NSAICE AI Capabilities Center,” said Cignal CEO Jaclyn Fiterman. “In addition to highlighting our product’s current applications within the homeland security sector, we are excited to illustrate the benefits it can provide across the defense industry. Cignal Workbench could be used, for example, to train AI systems to identify anomalies on printed circuit boards or microscopic fractures in metal from welding flaws. By generating synthetic training data for AI models to detect critical defects, Cignal is able to support non-destructive testing and supply chain security efforts in an efficient, low-cost manner.
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The applications are virtually limitless.” Cignal will be presenting at the NSAICE on Wednesday, March 24, 2021.
AI models rely on large amounts of labeled training data to learn, and generating this data for screening and inspection applications currently is a labor-intensive, manual process. However, Cignal’s creation of synthetic data to train AI models eliminates the need for manual image labeling while increasing the overall amount of quality training data.
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