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GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
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First, however, Sophia plans to begin by offering its TILEs to satellite operators that require compute solutions on orbit. Potential partners include Earth-observation satellites collecting large amounts of sensor data, missile warning and tracking systems that the Pentagon is investing billions of dollars to build, or even increasingly complex communications networks.