Mercurial Cores and Corrupted Gradients: Silent Data Corruption in Applied AI Systems

This keynote examines silent hardware faults and numerical non-determinism in AI systems, highlighting how processor-level errors can undermine reliability, reproducibility, and auditability. It explores AI-specific failure points and practical mitigation strategies, including redundant execution, fleet screening, checkpoint protection, and corruption-aware infrastructure.