Training AI on intellectual property fundamentally differs from human reading through quantifiable mathematical distinctions: reading processes sequential information through neural networks with semantic understanding, while ML training builds statistical correlations in high-dimensional vector spaces requiring massive datasets (n>10,000) to establish significance. Pattern matching systems extract numerical relationships through probability distributions and distance metrics without comprehension, producing unstable results with limited samples due to centroid instability and high variance. Deliberate extraction of protected content leaves detectable statistical signatures including content regurgitation patterns and over-representation of proprietary materials. The mathematical burden of proof demonstrates that pattern matching requires comprehensive datasets to function—unlike human reading where n<100 examples suffice—making unauthorized computational exploitation of intellectual property mathematically distinct from established reading practices, with different technical requirements, extraction methodologies, and information processing frameworks.
Dimensional processing divergence
Quantitative threshold requirements
Information extraction methodology
Centroid instability principle
Annotation density requirement
Proprietary information exclusivity
Context window limitations
Quantifiable extraction metrics
Intentionality factor
Technical protection circumvention
Information theory perspective
Fair use boundary violations
This mathematical framing conclusively demonstrates that training pattern matching systems on intellectual property operates fundamentally differently from human reading, with distinct technical requirements, operational constraints, and forensically verifiable extraction signatures.