Industry experts argue that terms like "intelligence" and "learning" lack rigorous technical definitions. This linguistic drift creates friction between academic researchers and commercial marketers. Misaligned vocabulary obscures actual technical progress and complicates peer review. Practitioners must standardize terminology to avoid conceptual errors when evaluating new machine learning architectures.