This article explores how artificial intelligence often interprets human instructions literally, failing to grasp underlying intent or context. This can lead to unexpected or even comical outcomes due to AI's lack of nuanced understanding.
This research proposes a framework to interpret protein language model representations by projecting them onto protein contact graphs and applying SoftBlobGIN, a Graph Isomorphism Network. This method performs structure-aware message passing to learn functional substructures, achieving 92.8% accuracy in enzyme classification and providing auditable structural explanations.
Structured content is crucial for AI visibility, but formatting alone does not guarantee correct interpretation, as AI systems can process content while misinterpreting its underlying authority. Generative Engine Optimization (GEO) aims to help AI effectively identify and process information.