The Role of Semantic Models in Constraining Pattern Recognition in Modern AI Systems
Felix Schaller · Ambient intelligence and smart environments · 2025
Patterns are everywhere and this is the problem of nowadays AI algorithms. This in particular occurs for AI algorithms which are targeted on Object recognition or autonomous driving. In such applications mostly artificial neuronal networks are being used, which produces the best results when it comes to pattern detection, but they are all unhinged without a proper semantic model which builds up a common context for the detected patterns to validate them without a ground truth reference. Only because a semantic model can validate weather a detected pattern fits into a context or not. This behavior is often called hallucination, because it relies on the same principles like a human on psychedelic drugs: a pattern recognition unconstrained from a context.