novatechflow | Alexander Alten

This article explains why AI hallucinations are not glitches but predictable outcomes of how modern models learn: overfitting to training data, hidden bias, distributional shift, gaps in domain knowledge, and even adversarial inputs. It illustrates how systems confidently produce polished but misleading narratives—as seen in common explanations around the Berlin Wall—showing how models blend facts, public sentiment and assumptions into answers that feel credible but may omit or distort reality. Hallucinations become dangerous when embedded into high-stakes systems like autonomous vehicles, medical diagnostics, military decision-making or information ecosystems already strained by disinformation. While eliminating hallucinations entirely is impossible, the article outlines mitigation strategies such as balanced datasets, regularization, uncertainty estimation, grounding to trusted sources, and transparency practices. The core message: trustworthy AI requires rigorous design, contextual...