The term "hallucination" is a misnomer when applied to AI, as it implies a human-like subjective experience that current models lack. Instead, AI outputs that deviate from factual accuracy are better described as errors in prediction or generation, stemming from the model's training data and architecture. Understanding these limitations is crucial for responsible AI development and deployment. AI
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IMPACT Clarifies terminology around AI errors, promoting more accurate understanding and discussion.
RANK_REASON Opinion piece discussing the terminology used to describe AI errors.