Why Does AI Tell Plausible Lies? (The True Nature of Hallucinations)

This article from Qiita DL explains why AI models, particularly large language models, often generate incorrect but seemingly plausible answers, a phenomenon known as "hallucination." The core argument is that AI doesn't seek truth but rather generates the most probable continuation of a given input. This is due to their training on vast datasets where statistical patterns are learned, not factual accuracy. The article highlights a fundamental limitation of current AI technology: its reliance on pattern recognition rather than genuine understanding. This can lead to misleading or even harmful outputs, especially in applications where accuracy is critical. Understanding this limitation is crucial for responsible AI development and deployment.

• •AI hallucinations are a result of the model generating probable continuations, not searching for truth.
• •Current AI models rely on pattern recognition rather than genuine understanding.
• •Understanding the limitations of AI is crucial for responsible development and deployment.