Part 2/11:
AI hallucinations refer to the erroneous outputs generated by AI systems, especially large language models (LLMs), that contain inaccurate, misleading, or entirely fabricated information. Unlike human errors, these hallucinations arise when AI models either lack sufficient information or attempt to formulate answers based on incomplete or irrelevant data. Factors contributing to hallucinations include gaps in training data, biases within the AI models, and the inherent limitations of these systems.