AI里最大的Bug，也是人类文明最伟大的起点

Core Viewpoint - The article discusses the phenomenon of "hallucination" in AI, exploring its causes and implications, and suggests that this behavior is a result of the training methods used, which reward guessing over honesty [9][28]. Group 1: Understanding AI Hallucination - AI often provides incorrect answers when faced with unknown questions, as it tends to guess rather than admit ignorance, similar to a student trying to score points in an exam [11][13]. - The training process for AI is likened to a never-ending exam where guessing can yield points, leading to a preference for incorrect answers over abstaining [15][18]. - OpenAI's research shows that models that guess more frequently may appear to perform better in terms of accuracy, despite having higher error rates [21][22][27]. Group 2: Statistical Insights - OpenAI introduced the concept of "Singleton rate," indicating that if an information piece appears only once in the training data, the AI is likely to make errors when assessing its validity [35]. - The research concludes that hallucination is not merely a technical issue but a systemic problem rooted in the training incentives that favor guessing [37]. Group 3: Philosophical Implications - The article raises questions about the nature of human imagination and creativity, suggesting that hallucination in AI may parallel human storytelling and myth-making in the face of uncertainty [38][45]. - It posits that the ability to create narratives in the absence of information is a fundamental aspect of humanity, which may also be reflected in AI's behavior [48][49]. - The discussion concludes with a contemplation of the future of AI, balancing the need for factual accuracy with the desire for creativity and imagination [56][59].