Core Viewpoint - The article discusses the phenomenon of "hallucination" in AI, explaining that it arises from the way AI is trained, which rewards guessing rather than admitting uncertainty [5][11]. Group 1: AI Hallucination - AI often provides incorrect answers when it does not know the correct information, as it is incentivized to guess rather than remain silent [5][6]. - An example is given where an AI model provided three different incorrect birth dates for a person, demonstrating its tendency to "hallucinate" answers [5][6]. - OpenAI's research indicates that this behavior is a result of a training system that rewards incorrect guesses, leading to a higher score for models that guess rather than those that admit ignorance [7][8]. Group 2: Training and Evaluation - The training process for AI can be likened to a never-ending exam where guessing is the optimal strategy to achieve a higher score [6][7]. - OpenAI compared two models, showing that one model had a higher accuracy but a significantly higher error rate, while the other model was more honest in its responses [7][8]. - The concept of "singleton rate" is introduced, indicating that if an information appears only once in the training data, the AI is likely to make mistakes when judging its validity [9]. Group 3: Limitations and Misconceptions - OpenAI argues that achieving 100% accuracy is impossible due to the inherent uncertainty and contradictions in the world, meaning hallucinations will always exist [10]. - The article emphasizes that hallucination is not an unavoidable flaw but can be controlled if AI learns to admit when it does not know something [10][11]. - It is noted that smaller models may sometimes be more honest than larger models, as they are less likely to guess when uncertain [11]. Group 4: Philosophical Implications - The article raises questions about the nature of human imagination and creativity, suggesting that hallucination in AI may reflect a similar human trait of creating stories in the face of uncertainty [14][15]. - It posits that the ability to create myths and stories is what distinguishes humans from other animals, and this trait may not be a flaw but rather a fundamental aspect of intelligence [14][15]. - The discussion concludes with a contemplation of the future of AI, balancing the desire for accuracy with the need for creativity and imagination [17].

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].

Core Viewpoint - The article discusses the phenomenon of "hallucination" in AI, explaining that it arises from the way AI is trained, which rewards guessing over admitting uncertainty [4][16]. Group 1: AI Hallucination Mechanism - AI generates incorrect answers when it lacks knowledge, often providing multiple wrong responses instead of admitting ignorance [4][5]. - The training process incentivizes guessing, leading to higher scores for models that guess rather than those that admit they don't know [5][7]. - OpenAI's research indicates that hallucination is a byproduct of the training system, where models are rewarded for incorrect answers if they guess [8][15]. Group 2: Statistical Insights - In a comparison of two models, o4-mini had a higher accuracy rate (24%) but a significantly higher error rate (75%) compared to gpt-5-thinking-mini, which had a lower accuracy (22%) but a much lower error rate (26%) [7][8]. - The abandonment rate of questions was also notable, with o4-mini answering almost all questions (1% unanswered) while gpt-5 had a 52% abandonment rate, indicating a preference for honesty over guessing [8][9]. Group 3: Theoretical Implications - The concept of "singleton rate" is introduced, highlighting that if an information appears only once in the training data, the AI is likely to make errors in judgment [11][12]. - OpenAI argues that hallucination is not an unavoidable flaw but can be managed if AI learns to admit uncertainty [14][15]. Group 4: Broader Reflections on Hallucination - The article draws parallels between AI hallucination and human creativity, suggesting that both arise from a need to make sense of uncertainty [17][31]. - It posits that the ability to create stories and myths is a fundamental aspect of humanity, which may also be reflected in AI's creative capabilities [23][30]. - The discussion raises questions about the future of AI, balancing the need for accuracy with the potential for creativity and imagination [39][42].