全网破防，AI“手指难题”翻车逼疯人类，6根手指，暴露Transformer致命缺陷

Core Insights - The recent "finger counting problem" highlights a significant flaw in AI models, particularly those based on the Transformer architecture, which struggle with visual reasoning and understanding discrete structures [1][50]. Group 1: AI Performance Issues - AI models, such as Nano Banana Pro and GPT-5.2, consistently fail to count the correct number of fingers on a six-fingered hand, often defaulting to the assumption of five fingers due to their training data bias [2][6][9]. - The inability of AI to recognize the six fingers is attributed to its reliance on basic shapes and traditional associations rather than precise visual recognition [21][32]. Group 2: Limitations of Transformer Architecture - The Transformer architecture's parallel computing capabilities, while beneficial for speed, hinder the model's ability to perform multi-step logical reasoning, leading to mechanical and fragmented thinking [37][39]. - AI's lack of a coherent thought process when faced with anomalies, such as the six-fingered hand, results in a failure to reassess and adjust its responses [39][46]. Group 3: Need for Advanced Models - To address the shortcomings revealed by the finger counting problem, there is a call for more advanced architectures and diverse training data that can enhance AI's understanding of complex visual details [50]. - The current models' reliance on strong statistical priors from training data limits their ability to understand and generate precise structures, indicating a need for hybrid modeling approaches that combine different AI techniques [45][50].