AI画不出的左手，是因为我们给了它一个偏科的童年。

Core Viewpoint - The article discusses the limitations of AI in generating images that accurately depict left-handed actions, highlighting a significant bias in the training data that affects AI's understanding of spatial relationships and hand orientation [21][23][41]. Group 1: AI Limitations - AI struggles to generate images of left-handed actions, consistently producing right-handed images instead [21][24]. - Various AI models, including Gemini's NanoBananaPro and others like ChatGPT and Seedream, fail to accurately depict left-handed writing despite clear prompts [5][7][9]. - The inability to distinguish between left and right is attributed to biases in the training datasets, which predominantly feature right-handed actions [41][56]. Group 2: Research Findings - A referenced paper titled "Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation" explains that the biases in training data hinder AI's generalization capabilities [23][27]. - The research indicates that the distribution of training data, rather than sheer volume, is crucial for AI's ability to understand spatial relationships [31][32]. - Two key metrics, Completeness and Balance, are defined to assess the effectiveness of training datasets in teaching AI about positional relationships [32][35]. Group 3: Implications of Bias - The article suggests that the training data reflects human biases, as most images depict right-handed individuals, leading to a skewed understanding of actions like writing [41][56]. - The analogy of a student only exposed to one side of a mathematical equation illustrates how AI can become limited in its understanding due to biased training [46][50]. - The conclusion emphasizes the need for a more balanced training dataset to improve AI's performance and understanding of diverse human actions [61][62].