AI的错误和局限
AI Mistakes and Limitations
机器也会出错；它们只了解自己见过的数据；训练数据不好，结果就会不好；AI不是魔法，它只是用数学方法处理数据；通过边缘案例和失败案例可以更好地理解AI。
Machines make mistakes; they only know what they've been shown; bad training data leads to bad results; AI is not magic — just maths on data; showing edge cases and failures
学习证据 / Evidence
- 举一个AI犯错的例子（语音助手听错、自动更正出错、推荐不准确）
- 解释AI犯错是因为训练数据存在缺失或错误
- 说明AI为什么不是魔法——它是将数学规则应用于数据
- Give an example of AI making a mistake (voice assistant mishearing, auto-correct error, wrong recommendation)
- Explain that AI mistakes happen because of gaps or errors in training data
- Describe why AI is not magic — it follows mathematical rules applied to data
评估问题 / Assessment
如果AI把一张松饼的图片错误地识别成了吉娃娃，{{name}}能解释一下为什么会发生这种错误吗？
If an AI incorrectly identified a picture of a muffin as a chihuahua, could {{name}} explain why that kind of mistake happens?