As slop takes over the Internet, labs may struggle to obtain high-quality corpuses for training models.
这一观察揭示了AI训练数据质量的危机。随着互联网内容质量的下降，AI系统可能面临'垃圾进，垃圾出'的风险。作者提出的'低背景钢'比喻巧妙地指出了使用2023年前纯净数据的解决方案，同时也暗示了数字时代知识污染的严重性，这可能会对AI系统的可靠性和偏见产生深远影响。
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[24] AI - Bias in Training Materials
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