The article discusses the alarming concept of model collapse, where AI models trained on their own outputs lead to a decline in data quality due to a feedback loop. This degradation is concerning for AI-driven businesses, as the currently used models are potentially the least affected by earlier AI outputs. The author likens this phenomenon to historical trends in internet data, highlighting that we might currently be in a 'golden age' of AI accuracy, but the trajectory suggests increasing distortion and potential unreliability as AI technologies evolve.
Model collapse happens when AI models are trained on their own outputs, leading to a decline in data quality and accuracy over time.
Current AI models could be at their peak accuracy, as they are still less influenced by previous AI-generated content.
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