The spread of AI-generated content on the internet poses a significant threat to the integrity of online information. As AI models are trained on the data they generate, a phenomenon known as “model collapse” can produce inaccurate content. This article examines the potential consequences of model collapse, such as creating echo chambers and degrading internet information. Researchers have proposed a solution to combat the model collapse, which involves blending AI-generated and human-authored data in training processes. By striking a balance between AI’s efficiency and human content’s reliability, we can harness AI’s potential while preserving the digital landscape’s authenticity.
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