The AI Echo Chamber: Model Collapse & Synthetic Data Risks

Recent research has highlighted the potential risks associated with training large language models (LLMs) on synthetic data generated by other AI models. This practice, known as "model collapse," can lead to low-quality outputs and reinforce biases inherent in the synthetic data. The phenomenon of AI eating itself raises ethical concerns and poses a serious question for the future of AI development. Some potential consequences include hallucinations, machine scale security attacks, algorithmic biases, loss of human innovation and creativity, extinction risks, AI overlords and authoritarianism, and public information scarcity. To mitigate these risks, companies developing LLMs must carefully select their training datasets and utilize responsible AI development practices, prioritizing diversity in the research, development, and implementation stages.