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Synthetic data has its limits — why human-sourced data can help prevent AI model collapse
With model degradation, AI development could stall, leaving AI systems unable to ingest new data and essentially becoming “stuck in time.”
Loss of nuance: Models begin to forget outlier data or less-represented information, crucial for a comprehensive understanding of any dataset. A case in point: A study published in Nature highlighted the rapid degeneration of language models trained recursively on AI-generated text. By choosing real, human-sourced data over shortcuts, prioritizing tools that catch and filter out low-quality content, and encouraging awareness around digital authenticity, organizations can set AI on a safer, smarter path.
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