Synthetic Data: The Double-Edged Sword in AI's Quest for Diversity and Security
The explosion of AI technologies like OpenAI’s GPT-4 has underscored the vital need for vast amounts of data to train increasingly complex models. However, as these models consume more AI-generated content, they risk a phenomenon known as model collapse, where the quality of outputs degrades over time. Synthetic data emerges as a potential solution, offering a way to feed AI systems with high-volume, diverse datasets without compromising the privacy and security of real individuals.
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