Regulatory Frameworks: As synthetic data becomes more prevalent, new regulations or guidelines may emerge to govern its use in AI training.
Integration with Other Technologies: Synthetic data may be combined with other emerging AI techniques, such as few-shot learning or transfer learning, to create more robust and adaptable models.
Ongoing Challenges
Verifiability: Developing methods to verify the quality and reliability of synthetic data remains a significant challenge.
Ethical Considerations: The use of synthetic data raises complex ethical questions about representation, bias, and the potential displacement of human workers in the annotation industry.