Originally published on Towards AI.
In the evolving landscape of artificial intelligence, data remains the fuel that powers innovation. But what happens when acquiring real-world data becomes challenging, expensive, or even impossible?
Enter synthetic data generation — a groundbreaking technique that leverages language models to create high-quality, realistic datasets. Consider training a language model on medical records without breaching privacy laws, or developing a customer interaction model without access to private conversation logs, or designing autonomous driving systems where collecting data on rare edge cases is nearly impossible. Synthetic data bridges gaps in data availability while maintaining the realism needed for effective AI training.
