What are the challenges in using synthetic data for AI training?

What are the challenges in using synthetic data for AI training?

Some challenges include ensuring data quality, maintaining diversity, and avoiding overfitting.

How do you ensure the quality of synthetic data?

Quality can be ensured by validating synthetic data against real-world data and using robust generation techniques.

Can synthetic data lead to biased models?

Yes, if the synthetic data reflects biases present in the training algorithms or real data, it can lead to biased models.

What are some best practices for using synthetic data?

Best practices include combining synthetic data with real data, continuous validation, and using diverse generation methods.