Synthetic Data Is a Dangerous Teacher

Synthetic data, also known as fake or artificial data, is generated by a computer program or algorithm to resemble real data. While it may seem like a useful tool for training machine learning models, it can also be a dangerous teacher.

One of the biggest risks of using synthetic data is that it may not accurately represent real-world scenarios. This can lead to biased models that perform poorly in practice.

Furthermore, relying too heavily on synthetic data can result in overfitting, where the model performs well on the training data but poorly on new, unseen data.

Another issue with synthetic data is that it may lack the complexity and nuances of real data. This can limit the model’s ability to generalize to new situations.

In addition, using synthetic data can also pose privacy concerns, as it may not accurately anonymize sensitive information.

Overall, while synthetic data has its uses in training machine learning models, it is important to approach it with caution and always validate the model’s performance on real data.