Synthetic Data Might be Quite Useful for Domain-Specific or Privacy-Critical Use Cases

There might be upsides and downsides as generative artificial intelligence systems--after crawling the whole internet--likely start to learn from each other. To be sure, some new data stores conceivably can be crawled, but that process will increasingly be expensive and involve much smaller, more-specialized sets of data, such as some proprietary enterprise content. 

But all that will be incremental. What is likely to happen is that models start to learn from each other, using “synthetic data” that is artificially generated mimicking real-world data in its statistical properties and structure, but without actual real-world data points. 

That could have both good and bad implications. Perhaps synthetic data can help compensate for scenarios where training data is under-represented or unavailable. That can help improve model performance and robustness. 

Model Type	Benefits from Synthetic Data
Domain-Specific Models (e.g., medical, legal, financial)	Access to large, private, and high-quality datasets is crucial for performance. Synthetic data can bridge this gap.
Models for Low-Resource Languages	Synthetic data can augment limited real-world data, improving model performance for languages with fewer available resources.
Models Requiring Diverse and Sensitive Data	Generating synthetic data can protect privacy while providing exposure to diverse scenarios, reducing biases.
Models for Data Augmentation	Synthetic data can expand training datasets, improving model robustness and generalization.

Since synthetic data doesn't contain real individuals' information, it can be used to train language models on sensitive topics without risking privacy violations.

Carefully generated synthetic data can be used to balance datasets and reduce biases present in real-world data, potentially leading to fairer language models. 

In domains where real data is scarce or expensive to obtain, synthetic data might provide a viable alternative for training language models. Cost effectiveness is a possible advantage as well. 

Also, models could be pre-trained on large synthetic datasets before fine-tuning on smaller real-world datasets, potentially improving performance in data-limited domains. Likewise, synthetic data could be generated to support training for languages with limited real-world data available.

On the other hand, there are potential downsides. When AI systems learn from each other, there's a risk of amplifying existing biases present in the original training data. As models build upon each other's outputs, subtle biases can become more pronounced over time.

With AI systems learning from each other, there's a danger of converging on similar outputs and losing diversity of perspectives.

Of course, it might not always be the case that synthetic data accurately represents real-world scenarios. The same danger exists in terms of models learning incorrect information from other models.

But there are many use cases where synthetic data is necessary or useful, including domain-specific models or privacy-sensitive models.