Synthetic data generation has emerged as a crucial technique for addressing various challenges, including data privacy, scarcity and bias. By creating artificial data that mimics real-world ...

Synthetic data generation is the process of creating artificial datasets that mimic real-world data and can be used to test or train agents or models.

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The primary benefits of synthetic data generation include the ability to generate large and diverse datasets, improved data privacy and security, reduced dependency on real-world data collection ...

AWS announced that users can now create labeled synthetic data with Amazon SageMaker Ground Truth. SageMaker Ground Truth is a data labeling service that makes it simple to label data and allows ...

Databricks Inc. today introduced an application programming interface that customers can use to generate synthetic data for their machine learning projects. The API is available in Mosaic AI Agent ...

Beware of AI 'model collapse': How training on synthetic data pollutes the next generation Oxford scholars found that large language models fed a diet of 'cannibal' data, created by other LLMs ...

In this edition of This Week in AI, TechCrunch's AI newsletter, we take a look at the big tech companies using synthetic data to train flagship models.