A Chinese research team has built an artificial intelligence system that never touched real-world data, yet still runs on ...

The team's SynthSmith data pipeline develops a coding model that overcomes scarcity of real-world data to improve AI models ...

Every synthetic dataset generated today trains tomorrow's models while potentially poisoning the ecosystem those models ...

In a time when health systems are struggling to gain meaningful insights from data – and simultaneously aware that safeguarding patient privacy is essential – synthetic data offers a lot of potential.

Artificial Intelligence (AI) models are only as good as the data on which they are trained. Yet gathering enough high-quality ...

The first time synthetic data was used to mimic real-world data was in 1993 by Donald Rubin. He created data that was statistically like genuine data, but without the risk of privacy compromise. With ...

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 ...

Get the latest federal technology news delivered to your inbox. Presented by GDIT: Art of the possible By GDIT: Art of the possible Presented by GDIT: Art of the possible By GDIT: Art of the possible ...

Synthetic data is becoming an increasingly attractive tool for companies looking to accelerate their AI development. By simulating realistic scenarios, it can protect privacy, speed up model training ...

Editor’s note: This article, distributed by The Associated Press, was originally published on The Conversation website. The Conversation is an independent and nonprofit source of news, analysis and ...

The generation of synthetic data in healthcare has emerged as a promising solution to surmount longstanding challenges inherent in the use of real patient data. By replicating the underlying ...