As AI systems become more sophisticated, the challenges of training them effectively—and responsibly—continue to grow. The use of real-world data often comes with concerns and roadblocks—privacy risks ...

In 2025, organizations operate amid escalating geopolitical tensions, data sovereignty restrictions, and stricter artificial intelligence (AI) regulations like the EU’s Cyber Resilience Act. These ...

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard ...

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 ...

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.

Slator’s Data-for-AI Market Report identifies this shift as a structural change in the AI value chain, where competitive ...

When federal researchers tried to build an AI model to detect fraud in disability claims, they ran into a roadblock: they couldn’t use real claimant data. The workaround wasn’t a weaker model — it was ...

As AI becomes more common and decisions more data-driven, a new(ish) form of information is on the rise: synthetic data. And some proponents say it promises more privacy and other vital benefits. Data ...

* The Matrix analogy: Are we training AI inside simulations? Whether you're a data scientist, CTO, or just curious about how AI models learn, this episode offers a deep dive into one of the most ...

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 ...