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 ...

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 ...

Real data is not sufficient to train better artificial intelligence models, experts said at South by Southwest. But simulated data must be done right. Jon covers artificial intelligence. He previously ...

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 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.

* 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 ...

We used Tonic Fabricate to generate a fully synthetic email corpus, then RL fine-tuned an open-source model against it. The ...

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 ...

In today’s dynamic global economy, financial institutions are increasingly confronted with uncertainties that defy historical precedent. Traditional stress testing long reliant on past market data ...