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 ...

COMMISSIONED: As with any emerging technology, implementing generative AI large language models (LLMs) isn’t easy and it’s totally fair to look side-eyed at anyone who suggests otherwise. From issues ...

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 ...

Global tech executives are racing to deploy autonomous agents over the next two years, but in doing so they face a balancing act: How do you leverage data in a way that maximizes trust and confidence ...

The lesson for marketers is clear: AI’s value lies not only in efficiency, but in expanding the scope of what’s measurable and actionable. Much of the conversation around AI in marketing has centered ...