While many organizations are experimenting with synthetic data, few are focusing on scalability and building AI-ready data ...

As artificial intelligence models continue to evolve at ever-increasing speed, the demand for training data and the ability to test capabilities grows alongside them. But in a world with equally ...

AI and ML algorithms rely heavily on vast data for training and development. However, the availability of high-quality, diverse, and secure data can be a significant challenge. In fact, upon not being ...

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 ...

This article is part of a VB special issue. Read the full series here: The quest for Nirvana: Applying AI at scale. Artificial intelligence (AI) relies heavily on large, diverse and ...

Strict data privacy regulations have compelled companies to transition to using synthetic data, the ideal substitute for real data, containing similar insights and properties yet is more privacy-safe ...

* 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 ...

To reduce the threat of model loss, synthetic data corruption and insight erosion, CXOs must create a new class of "AI-aware" ...