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 ...

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 ...

In the evolving landscape of artificial intelligence (AI), the assumption that more data lead to better models has driven unchecked reliance on synthetic data to augment training datasets. Although ...

The business analytics company’s new Data Maker allows organizations to create synthetic data for AI training.

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

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 ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Mostly AI is moving to address a major AI training bottleneck for ...

As more companies invest in generative AI (gen AI) for bespoke use cases and products, proprietary data is becoming increasingly important to training large language models (LLMs). Unlike ChatGPT, ...