Synthetic data generation has emerged as a crucial technique for addressing various challenges, including data privacy, scarcity and bias. By creating artificial data that mimics real-world datasets, ...

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 ...

Whether AI developers scrape or license data, each approach poses challenges for content rights holders and AI companies Sophisticated systems capable of generating high-quality synthetic data can ...

First, institutions must ensure that synthetic datasets are continuously recalibrated against fresh, real-world evidence. The world moves, behaviors shift, economies cycle and disease patterns evolve.

(The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.) Ambuj Tewari, University of Michigan (THE CONVERSATION) You’ve just finished a ...

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 ...

Advancements in Natural Language Processing (NLP) models and generative artificial intelligence (GAI) models have fundamentally changed the way that we think of human interaction—think AI chatbots and ...

The enterprise data analytics software company SAS Institute Inc. is moving into the realm of synthetic data to boost its artificial intelligence portfolio with the acquisition of intellectual ...

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 ...

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 ...

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.