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 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 ...

When federal researchers tried to build an AI model to detect fraud in disability claims, they ran into a roadblock: they couldn’t use real claimant data. The workaround wasn’t a weaker model — it was ...

Synthetic data is generated as a replacement for real data that is considered poor quality, fragmented, siloed, sensitive or otherwise unusable for AI training in the enterprise. However, synthetic ...

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 ...

Jēnna Reese is CEO of Connect Centric, a D.C.-based firm that helps Fortune 500s and large nonprofits execute technology initiatives. In the race to modernize with AI, a new kind of risk is quietly ...

Mantis Biotechnology Corp, a San Francisco-based startup, is building simulated human bodies to generate the synthetic ...

Slator’s Data-for-AI Market Report identifies this shift as a structural change in the AI value chain, where competitive ...

Traditionally, AI progress was constrained by one thing above all else: access to data. Not enough volume. Not enough diversity. Not enough coverage of edge cases. That constraint is disappearing.

On November 7, CAAI hosted Dr. Ryan Kappedal, ’19, a Booth alumnus and Technical Lead Manager at Google, for an insightful discussion on the evolving landscape of AI and the critical role of data ...

Generative AI models must be trained on a lot of information, and sometimes you have to make it up.