As AI demand outpaces the availability of high-quality training data, synthetic data offers a path forward. We unpack how synthetic datasets help teams overcome data scarcity to build production-ready ...

I take it with a grain of salt when a book author makes a comment like “This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default ...

Currently, deep learning is the most important technique for solving many complex machine vision problems. State-of-the-art deep learning models typically contain a very large number of parameters ...

Ludovic Lassauce, Chief Product Officer at SIMO, a SaaS company, providing connected experience for Laptop, CPE and Portable Wi-Fi devices. Many AI startups that received substantial funding from ...

Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for ...

Synthetic data allows regulators to test the resilience of critical infrastructure defenders under extreme hypothetical scenarios.

The first time synthetic data was used to mimic real-world data was in 1993 by Donald Rubin. He created data that was statistically like genuine data, but without the risk of privacy compromise. With ...

New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...

Artificial intelligence (AI) is transforming our world, but within this broad domain, two distinct technologies often confuse people: machine learning (ML) and generative AI. While both are ...