Companies can’t avoid working with data, but management of that data can pose serious challenges. Customer and other personal data keep escaping, courtesy of breaches that surged 78% last year in the ...

Artificial intelligence systems are only as powerful as the data they are trained on. High-quality labeled datasets determine whether a model performs with precision or fails in production.

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 ...

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 ...

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 ...

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard ...

Machine learning models are usually complimented for their intelligence. However, their success mostly hinges on one fundamental aspect: data labeling for machine learning. A model has to get familiar ...

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 ...

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 ...

As AI-generated deepfakes grow more realistic, they pose new threats to scientific integrity. This article unpacks how ...