Every synthetic dataset generated today trains tomorrow's models while potentially poisoning the ecosystem those models ...
The tangible world we were born into is steadily becoming more homogenized with the digital world we’ve created. Gone are the days when your most sensitive information, like your Social Security ...
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 ...
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 ...
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 ...
Deepfakes have moved from novelty to material enterprise risk, reshaping how organizations assess privacy, security, brand integrity and ...
Artificial Intelligence (AI) models are only as good as the data on which they are trained. Yet gathering enough high-quality ...
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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 ...
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 ...
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