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 ...

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

AxxonAI, developed by Athenatech.ai, a Malaysia Digital –certified company, is a next-generation synthetic GenAI platform that empowers organisations to maximise the value of their data—without ...

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 ...

AI scaling faces diminishing returns due to the growing scarcity of high-quality, high-entropy data from the internet, pushing the industry towards richer, synthetic data. Nvidia is strategically ...

Synthetic data has rapidly transitioned from experimental curiosity to enterprise standard. Companies now rely on it to train credit models, medical diagnostic systems, customer segmentation engines, ...