How Synthetic Data Is Boosting Ai At Scale

Artificial Intelligence (AI) relies heavily on large, diverse and meticulously-labeled datasets to train Machine Learning (ML) algorithms. However, collecting and labeling vast datasets with millions of elements sourced from the real world is time-consuming and expensive. As a result, those training ML models have started to rely heavily on synthetic data, or data that is artificially generated rather than produced by real-world events. Synthetic data has soared in popularity in recent years, presenting a viable solution to the data-quality problem and offering the potential to reshape large-scale ML deployments. According to a Gartner study, synthetic data is expected to account for 60% of all data.