There is No Longer Any Excuse for Not Using Fully Synthetic Data for Developing and Training Your AI

​By 2024, Gartner predicts 60% of data for AI will be synthetic to simulate reality, future scenarios and de-risk AI, up from 1% in 2021. Investment in AI will continue to accelerate by organizations implementing solutions, as well as by industries looking to grow through AI technologies and AI-based businesses.  The beauty of synthesizing data on a computer is that it can be procured on-demand, customized to your exact specifications, and produced in nearly limitless quantities.  Training a billion-parameter foundation model takes time and money. Replacing even a fraction of real-world training data with synthetic data can make it faster and cheaper to train and deploy AI models of all sizes.  Collecting samples of all potential scenarios, including rare, so-called edge cases, would be impractical to impossible. Synthetic data makes it possible to create customized data to fill the gaps.  Large models almost always contain hidden biases, too, picked up from the articles and images they have ingested.  Use of synthetic data will enable you to test to ensure that these are found and corrected.