Accelerating AI with Synthetic Data

Chapter 1. Defining Synthetic Data

Interest in synthetic data has been growing quite rapidly over the last few years. This has been driven by two simultaneous trends. The first is the demand for large amounts of data to train and build artificial intelligence and machine learning (AIML) models. The second is recent work that has demonstrated effective methods to generate high-quality synthetic data. Both have resulted in the recognition that synthetic data can solve some difficult problems quite effectively, especially within the AIML community. Groups and businesses within companies like NVIDIA, IBM, and Alphabet, as well as agencies such as the US Census Bureau, have adopted different types of data synthesis to support model building, application development, and data dissemination.

This report provides a general overview of synthetic data generation, with a focus on the business value and use cases, and high-level coverage of techniques and implementation practices. We aim to answer the questions that a business reader would typically ask (and has typically asked), but at the same time provide some direction to analytics leadership seeking to understand the options available and where to look to get started.

We show how synthetic data can accelerate AIML projects. Some problems that can be tackled by using synthetic data would be too costly or dangerous (e.g., in the case of training models controlling autonomous vehicles) to solve using more traditional methods, or simply ...