This post originally appeared on MIT Technology Review
Last year, researchers at Data Science Nigeria noted that engineers looking to train computer-vision algorithms could choose from a wealth of data sets featuring Western clothing, but there were none for African clothing. The team addressed the imbalance by using AI to generate artificial images of African fashion—a whole new data set from scratch.
Such synthetic data sets—computer-generated samples with the same statistical characteristics as the genuine article—are growing more and more common in the data-hungry world of machine learning. These fakes can be used to train AIs in areas where real data is scarce or too sensitive to use, as in the case of medical records or personal financial data.
The idea of synthetic data isn’t new: driverless cars have been trained on virtual streets. But in the last year the technology has become widespread, with a raft of startups and universities offering such services. Datagen and Synthesis AI, for example, supply digital human faces on demand. Others provide synthetic data for finance and insurance. And the Synthetic Data Vault, a project launched in 2021 by MIT’s Data to AI Lab, provides open-source tools for creating a wide range of data types.
This boom in synthetic data sets is driven by generative adversarial networks (GANs), a type of AI that is adept at generating realistic but fake examples, whether of images or medical records.
Proponents claim that synthetic data avoids the bias that is rife in many data sets. But it will only be as unbiased as the real data used to generate it. A GAN trained on fewer Black faces than white, for example, may be able to create a synthetic data set with a higher proportion of Black faces, but those faces may end up being less lifelike given the limited original data.
Join us March 29-30 at EmTech Digital, our signature AI conference, to hear Unity’s Danny Lange talk about how the video game maker is using synthetic data.
