Synthetic Data Generation
Synthetic data is generated with models to create a statistically representative replica of a production dataset without the sensitive information. This helps businesses avoid privacy issues and reduce bias compared to data sourced from real-world sources.
For fields with highly critical distributions (e.g. dates in time series), the exact distribution may not be preserved when using a synthetic dataset. In such cases, consider using different distributions or generating a single subject table for the data.
Datasets
There are a plethora of tools for creating synthetic data generation, from rule-based methods to generative models. NVIDIA, for example, offers an open-source tool called Isaac Replicator that lets users generate billions of rows of data in a fraction of the time it takes to collect real-world data.
Synthetic data has several benefits, including overcoming privacy concerns. Because it doesn’t contain personally identifying information, it can be shared more freely and used for innovation and monetization without fear of privacy breaches or discrimination.
It’s also much faster and cheaper to create than gathering and cleaning real-world data, which can be time-consuming and costly. It’s also more consistent than real data, which can be variable due to its natural origins. This can make it easier for businesses to train and test AI algorithms. This is a key factor in increasing agility and competitiveness in the market. The ability to create consistent and diverse datasets can also help overcome AI bias by ensuring that minority groups are well represented.
Models
A variety of models can be used to create synthetic data. Rules-based algorithms can replicate the distributions and structure of real datasets, while generative models such as generative adversarial networks can generate data sets that look real but are essentially random.
Many companies use generative models to test their software, and some generate synthetic data for training machine learning models or quality assurance in DevOps environments. Other common use cases include modeling the impact of a new roadway, mall, or other infrastructure, or creating simulations to inform transportation planning.
Synthetic data can also be created for research purposes, including overcoming bias and making sure minority classes are well represented. For example, BMW uses gaming engines to simulate car assembly processes and train its robots to improve the way they work with human colleagues. Other firms, such as UC Davis Health in Sacramento, California, are using synthetic data to help them develop AIML models to predict and diagnose diseases.
Synthesizing
The results of generating synthetic data can be used for many use cases, such as data testing and analytics. It also helps companies overcome privacy concerns. For example, it can be used to provide models for model training without risking the privacy of PII. It can also be used to overcome AI bias by ensuring that minority classes are represented in the generated data.
Synthetic data can be created using a variety of tools, such as data cloning and data aging. These techniques replicate real-life data and modify it to produce datasets that are useful for training machine learning algorithms. They can also fill in missing values and remove outliers to improve the quality of the data. Then they can either back- or forward-date the data to recreate time-based scenarios for testing and training purposes. They can even generate datasets that are based on theoretical distributions, such as variation autoencoders or generative adversarial networks (GAN). These models convert the original data into simpler distributions that are easier to learn from.
Training
To train machine learning models, they need a lot of data. But access to real data may be limited by privacy concerns or financial or time constraints. This is where synthetic data comes in.
The data synthesis process works by training an algorithm on a sample of the original data set. Once it has learned the patterns, correlations, and statistical properties of the data, it then produces statistically identical synthetic data. This new data looks, feels, and means the same as the original, but does not reveal personally identifying information.
Several tools are available for generating high-quality synthetic data. These include Twinify, which provides a free service to create test data for machine learning applications and databases; Generator, which offers software for database obfuscation and generation for testing and training; and Omnivores, a virtual world where NVIDIA has developed applications like Isaac Sim for robotics and DRIVE Sim for self-driving cars.
