Synthetic Data for Machine Learning
Obtaining good data for machine learning can be a difficult, expensive, and time-consuming process. Synthetic data can provide a way to speed up model development and improve accuracy.
One of the most powerful use cases for synthetic data is in 3D simulations. This allows companies to train robots, self-driving cars, and other autonomous systems at a scale and speed that is not possible with real-world data.
Cost-Effectiveness
Many machine learning models are based on a limited data set. Accumulating new data sets can be difficult or expensive. In addition, real-world data may be prone to errors and biases that could impair model performance. Synthetic data is a way to address these problems. It can provide better data quality, balance, and variety, as well as auto-completing missing values. It also helps ensure data privacy by removing all personal information from the original dataset.
Moreover, synthetic data can be cost-effective for businesses that are trying to accelerate their machine learning development and testing. It can reduce the costs of deploying cameras, renting drones or satellites, and hiring annotators for error-prone manual tagging of images.
It can also help level the playing field for small startups that otherwise might not be able to compete with established companies. For example, Google’s Waymo has spent billions and a decade collecting miles of driving data to beat competitors.
Scalability
Synthetic data allows teams to work with sensitive or regulated datasets without putting the company’s reputation at risk. For example, a healthcare provider could use synthetic data of healthy lung imagery and early-stage tumors to train a classifier to detect lung cancer in patients. This approach also mitigates privacy concerns for companies dealing with personal health information.
It’s not only useful for enterprises whose business is data-driven but also for startups and other businesses that don’t have the resources to collect and store large volumes of real-world data. Using the same techniques, these startups can generate realistic data sets to test and develop machine learning models.
Synthetic data has been around in one form or another for decades, from 3D flight simulators to scientific simulations of everything from atoms to galaxies. But it’s now making its way into the business world, with companies such as BMW and NVIDIA Omniverse leveraging it to simulate real-world scenarios like autonomous vehicle testing in a simulated parking lot or optimizing how assembly workers and robots work together.
Complexity
While copious amounts of high-quality data remain a prerequisite for machine learning success, real-world data sets are often hard to come by due to privacy concerns or time and cost constraints. Synthetic data can offer these organizations a solution.
The process of generating synthetic data requires a sophisticated system that uses AI to learn the patterns, correlations, and statistical properties of a sample dataset. It then generates new data that looks, feels, and means the same as the original, but it does not reveal any personal information.
The process is scalable and cost-efficient, but it can also introduce biases that are not present in the original dataset. For example, if the original dataset includes multiple images of billionaires with varying net worth values, it will be possible to map these unique data points back to the same individual in the synthetic datasets. To avoid this, organizations must regularly monitor changes in real-world data and adjust their data generation accordingly.
Privacy
Synthetic data generation is growing in popularity as a way to train machine learning algorithms that need massive amounts of labeled training data. It can also be used for quality assurance testing and software development. However, it’s important to know that synthetic data can pose privacy risks. The risk is related to the ability to re-identify real data subjects based on the simulated data. It’s important to conduct a privacy assurance assessment before using synthetic data.
This assessment can be performed by a qualified data team. The team will evaluate how much the simulated data matches the original, and whether any unique values remain. This will help determine the likelihood of re-identification and assess whether there’s a high risk that sensitive information about real people would be revealed. As long as the re-identification risk remains low, it’s okay to use synthetic data for machine learning. This can help reduce bias and democratize data while preserving privacy.
