Data is a crucial tool for developing machine learning models, but it’s not always easy to access. Using synthetic data solves many of these challenges.

It’s also cheaper and more convenient than real-world data. With these benefits, the industry is growing rapidly. There are now dozens of companies that provide synthetic data for various use cases.

Increased Accuracy

Synthetic data creates more accurate datasets by eliminating some of the common biases that real-world data sets can have. It also allows organizations to create more diverse data sets, which helps them better understand their problems and make more informed decisions.

In addition, using synthetic data can save time and money for organizations that are working with limited resources. For example, in the medical field, it can be difficult to find enough patients for MRI scans. Moreover, MRI machines can be expensive, with some costing up to $3 million each.

Synthetic data can help overcome these challenges and speed up the development of medical AI, which could ultimately save lives. However, there are still concerns around privacy and the potential for algorithms to reveal private information or be used to discriminate against people when hiring, renting or making financial decisions. This is why it’s important to use data metrics to measure the accuracy of synthetic data and identify any biases.

More Diverse Datasets

For data scientists who must train models on real-world data, finding enough examples of rare or crucial corner cases can feel like searching for a needle in a haystack. But synthetic data can help address this issue, says Michael Rinehart, VP of AI at multicloud data security platform Securiti AI.

For example, a machine learning engineer might want to build a model to diagnose a rare genetic condition. But they might have only a small sample of patients to use to train the model. Using synthetic data can circumvent this problem, by creating a dataset that mimics real-world conditions but without any sensitive personal information, says Securiti AI’s Rinehart.

Synthetic data isn’t just useful for training AI models, but also for validating them and testing their performance. That’s because the data that drives simulations is often the same as that used in production, with some of it masked for privacy reasons. But this process can still be expensive and time consuming.

Lower Costs

A key benefit of synthetic data is that it can reduce the cost of generating datasets. Collecting real-world data can be expensive and time consuming. For example, medical imaging data requires MRI machines—which can be very costly, with even cutting-edge models running up to $3 million each. These machines also require specialized, sterile rooms to ensure safety.

Using GANs to generate medical imaging data is particularly challenging, as the computational costs and time needed to train these models can be prohibitively expensive. However, the ability to use synthetic data for this purpose can drastically lower these costs.

Synthetic data also doesn’t include any information that could identify real data, allowing companies to share and work with it without risking privacy laws or copyright infringements. This feature is especially important for industries dealing with sensitive customer data like healthcare and financial services. It can help organizations comply with privacy regulations and lessen bias in their data sets.

Increased Accessibility

As AI and ML applications expand into fields as diverse as healthcare, art and financial analysis, concerns have arisen about how data sets are used. Such algorithms can consume vast amounts of information, including personal details that could reveal private information or be used to discriminate against people, for instance in hiring or lending decisions.

While real-world data may be subject to usage restrictions due to privacy rules or regulations, synthetic data can replicate all the statistical properties of the original dataset without exposing any confidential information. This can enable data scientists to overcome such limitations, and enables them to use the same models to analyze new types of data.

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The emergence of synthetic data also has the potential to accelerate the pace at which new insights are generated. For example, NVIDIA’s Omniverse has been enabling developers to test their autonomous driving software in virtual worlds, using synthetic data and domain randomization. Such simulations can help speed up the process of bringing self-driving cars to market, while minimizing risk to human lives.