Synthetic data generation has been around for a long time. It is used to provide information and/or to test the effectiveness of algorithms. Conventional ways of obtaining synthetic data include using special tools and software or purchasing it from third parties.
In cases where real data is rare or dangerous to collect (like road accidents that self-driving cars must react to), synthetic data can substitute for the actual events. It is also much cheaper and faster to generate than acquiring and processing real-world data sets.
Synthetic data is artificial data that can be created manually or generated automatically for a variety of use cases. It can be used for all forms of functional and non-functional testing, populating new data environments, or training and validating machine learning algorithms for AI applications.
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How does it work?
There are several ways in which machine learning can use synthetic data. For image data, this includes computer vision algorithms that perform tasks like object recognition and facial detection or the automated creation of bounding boxes in images to identify recognizable elements like trees or cars. Text data can include chatbots and machine translation algorithms that operate on artificially generated text.
Tabular synthetic data enables users to create statistically identical datasets for model training and testing in a fraction of the time it takes to collect real-world data. Neural networks are particularly well suited for synthesizing data because their transformation functions generate distributions that are easier to learn from.
In some cases, using real-world data is too expensive or too precious. For example, Swiss insurance company La Mobiliere used synthetic tabular data to train a churn prediction model that could effectively predict which customers were likely to leave their service. This enabled them to proactively contact these customers with offers they knew would be effective.
Synthetic data generation provides a way to scale and train AI models at high-performance levels without compromising the privacy of real-world data. This is especially important in regulated industries like healthcare, finance, and education.
There are many ways to generate synthetic data, ranging from rules-based to more complex artificial intelligence techniques. For example, the NVIDIA team has created a product that turns 2D video data into full 3D simulations using neural reconstruction engines. Another popular method is data augmentation, which involves adding new data to existing datasets.
Regardless of the method or technology, all methods require a high level of sophistication and automation to deliver quality synthetic data for software testing. This is why companies use a business entity platform approach to synthetic data generation, such as GenRocket, to eliminate risk and enable quality at speed. GenRocket’s intelligent automation ensures consistency, scalability, referential integrity, and privacy for all test data designs. This means Agile teams can design test cases and integrate them into their CI/CD pipelines with confidence.
From training navigational robot models to researching radio signal recognition, synthetic data is useful for a wide variety of purposes. While many applications require a huge dataset, gathering this amount of real-world data can take weeks, months, or even years for some projects. Synthetic data is easier to produce and can be used to test workflows without putting users at risk.
Privacy concerns are another reason why organizations use synthetic data. Machine learning algorithms consume vast amounts of data, some of which reveals personal details. This can raise ethical and legal concerns about the ability of the technology to detect anomalies and discriminate against people.
Synthetic data removes traces of real-world identity, which alleviates privacy concerns and allows companies to use the technology without running afoul of regulations such as GDPR and HIPAA. However, these repackaged data sets can still be vulnerable to attacks. Additionally, the manual processes involved in turning real-world data into synthetic data can introduce biases that undermine the purpose of the process.
Collecting quality data is the most important, and most challenging, part of AI development. However, real-world data is often costly and time-consuming to collect, limiting its availability and utility for machine learning model training.
Synthetic data is an attractive alternative, particularly when it comes to acquiring sensitive datasets, as many companies must adhere to stringent regulatory requirements for handling personal information. Generating synthetic datasets that retain the statistical properties of an original set without PII is possible. And can enable businesses to continue to leverage valuable data for machine learning model training while avoiding potentially costly or illegal liabilities.
Several providers specialize in visual synthetic data generation. including GenRocket’s face generation software which generates realistic photos of people that don’t exist. And Datagen which focuses on human and object data synthesis. Additionally, several providers offer tabular and relational synthetic data generation. These include MOSTLY AI, GenRocket, YData, and Gretel.