![]() This makes it possible to share datasets for collaborative research, open data initiatives, or data analysis competitions without exposing sensitive information. Simulated environments can provide such data without the need for costly and time-consuming real-world interactions.ĭata Sharing: Synthetic data can be used to create datasets that maintain the statistical properties of the original data while ensuring privacy and confidentiality. Synthetic data can be particularly useful in reinforcement learning where the agent requires a lot of interaction with its environment. This can help overcome challenges like imbalanced classes in the dataset, lack of labeled data, or data privacy concerns. The three main uses for synthetic data includeĪrtificial Intelligence (AI) and Machine Learning (ML): Synthetic data is extensively used in training machine learning models, especially in cases where real-world data is scarce or sensitive. When we speak about synthetic data, we mean synthetic data that is created using Generative Models / Machine Learning. For example, a weather simulation might generate synthetic data about temperature, wind speed, and precipitation. ![]() Simulation: Simulation involves creating a model of a system and then using that model to generate data. This can be used to generate synthetic data about complex systems, like traffic patterns or financial markets. #Data creator generatorThe two networks are trained together, with the generator network improving its ability to create realistic data as the discriminator network gets better at spotting fakes.Īgent-based Modeling: This technique creates individual 'agents', each with their own behaviors and rules, and allows them to interact with each other in a simulated environment. GANs, for example, involve two neural networks: a generator network that produces synthetic data and a discriminator network that tries to distinguish between the real and synthetic data. These models are trained on real-world data and learn to generate new data that is similar to the training data. ![]() Generative Models or Machine Learning: Generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and others, are a powerful way to generate synthetic data. For example, you might generate synthetic data that follows a particular distribution, such as the normal distribution, or you can generate synthetic data that has the same mean and variance as a real-world dataset. Statistical or Rule based Methods: These methods generate data based on statistical properties or defined rules. The generation of synthetic data can vary greatly depending on the specific requirements of a project. The result is data that looks and feels just like real-world data and that contains all its statistical information but no Personal Identifiable Information (PII). For this kind of synthetic data Generative AI is used to create data that can be highly complex - far beyond what a user could describe with simple rules. When we talk about synthetic data, we mean machine learning generated synthetic data. For example: create a numerical variable without any decimals with a range from 100 to 1,000 with a normal distribution. That is a user would define specifically the rules upon which the data would be generated. In the past synthetic data was most often understood as “rule based” synthetic data. ![]() The term is not new and has been around for many years. It is typically created with the help of algorithms or simulations and often used in settings where real-world data is hard to collect or where privacy concerns exist. Synthetic data is data that's artificially generated rather than collected from real-world events. ![]()
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