Important points
Synthetic data sets created by artificial intelligence (AI) or machine learning (ML) retain the properties of the original data, but are not real.
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What are synthetic datasets? Why are they important?
Synthetic data sets are artificially created data that can be used in place of real data to train machine learning models, conduct scientific research, develop software, and more. Synthetic data can help gain insight into data properties and underlying mechanisms in situations where creating authentic datasets is difficult. For example, medical research trials rely heavily on sensitive patient data, which can pose privacy risks.
Researchers can use original sensitive data to create synthetic data sets, ultimately creating data sets that many people can access and work with without putting their personal information at risk.
Synthetic data also creates data equity by making datasets accessible to more people.
Businesses and organizations restrict access to data for a variety of reasons, two of which are privacy and the sheer value of the data. Researchers can more easily share synthetic data, making it accessible to more people and organizations. Kalyan Veeramachaneni, principal investigator at MIT, compared the opportunities that synthetic data presents to early career students and individuals to advances in computing power and access to resources over the past two decades. Veeramachaneni recalled that during graduate school, he had difficulty accessing the computing power he needed for his work. Today’s graduate students can easily access that power through cloud computing services. “If I didn’t have access to datasets like I have for the past 10 years, my career would not have happened,” Veeramachaneni said. [1]. Synthetic data can open these opportunities to a growing number of future researchers.
What types of synthetic data are there?
Synthetic data typically falls into one of three categories: fully synthetic, partially synthetic, or hybrid synthetic. Fully synthetic data does not contain any real-world information, whereas partially synthetic data uses real-world information as a foundation, but replaces some of it. Hybrid synthetic data, on the other hand, combines a real dataset with a fully synthetic dataset.
How to create a synthetic dataset
Traditional data analysis can be used to generate synthetic data. However, machine learning and deep learning can also be applied to real data sets to create valuable synthetic data sets.
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Statistical distribution: This method allows data scientists to create statistical models using real data, which can then be used as the basis for creating synthetic data without losing important characteristics of the data.
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Model base: Instead of using data analytics to analyze data, scientists can deploy machine learning algorithms to complete this analysis. Deep learning can use a variety of models, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and large-scale language models, to first understand the characteristics that define data and then generate synthetic or fake data that is faithful to the original data.
Examples of using synthetic datasets
Synthetic data can be used for two main purposes. One is to compensate for situations where it is difficult or impossible to obtain more actual data, and the other is to protect the privacy of data sets containing sensitive information. Consider different scenarios in which you use synthetic data in place of real data.
Difficult or impossible to obtain actual data
You may encounter situations where it is difficult, impossible, or unethical to collect the amount of real data required to accomplish your task. One example is self-driving car crash data. To train a model that can control a vehicle, you need to provide data to the model so that it can understand the complex relationships between the objects it sees and how it reacts as a result. These models can be improved by providing data about crashes and accidents, allowing them to understand why these accidents occur and modify their behavior to avoid them in the future.
However, there are limits to the amount of data scientists can collect from real-world accidents. By using synthetic data, researchers can give their models training material with the underlying patterns and principles of real crash data, without having to crash cars with real people.
Similarly, these concepts can also be applied to software testing. In this case, you will need more data about the likelihood of security breaches and fraudulent transactions so that you can train models that mitigate these events. Synthetic data allows you to create the data you need without putting your development project at risk.
Synthetic data can be used to train machine learning and AI models in a variety of situations beyond computer vision and software testing. In addition to gaining access to previously inaccessible data, you can also control synthetic data to obtain specific types of additional data. Returning to the self-driving car example, you can use synthetic vision to create more images in low-light and low-light conditions and train your model for these scenarios.
read more: Artificial intelligence in medical diagnosis: practical examples and applications
Sensitive data with privacy or security concerns
The second main reason to use synthetic data is to address privacy or security concerns inherent in the data set. For example, scientists and researchers often require sensitive healthcare and medical research data. Researchers can gain a lot of insight by analyzing patient records, how patients respond to drugs in clinical trials, and by reviewing medical images.
Another example of using synthetic data in place of sensitive data is The Global Synthetic Dataset, a joint project between The Counter-Trafficking Data Collaborative and Microsoft Research. This is a synthetic dataset that researchers and organizations can use to study global human trafficking patterns in order to develop evidence-based practices to combat human trafficking. Understanding patterns within this dataset can provide community-based organizations with insight into how to best approach this issue and work on prevention in their communities without putting personal or sensitive information about human trafficking victims at risk.
Both difficult and delicate
Synthetic data can also be used for both purposes, such as being used to train machine learning algorithms to identify medical images containing potentially cancerous tumors. In this case, training the algorithm requires a large amount of potentially sensitive data. Synthetic data solves the problem of creating enough data to effectively train a model without putting real patient information at risk.
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