Artificial Intelligence Creates Synthetic Data for Machine Learning


In recent years, artificial intelligence (AI) has advanced significantly, and the discipline of machine learning is one area where this has been particularly clear. Getting enough high-quality data to train models is one of the biggest problems that machine learning practitioner’s face. Here's where artificial data comes into play.

Artificial Intelligence Creates Synthetic Data for Machine Learning

Artificially produced synthetic data can be used to train machine learning algorithms. The advantages of employing artificial intelligence to generate synthetic data will be examined in this article, along with some of the challenges that still need to be cleared.

Generative Adversarial Networks are one of the main tools that artificial intelligence is used to produce synthetic data (GANs). A generator plus a Bayesian classifier make up a GAN, a particular kind of neural network. The generator oversees producing fake data, while the discriminator determines if the data is real or fake. Together, the two networks are trained, with the generator attempting to produce data that the discriminator finds difficult to separate from actual data and the discriminator working to become more adept at recognizing artificial information.

Synthetic Data

Two sources exist for synthetic data −

  • Real World Data

  • Simulated Data

Although personally identifying information (PII) and personal health information (PHI) can be removed from real-world data, this does not completely protect privacy since the data records can still be matched to other sources that can be used to identify individuals. Like the COVID-19 example, the anonymized data must be mixed again in a way that keeps all the data set's statistical characteristics for the machine learning algorithms to make accurate inferences and develop accurate rules.

In some cases, a lack of real-world data is a challenge for machine learning. Sometimes it would be impractical or too expensive to acquire data from the real world. Simulated data may occasionally be close enough to real-world instances for machine learning algorithms to recognise it. The self-driving car industry, for instance, blends real sensor data from moving vehicles with simulated data from driving simulations (even video games like Grand Theft Auto).

The use of synthetic data in machine learning has several advantages. The fact that it can be used to supplement small real-world data sets is one of the key advantages. For instance, if a business only has a limited number of photographs of a certain product, they can use a GAN to create artificial images of the product, which can then be used to train a machine learning model. This can lessen the chance of overfitting and increase the model's accuracy.

The ability to manufacture data for jobs for which real-world data collection is challenging or impossible is another advantage of synthetic data. Consider a scenario in which a business wants to train a machine learning model to forecast the propensity of a specific disease in patients. Yet, they are unable to obtain actual patient data because of privacy challenges. In this situation, they can create fictitious patient data using a GAN and then train the model with it. Several AI methods can be used to produce synthetic data in addition to GANs. For instance, a particular kind of neural network called a variational autoencoder (VAE) can be used to create synthetic data by studying the underlying distribution of a dataset. In addition, methods like data imputation, data augmentation, and data simulation can be applied to generate artificial data.

Unfortunately, adopting synthetic data comes with several difficulties that must be overcome. The requirement that the synthetic data be representational of the real-world data presents one of the key obstacles. The machine learning model may not function well if the synthetic data does not precisely match the real-world data. Another challenge is that the synthetic data must be sufficiently varied to account for every scenario that the model might face in the actual world.

Another challenge is that biassed models might be produced using fake data. Biased models are models that have learned to produce inaccurate predictions for certain groups of people. For example, a model that is trained on synthetic data that is biased towards a particular race or gender may produce inaccurate predictions for people who are not in that group. To avoid this, it is important to ensure that the synthetic data is diverse and representative of the real-world data.

Synthetic Data Applications

  • Software Testing that is automated for DevOps. Test data has always been necessary for software development, but today's quick Agile development cycles of DevOps demand more test data than ever.

  • Development of self-driving vehicles. Operating sensor cars on actual roads is an expensive and time-consuming procedure and combining data from driving simulations gives self-driving AI a considerably larger dataset to train on.

  • Robots and Automation in manufacturing. Synthetic data can speed up the training of AI systems in robotics and manufacturing applications because real-world data collecting can be sluggish and expensive, like automobile data collection.

  • Monetary services. Personal financial data is subject to strict confidentiality restrictions, just like healthcare data, and synthetic data provides developers and business users with access to larger datasets without invading privacy.

  • Consumer Behavior Simulations in Marketing. Since the GDPR and other restrictions apply to actual consumer online behavior, marketing AI can be trained more broadly and thoroughly using a synthetic dataset.

  • Clinical Medical Investigation. Since PHI is heavily regulated, artificial intelligence (AI) and machine learning are made viable in situations where datasets might otherwise be too limited to be helpful.

  • Facial Identification to avoid privacy violations and biases from underrepresented types of faces, synthetic facial data can be used instead of real-world pictures to train facial recognition.


In conclusion, AI is being used to create synthetic data that can be used to train machine learning models. Synthetic data can be used to augment limited real-world data sets, as well as to create data for tasks that are difficult or impossible to collect real-world data for. However, it is important to ensure that the synthetic data is representative of the real-world