What Is Synthetic Data?

1. Introduction

In this tutorial, we’ll learn about synthetic data and how to create it.

Synthetic data is the data we create using algorithms and simulations to mimic the results of real-world processes. So, it doesn’t come from real people or events but is intended to look as if it does.

In areas like AI, computer security, medicine, and money management, obtaining sufficiently large real-world datasets for training ML models or doing data analysis can be difficult or impossible. It can cost a lot, take a long time to collect, and can put people’s private information at risk. Luckily, synthetic data can solve these problems.

2. What Is Synthetic Data?

Unlike real-world data, which comes from real-world events and processes, such as people using a website, synthetic data comes from algorithms and simulations. It imitates real data, ensuring the important patterns and numbers are the same. As a result, AI models can learn just as well from synthetic data.

For example, imagine a company making self-driving cars. Researchers need a lot of driving information to train AI models. Actual driving data takes a lot of money and time to obtain. So, they can create synthetic driving data using computer programs that simulate driving. Specifically, in these simulations, researchers can vary factors such as weather, traffic, and roads. This way, the models can learn from many different driving situations and get a wide and complete understanding.

3. How Is Synthetic Data Created?

We can create synthetic data in several ways based on the type of data we want.

3.1. Rule-Based Generation

We use rules and logic to make this kind of synthetic data. For instance, when making synthetic customer data details, we can define the following rules:

• Names need to have at least two parts, e.g., John Doe.
• Email addresses should look like this: [email protected].
• Phone numbers should look like this: +6218000000.

By following these rules, we can create large amounts of synthetic data.

3.2. Statistical Models

Real data exhibits patterns we can capture with statistical models to create new synthetic data with those same patterns. We can use different models to fit real and synthetic data, such as normal distribution or linear regression.

The choice of the statistical model depends on what we need to do and the data we have.

3.3. Machine Learning and AI

More advanced techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), produce highly realistic synthetic data creation. These AI-driven models learn patterns from real data and create new, similar data that looks and acts just like it.

3.4. Simulations

We can run simulations to mimic real-world data-generating processes, and then produce synthetic data based on those simulations. This is used a lot in self-driving cars, healthcare, and cyber security.

For examples:

• Self-driving car companies are training AI models using virtual roads.
• Healthcare researchers create synthetic patient data to test medical AI applications.
• Security experts simulating cyberattacks to strengthen security systems.

4. Advantages and Disadvantages

Synthetic data has big advantages, like saving money and keeping things private. However, it also has some downsides.

4.1. Advantages of Synthetic Data

Synthetic data is useful in several ways. First, it’s not from real people, so it doesn’t expose sensitive information. Privacy is an important concern in many medical and financial applications, e.g., in hospitals and banks.

This is also important in testing, where it’s a good idea to use synthetic data. That way, we’re sure that even if there’s a leak, sensitive information will be safe.

Additionally, synthetic data saves money as we can avoid expensive data collection. Since we can create any quantity we want, we can always make sure there’s enough data, even when real data is hard to find.

4.2. Disadvantages of Synthetic Data

While synthetic data offers numerous benefits, it’s important to acknowledge its limitations.

Firstly, there’s the challenge of ensuring it perfectly mirrors real-world data. Because synthetic data is generated artificially, it might not capture all the subtle complexities and variations that occur in real-life situations. This limited real-world representation restricts the training pool to known or simulated scenarios, potentially leading to AI models that struggle with unexpected real-world variations, hindering their long-term adaptability and robustness.

To illustrate this potential for unreality, here’s an example of a generated picture by AI that mimics a human face:

The image looks a bit strange: it consists of tiny squares, like a digital puzzle, and the color combinations are unrealistic. Training a model on images like these can cause underperformance when applied to real images.

Secondly, creating good synthetic data takes skill. It’s not easy. If we’re not careful, the data can exhibit biases or miss important details. This means the AI using it won’t make accurate predictions.

Thirdly, over-reliance on synthetic data during initial training can overfit the model to its patterns. Even with real-world retraining during production in online mode using real data, the model may remain heavily influenced by the synthetic data. This is because synthetic datasets are typically larger, limiting the impact of real-world updates and hindering adaptation to new data.

Finally, although synthetic data protects personal information, it isn’t immune to misuse. For instance, deepfakes raise significant ethical concerns, particularly in the realm of media and politics, where they can be used to spread misinformation.

4.3. Quick Summary

Let’s take a quick look at the pros and cons of synthetic data. This table will break it down:

While synthetic data presents potential challenges, these can be effectively mitigated through careful planning and execution.

Firstly, to ensure synthetic data closely matches real-world scenarios, we need thorough validation, which means comparing fake data to real data, finding differences, and improving how we make it. Moreover, techniques such as domain adaptation can also help AI learn from both real and synthetic datasets.

We must ensure that our synthetic data is realistic and varied to avoid unfair AI results. Regular testing helps catch and fix problems early on.

Finally, we need responsible practices to prevent misuse, especially with deepfakes. This includes clear rules for making and sharing fake media, tools to find deepfakes, and educating the public.

5. Conclusion

In this article, we learned about synthetic data. Using it offers a scalable, privacy-friendly, and cost-effective alternative to traditional data collection methods.

However, while synthetic data offers many benefits, it isn’t a perfect substitute for real datasets.