Amazon uses synthetic data to train Alexa’s language understanding capabilities, ensuring that the AI system can handle diverse user interactions without relying on real user data. As the field of artificial intelligence (AI) continues to evolve, there’s a growing interest in leveraging synthetic data to enhance AI systems. But what is synthetic data, and how to generate them? Let’s find out in the following sections.
What is Synthetic Data?
Synthetic data is artificially generated data that is designed to mimic all the properties of the source dataset. Synthetic data doesn’t contain any real world facts but statistically represents the source dataset. Synthetic data masks the real data to offer privacy and security while serving as a quality dataset for training and testing AI and ML models.
Use-cases of Synthetic Data
The UK’s National Health Service (NHS) has converted real-world data on patient admissions for accidents and emergency (A&E) treatment into a statistically similar but anonymized open-source dataset to help NHS care organizations better understand and meet the needs of patients and healthcare providers. Let us check out some scenarios where synthetic data can be implemented.
Training machine learning models
Synthetic data is a popular choice for training machine learning models because it offers a versatile alternative to real-world data. Gathering large amounts of real-world data can be challenging, especially when dealing with sensitive information or navigating regulations like GDPR. Real-world data often comes with its own set of issues—bias, incompleteness, and errors.
Synthetic data steps in to fill this gap. It supplements or even replaces real-world data, allowing machine learning models to train on a broader and more diverse dataset. This approach boosts model performance and helps it generalize better across different scenarios.
Reducing bias in your data
Synthetic data helps reduce bias and increase fairness in datasets. Real-world data gathers bias like being skewed towards certain groups, leading to biased machine learning models. The output from such ML models wouldn’t holistically represent all groups. For instance, if a training dataset primarily represents one race or gender, it fails to accurately reflect other groups’ experiences.
Researchers actively use synthetic data to create more balanced datasets. By controlling demographic characteristics like gender and race, they ensure that the data better represents the population it serves. This approach helps build more inclusive and fair machine learning models.
Enhance privacy by protecting personally identifiable information
Synthetic data enhances privacy by safeguarding personally identifiable information (PII). Real-world data often contains sensitive details that cannot be shared publicly. Researchers use synthetic data to represent this information securely, allowing it to be used for research without compromising individual privacy.
PII includes details like names, addresses, phone numbers, and Social Security numbers. It also covers financial records, medical data, and biometric information. Under GDPR, organizations must protect this data and obtain explicit consent before collecting, using, or sharing it.
Synthetic Data Types: Structured and Unstructured
Synthetic data comes in various forms, each tailored to meet specific needs and applications. At its core, synthetic data is categorized into two main types: structured and unstructured. Let’s dive into what makes each unique and how they’re used.
Structured synthetic data
Imagine having a perfectly organized library where every book is shelved in its precise place. That’s what structured synthetic data offers—precisely defined fields, values, and relationships, all following a predetermined format. It’s ideal for scenarios requiring large volumes of consistent and predictable data.
Examples of Structured Synthetic Data:
- Numerical Data: Financial transaction records that mimic real-world patterns.
- Categorical Data: Synthetic customer demographics that help test marketing strategies.
- Temporal Data: Time series data that simulates seasonal trends or historical events.
This type of data is invaluable for testing software and models under controlled conditions, ensuring reproducibility and reliability. It includes synthetic census data, financial records, or transaction histories—perfect for rigorous testing without exposing sensitive real-world data.
Unstructured Synthetic Data
Now, picture a vibrant art gallery where creativity knows no bounds. Unstructured synthetic data replicates the randomness and unpredictability of real-world data, making it perfect for applications like natural language processing and image recognition.
Examples of Unstructured Synthetic Data:
- Textual Data: Synthetic social media posts that mimic real conversations.
- Geospatial Data: Maps that simulate real-world locations and environments.
- Audio Data: Speech recordings that sound as natural as real speech.
- Visual Data: Images and videos that retain the essence of real-world visuals.
Unstructured synthetic data is crucial for training advanced AI and ML models. It provides lifelike data while addressing privacy concerns, ensuring that models learn from diverse, realistic scenarios without compromising sensitive information.
How to generate synthetic data
To help us achieve the level of likeness to real data, we can employ some available tools and technologies out there in the market.
Synthetic text generators using LLMs
Large Language Models (LLMs) like GPT-4o and Gemini play a crucial role in generating high-quality synthetic data by leveraging their advanced capabilities to create diverse, realistic, and privacy-compliant datasets. NVIDIA’s Nemotron-4 340B generates diverse synthetic data that mimics real-world characteristics, improving the performance and robustness of custom LLMs across various domains.
LLMs can generate synthetic data much faster than traditional methods, which often involve manual collection and annotation of real-world data. This speed is particularly beneficial in scenarios where large datasets are needed quickly, such as training machine learning models or fine-tuning LLMs themselves. LLMs can produce datasets that are more comprehensive and diverse than human-labeled ones. They can generate synthetic queries and contexts that mimic real-world scenarios, ensuring that the data is nuanced and contextually relevant.
Generative Adversarial Networks (GANs)
GANs consist of two neural networks: a generator and a discriminator. The generator creates synthetic data, while the discriminator evaluates whether the data is real or synthetic. Through adversarial training, both networks improve simultaneously, with the generator learning to produce more realistic data and the discriminator becoming better at distinguishing between real and synthetic data
GANs have been used for a variety of applications, including:
- Financial Models: GANs are used to generate synthetic financial data that closely aligns with real-world data, enhancing model precision and performance.
- Medical Records: GANs can create synthetic time-series medical records, which are useful for training models without exposing sensitive patient information.
- Tabular Data: GANs are also applied to generate synthetic tabular data, which is essential for various business applications
Variational Autoenoders (VAEs)
Another approach utilizes Variational Autoenoders. VAEs are ANNs (artificial neural network architectures) that learn the underlying distribution of the source data, allowing them to generate new data points that statistically resemble the original data. This is achieved by capturing the mean and variance of the data distribution, ensuring that the synthetic data retains essential statistical properties. A VAE consists of an encoder and a decoder. The encoder maps input data to a latent space, capturing the essential features of the data. The decoder generates new data by sampling from this latent space.
VAEs are particularly useful for augmenting small datasets. By generating additional synthetic data points, they can help increase the size of a dataset, which is beneficial for improving the performance of machine learning models. VAEs provide a more interpretable latent space compared to GANs. This can be beneficial when understanding the underlying structure of the data is important. VAEs offer a probabilistic approach to data generation, allowing for sampling from a learned distribution. This is particularly useful for creating diverse synthetic data that captures the variability of real data.
Conclusion
Synthetic data is a powerful tool that helps organizations overcome data challenges and drive innovation. As AI technology advances, synthetic data will play an increasingly significant role. It addresses real-world data limitations like bias and incompleteness while protecting privacy by removing sensitive information. With regulations like GDPR emphasizing data privacy, synthetic data is essential for building robust AI models without compromising personal data.
