In the growing field of artificial intelligence (AI) and machine learning (ML), existing methods for collecting and using data are undergoing a significant transformation. As the demand for more optimized and sophisticated algorithms continues to grow, the need for high-quality datasets to train AI/ML modules also grows. However, using real-world data for training comes with its own complexities, such as privacy and regulatory issues and limitations of available datasets. These limitations paved the way for the opposite approach: synthetic data generation. This article discusses this revolutionary paradigm shift as the popularity and demand for synthetic data grows exponentially, showing great potential in reshaping the future of intelligent technologies.
The need to generate synthetic data
The need for synthetic data in AI and ML stems from several challenges associated with real-world data. For example, obtaining large and diverse data sets to train an intelligent machine is a difficult task, especially for industries where data is limited or subject to privacy and regulatory restrictions. Synthetic data helps create artificial data sets that replicate the characteristics of the original data set.
One of the most common shortcomings with existing data sets is biased decision making when new data is obtained. Moreover, privacy concerns surrounding sensitive data hinder the sharing and use of real-world datasets. This scenario is especially true in key industries like healthcare and finance, where compliance and privacy regulations are taken much more seriously. Synthetic data generation plays a key role in overcoming real-world data challenges, making it a perfect solution to data scarcity, diversity and privacy concerns.
Advantages of synthetic data in AI/ML
The benefits of using synthetic data in the fields of artificial intelligence (AI) and machine learning (ML) are multiple, offering advanced solutions for solving challenges associated with real-world datasets. There are many advantages to adopting synthetic data, but the two most significant advantages of leveraging synthetic data to train intelligent models are below.
Overcoming the lack of data
A perennial problem in training AI/ML modules is the lack of data. This problem is solved by synthetic data in the image. In cases where obtaining large data sets is not possible or if there are concerns about the security and privacy of the obtained data, synthetic data act as a realistic alternative.
Accelerated model training
Ideally, training an AI/ML module using real-world data requires significant computing resources. Synthetic data can reduce the computational load to speed up the model training process. This efficiency gain is crucial for making decisions related to time or rapid iteration of the model.
The strengths of synthetic data in AI and ML lie in their ability to provide scalable and diverse datasets without any privacy or regulatory concerns. By addressing the challenges associated with real-world data, synthetic data acts as a catalyst for innovation and empowers researchers to push the boundaries of intelligent systems across domains. According to studies, by 2030, the field of artificial intelligence alone is expected to be valued at around 1811 billion dollars.
Types of synthetic data
There are multiple ways to generate synthetic data based on characteristics that must be replicated from the properties and complexity of real data. Understanding the type of data to be generated plays a key role in training AI/ML modules. Many data management solution providers offer synthetic data generation tools based on the needs of clients to use the generated data and train AI/ML modules.
Procedural generation
Synthetic data is created using algorithmic rules and mathematical models to generate images or procedural methods to create textures, shapes or patterns, allowing for the creation of diverse and realistic datasets. This is most commonly used in computer graphics, games and simulations.
Approaches based on transformation
Modifying existing data sets to create synthetic copies, such as adding noise, introducing perturbations, or simply adding changes to the original data set, falls under the transformation-based approach to generating synthetic data. The most prominent reason for adopting this approach is that it is very effective for increasing data sets, solving problems such as data imbalance, and increasing the diversity of the training data set.
A rule-based approach
As the name suggests, synthetic data that is generated using a predefined set of rules falls into this specific category. These rules are created based on expertise or statistical analysis of existing data sets. This method is particularly useful in the field of health care. For example, rule-based generation of synthetic patient records that adhere to certain medical criteria without compromising individual privacy.
Domain specific approach
Generating synthetic data that is tailored to specific domains. For example, paraphrasing techniques can generate different but semantically similar sentences in the domain of natural language processing (NLP). Domain-specific approaches are designed to capture the intricacies and nuances unique to certain types of data.
Understanding the different synthetic data generation methods is essential to choosing the most optimized approach based on the specific requirements or challenges associated with a particular AI/ML project. Each type serves its own purpose in overcoming data scarcity and privacy issues and improving model generalization.
The rise of synthetic data generation in AI and ML marks a significant shift in the methods of data collection and use. As technology continues to evolve and reach new milestones, the role of synthetic data is emerging as a cornerstone, accelerating innovation and ultimately reshaping the future trajectory of intelligent systems in various domains.