In this insight, focusing particularly on AI and ML, we look at what synthetic data is, where it comes from and what it’s used for, its challenges, and the implications for businesses.
What Is Synthetic Data?
Synthetic data is data that’s been artificially created rather than derived from real-world activities. Synthetic data is not a new idea and had its first major commercial use in developing simulations for autonomous vehicle systems. Today it’s become particularly useful in Artificial Intelligence (AI) to train Machine Learning algorithms.
Why Use Synthetic Data?
The training and development of the ML algorithms behind AI requires access to substantial amounts of the right kind of data (datasets). With this in mind, there are several good reasons why synthetic data rather than real-world data is used in training AI models. For example:
– Generating and using synthetic data is often less costly and time-consuming than collecting real-world data, thereby leading to cost and time efficiency. Using synthetic data can also save costs and time, for example in providing image data, because no manual data labelling is required.
– Synthetic data also provides a solution to privacy concerns because it doesn’t contain information about real individuals (data protection).
– In terms of bias mitigation, synthetic data can be constructed to represent a wide variety of information, thereby reducing biases in AI models.
– Using synthetic data can also effectively serve the need for quality control because it can be tailored to specific needs, including cases that might be hard to find in real-world data.
– Synthetic data can be generated on demand, in whatever volume is required, making it very practical, convenient, and efficient, plus a very fast way for businesses to acquire data.
– Using synthetic data allows the creation of machine learning models made for scenarios that wouldn’t have been possible before, say if the data either didn’t exist, wasn’t good enough or perhaps had restrictions on it.
– Using synthetic (rather than real-world) data is a way to avoid the challenges posed by restrictions on data in some sectors such as healthcare and finance.
Where Does Synthetic Data Come From? / How Is It Created?
Synthetic data is generated using several key techniques. For example: |
– Simulations and 3D models provide a means to create imagery and objects. This is particularly useful for training image classifiers without having to use real-world data.
– Data augmentation involves taking existing real-world data and applying various transformations to create new data points that retain the underlying patterns and information of the original dataset. For example, if the original data consists of images, data augmentation might include rotating, flipping, or cropping these images to create new variations. These alterations expand the dataset, allowing models to learn from a broader array of examples without the need to collect additional real-world data.
– Generative models, such as Generative Adversarial Networks (GANs), transform datasets while preserving essential characteristics, without exposing sensitive information.
– Diffusion models, which are used in image generators like DALLE-2 can use ‘denoising’ technology to create synthetic data from random inputs.
– Neural radiance fields (NeRFs) can produce synthetic data by manipulating parts of the process of turning two-dimensional images into three-dimensional scenes.
Together, these methods offer a multifaceted approach to generating synthetic data, facilitating a wide range of applications in various fields.
What Are The Drawbacks And Challenges Of Using Synthetic Data?
Although it’s generally a powerful and cost saving tool, generating and using synthetic data does have several challenges and drawbacks. These include:
– Complexity. Crafting high-quality synthetic data is not trivial and needs not only specialised skills but also profound understanding of the underlying domain. This complexity can be a barrier to entry for smaller firms or projects lacking expert resources.
– Effectiveness compared to real-world data. The efficacy of synthetic data when compared to real-world data is still a subject of ongoing investigation. While it can mimic real data, some question whether it really can fully represent the nuanced variations found in genuine datasets.
– Quality and Bias. Incorrectly constructed synthetic data can inadvertently lead to biased or flawed conclusions. Ensuring that synthetic data accurately represents diverse scenarios without incorporating biases is, in itself a nuanced and complex task. If this balance isn’t right, models trained on this data might perform poorly in real-world applications.
– A privacy trade-off. Generating synthetic data often stems from a need to circumvent privacy issues inherent in using real-world data. However, there is an intricate balance between the usefulness of the synthetic data and the level of privacy it offers. If the synthetic data is too detached from the original, it may lose valuable insights. If it’s too closely related, it might still expose sensitive information.
– Regulatory considerations. As previously mentioned, in sectors like healthcare and finance, where data handling is heavily regulated, the use of synthetic data must still comply with existing laws and guidelines. Ensuring this compliance while maintaining the data’s utility can be a complex and time-consuming process.
What Does This Mean For Your Business?
The ability to generate data on demand in whatever quantity is required, tailored to specific needs which may not be available in the real world, and free of restrictions, bias and other complications is driving the AI development that’s bringing new opportunities and solutions in many sectors. For example, for research and healthcare, having cost-effective access to on-tap specific datasets is speeding up training for and scaling models so that they can solve problems faster and explore and make breakthroughs in new areas with new simulations and scenarios and innovative algorithms in a way they couldn’t before, due to the restrictions and limitations of real world data. The disruptive force of synthetic data is changing the whole economy and strategy of data in way that is benefitting the creation of better and more specific AI models in a variety of industries and creating new business opportunities for startups along the way.
As highlighted by Gartner’s prediction that 60 per cent of all data used in the development of AI will be synthetic rather than real by 2024, synthetic data is rapidly becoming the preferred choice in the AI world for a wide range of reasons, and its use is speeding up and helping to deliver new and beneficial possibilities for businesses and individuals.