In the evolving landscape of data management, synthetic data emerges as a key player, reshaping the paradigms of AI, enhancing privacy protocols, and redefining data analysis. Our white paper delves into this realm, uncovering the vast potential and diverse applications of synthetic data with both traditional techniques and generative AI.
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Synthetic data is an artificial dataset designed to replicate the statistical properties of real-world data, crucial for training and validating AI models in a secure and privacy-compliant manner.
The use of synthetic data offers several advantages across various sectors and applications. This type of data allows for the creation of datasets more quickly and efficiently compared to the challenging task of collecting and cleaning real-world data. Moreover, synthetic data enables the generation of realistic data in situations where real data is scarce or hard to acquire. This becomes crucial for expanding datasets and generating large volumes of data required for testing or training AI models while upholding stringent privacy and security standards. Lastly, the AI's capability to produce a diverse array of data is essential for obtaining more comprehensive datasets, thus enhancing the accuracy and resilience of AI models.
In this white paper, we explore the potential of synthetic data in three main areas.
This approach involves replacing sensitive personally identifiable information (PII) in tabular data with synthetic data. Techniques like statistical and deep learning methods are used to preserve the statistical properties of the data, ensuring that crucial information like name, age, and gender is anonymised without compromising the validity of the model.
Here, synthetic data is generated to augment original datasets, particularly useful in scenarios with imbalanced features. For instance, in fraud detection, the scarcity of fraudulent activities compared to non-fraudulent ones can lead to biased models. Synthetic data helps balance the dataset, enhancing the performance and accuracy of machine learning algorithms by mitigating bias.
In design and manufacturing, AI-powered visual inspections for defect detection require extensive training data, including images and 3D objects. Synthetic data aids in creating artificial defects for training AI models and, with generative AI, generates synthetic styles and patterns for product design, speeding up the design process and offering creative inspirations.
Reply is at the forefront of embracing the potential of synthetic data. Our approach includes an in-depth exploration and practical application of this technology, assessing its impact and capabilities through a series of tests and experiments. Delve deeper into the topic by downloading our white paper.