2023-11-14 12:07:48
D’iBy 2024, Gartner analysts predict that 60% of the data used to train artificial intelligence systems worldwide will be synthetic, compared to 1% in 2021. This massive shift to synthetic data marks a significant shift towards human-centric AI. data.
According to the Gartner Trends in Data Science and Machine Learning (DSML) report, this more data-driven approach helps create better AI systems.
Using generative AI to create synthetic data is a rapidly growing trend. This approach helps generate data that convincingly mimics reality, while providing flexibility and ease of obtaining that real data cannot always provide.
Synthetic data can be used to supplement or replace real data when training machine learning models. They help solve certain data challenges, including:
Accessibility : Actual data can be difficult to obtain, whether it is sensitive, rare, expensive to collect, or unavailable. Generative AI solutions make it possible to create synthetic data quickly and easily, filling this gap.
Volume : Machine learning models often require massive datasets to be trained effectively. Generating synthetic data helps increase the amount of data available for training.
Confidentiality : In many fields, such as healthcare, finance and education, data privacy is a major concern. Synthetic data preserves privacy by avoiding the disclosure of sensitive information.
Security : The generation of controlled synthetic data helps minimize the risks associated with the manipulation or disclosure of sensitive data. This data is less vulnerable to security threats.
Complexity : Some problems or phenomena may be difficult to model with real data due to their complexity. Synthetic data can be adjusted to simulate complex scenarios in a controlled manner.
Bias reduction : They also help reduce bias because they are artificially created to replicate the statistical characteristics and patterns of real data, while avoiding discriminatory or unrepresentative elements that may exist in real-world data.
Scope : Synthetic data can cover a wide range of situations and contexts, making it versatile for various applications.
Application areas
Due to its ability to simulate real data in a controlled manner, synthetic data indeed has a wide range of applications in various fields besides training ML models, including:
Software testing and validation : Synthetic data is used to test and validate software and systems, simulating a variety of scenarios and identifying potential vulnerabilities. This helps improve the quality of software and applications.
Scientific Research : Researchers frequently use synthetic data to study complex phenomena. For example, synthetic data is used in climate modeling, genomics research, and other areas where collecting real data may be difficult or expensive.
Process optimization : In the field of supply chain management and logistics, synthetic data is used to optimize processes, improve demand forecasting and reduce operational costs.
Finance and risk management : Synthetic data is useful for financial modeling, fraud detection and risk management. They allow financial institutions to test their systems without using sensitive data.
Education and formation : Synthetic data is used in education to create simulations and virtual learning environments. They allow learners to train in realistic conditions without risking real data.
Medicine and healthcare : Synthetic data is used to create virtual patient models, which facilitates the training of healthcare professionals, disease research and personalization of treatments.
Data forecasting and analysis : Synthetic data is used to simulate future scenarios and perform predictive data analytics in various fields, from meteorology to urban planning.
IT security : Synthetic data is used to test the security of computer systems by simulating attacks and potential vulnerabilities.
Synthetic data represents a major advancement in the field of data management: it offers an efficient solution for working with sensitive information while preserving privacy and improving search and analysis capabilities.
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