Explanation
Imagine needing thousands of photographs to teach an AI to recognise different types of damage to wind turbines. Gathering all that real-world data would be expensive, time-consuming and even dangerous.
Synthetic Data Generation offers a clever alternative. It involves creating artificial data that mimics the characteristics of real-world data. Think of it as a digital factory that churns out realistic images, videos, or text.
This data is generated by algorithms, not collected from real-world sources. The great thing is that this allows for complete control over the data, reducing bias and ensuring comprehensive coverage of all scenarios.
It is particularly useful when real data is scarce, expensive, or raises privacy concerns. It allows us to train AI models effectively, even when access to real data is limited.
Examples
Consumer Example
Consider a fitness app that uses AI to analyse your running form. To train the AI, the developers need data showing various running styles and potential injuries. Synthetic data can generate realistic simulations of people running, with different body types, gaits, and potential problems, allowing the AI to learn without needing to collect data from real runners.
Business Example
Imagine an insurance company that wants to use AI to automatically assess car damage from photos. Gathering enough real-world accident photos can be difficult and slow. Synthetic data generation can create a massive library of realistic car accident images, showing various types of damage, angles, and lighting conditions. This allows the AI to be trained quickly and effectively, improving the accuracy of claims processing.