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As artificial intelligence technologies continue to evolve and become more sophisticated, the need for data grows ever greater. Generally speaking, engineers use big piles of actual data for machine learning.
Unfortunately, acquiring such a database could cause severe privacy issues, as well as high costs and other obstacles. This challenge can be solved with an innovative solution, which will dramatically change the game for technology teams.
But what is synthetic data? In essence, it is the data generated using AI models, not obtained from real-life events or subjects. It should be noted that artificial data differs fundamentally from anonymized records. Instead of concealing or masking original information, advanced generative AI models analyze their behavior patterns and features.
Once they are discovered, the system generates completely new artificial records. In other words, artificial data looks just like real-life data but does not contain a scrap of personal information.
All artificially generated datasets aren’t made equal. Depending on the demands of the project, the developer uses several kinds, namely:
Produced through the training of a machine learning model that aims to copy complex statistical dependencies and connections from real-world data.
The product of generating mock data based on simple, pre-programmed rules, randomness, or templates, but without using any real data.
Examples of structured versions include structured data with the use of tabulation, such as financial transactions or patient history, while unstructured data includes AI-generated photos, sound, or video.
This growing trend towards the use of artificial data presents huge benefits for enterprises working within industries where high data security is essential, such as medicine and finance.
The biggest advantage of utilizing this technology in business is the ability to solve the problem of data leakage, since no actual individual can be identified through an artificially generated entry.
Second, it grants enormous freedom to the development team, who will easily be able to supplement a data set that doesn’t have sufficient variability with artificially generated outliers.
Having knowledge about what synthetic data is makes it clear why it becomes the basis of developing safe technologies. The use of data, which serves as an ideal replacement without breaching any private information, eliminates all potential legal issues that usually prevent innovation.
Considering that technology companies face challenges in finding a balance between innovation and privacy regulations, artificial data becomes the best choice.
