Synthetic data can help AI Implementation for business
Today, many industries are striving to integrate AI into their systems and technologies. Business competitiveness increasingly hinges on the ability to predict consumer demand and provide personalized services. A well-trained AI model can achieve this with speed and accuracy, so making AI investment is becoming a key focus for business advancement.
However, implementing AI is not straightforward. It demands not just skilled AI developers but also a significant amount of data to train customized models. The performance of an AI model is largely determined by the data it is trained on, emphasizing the importance of both the quantity and quality of data. Thus, data collection becomes a critical but time-consuming and effort-intensive task.
For companies facing with this challenge, synthetic data offers a solution. Synthetic data, created from a set of real data, can statistically mirror actual datasets. This makes it a viable resource for research or training AI models. There are several methods for generating synthetic data with ease, including:
- Generation through Sampling and Interpolation
- Generation with Variational Auto-encoders
- Generation using Generative Adversarial Networks (GANs)
- Generation with foundation model (ChatGPT, StableDiffusion, ETC)
Despite the ease of generation, these methods pose challenges in terms of data quality and privacy. Universal data generation techniques as above do not guarantee the quality and diversity needed for the practical utility of the data. Additionally, if the original data is subject to copyright, the derived synthetic data may also face legal challenges.
In our upcoming post, we will discuss how to evaluate the synthetic data, and how to create impeccable synthetic data that respects both privacy and utility, from both theoretical and practical perspectives.
Do you have interest in Cubic’s synthetic data? Click the below link please!
https://cubig.ai/Blogs/wp-admin/post.php?post=32&action=edit