“Synthetic Data makes everything possible,” a pivotal element to AI model performance
Hello, I’m Evelyn, a marketing manager at AIMMO, an AI data company.
The quantity and quality of data are the most important keys to improving the performance and reliability of AI models. To effectively operate an AI system, especially in autonomous driving, precise data must be supported!😊
Data quality is directly related to AI model performance, and improving it is an essential task for the success of future autonomous driving systems. Therefore, if you develop an AI model based on high-quality data, you can minimize errors and optimize the performance of the AI system based on solid reliability and stability.
‣ Data-centric AI models are fundamental in autonomous driving. Training time can be shortened, required data storage space can be reduced, and model optimization and updates can be smoothly performed through a unified method for collecting and labeling image data.
‣ There is growing interest in creating AI models. Generative models are one way to interpret and manipulate existing data to create new instances that can be used to train AI systems. Generative models can produce specific data types and are therefore used as tools for generating synthetic data.
‣ AIMMO leverages generative models to produce synthetic data. Synthetic data can meet the insufficient data needed to run AI without real data, so in cases where collecting real data is difficult or expensive, it provides significant benefits when real data collection is difficult or costly.
‣ In addition, synthetic data can significantly improve AI model performance. By creating diverse data sets and leveraging synthetic data, you can reduce labeling costs and make your models more robust by providing a variety of scenarios to increase the stability and reliability of autonomous driving systems. By creating and simulating various cases, including specific conditions and environments, you can reduce the time and cost of collecting data in the natural environment!
Moreover, cases and driving segments can be created and simulated according to the specific conditions of the operational design domain (ODD) and various applications, including environment, geography, time, etc.
For example, consider detecting road defects! Labeling these defects can require a lot of effort and cost. However, synthetic data can generate as much data as you want and ensure accurate information.
AIMMO’s synthetic data covers a variety of scenarios and situations and significantly reduces the cost and time required to collect data in real environments. So what if you detect a road defect such as a crack or pothole? Labeling these defects would require a significant amount of work.💁
Take a closer look at various cases and detailed information about AIMMO’s synthetic data.
Written by Evelyn
Translation by Nora
