Machine learning engines typically require approximately 1 million data points per training session, with companies investing around $10 million annually on data acquisition. This presents significant challenges, for example in the self-driving vehicle industry. For instance, obtaining accurate images of road signs or animals crossing is difficult, leading to missed targets. Furthermore, there’s a 20-30% disparity in quality between simulated and real data.
AI models need exposure to both high-quality and flawed data for comprehensive training. Adding to the complexity, different countries use various types of cameras and have unique road signs, requiring specialized training data for each region.
Founded in January 2022, GenGenAI is rapidly emerging as a pioneer in AI synthetic data solutions. The company’s innovative approach to generating high-quality synthetic data is transforming the landscape of machine learning model training, addressing key limitations and ethical concerns associated with real-world data.
At the core of GenGenAI’s offerings is its domain-specific generative AI technology, which has produced two key products. First, GenGen Data generates customized synthetic datasets tailored to specific customer requirements. Second, GenGen Vision provides vision AI technology as an SDK or API, ideal for developers integrating advanced visual recognition capabilities.
The company is also developing GenGen Studio, a platform that will enable customers to create their own synthetic data, potentially democratizing AI training.
GenGenAI recently secured KRW 4.5 billion (approximately $3.8 million) in pre-Series A funding. This investment will fuel expansion into sectors with high demand for diverse AI learning data, including mobility, defense, and healthcare.
GenGenAI’s focus on synthetic data addresses several critical challenges in AI development, such as privacy concerns, limited availability of real-world data, and bias in existing datasets. By providing synthetic data solutions, GenGenAI aims to accelerate AI development across various industries, making AI more accessible, efficient, and ethical.
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