Amygda's AI models overcome a major challenge in the lack of large sets of well-annotated data. This presents a significant advantage in real-world applications, as it eliminates the risk of equipment failure on critical assets.
Amygda's models are built using unsupervised and semi-supervised techniques, with further development focusing on self-supervised learning. This approach has reduced reliance on well-annotated datasets, paving the way to addressing novel, diverse and less understood use cases.