Introduce Your Data To AI

Emerald uses Bayesian Meta-Learning which, has seen rapid improvement recently[1-2] and we are ready to move on from benchmarks[3-4] to real use cases with the launch of a closed beta by the end of this year. Our methods are also extremely computationally efficient which, allows for rapid feedback loops. One advantage of the accurate uncertainty estimates we have is that we can provide that information in our data visualizations on the produced labels in a matter of minutes, from labeling, to training, to whole dataset application. When attribution is used for dataset balancing, the automated attribute labels do not need to achieve extremely high accuracy or certainty before the results are acceptable. This is because deep-learning models are generally robust to slightly imbalanced data. Finally, by utilizing external data sources, we can search and provide augmented data to balance and improve datasets based on underrepresented data attributes.