Extracting actionable insights from unstructured data is a major challenge facing Data Science. In addition, without unbiased and high-quality labeled data, one cannot train AI models to perform new tasks with high accuracy and fairness. We introduce the first automated data labeling (ADL) technology that fuels AI by identifying core concepts (topics) from raw text data across several languages. The alternative is to use human-based data labeling via crowdsourcing that takes months vs. minutes using ADL. We discuss the major use cases of data labeling and ontology discovery technology for automated large-scale data cleaning; semantic search; sentiment and emotion analysis; automated feature engineering; predictive text analytics; and conversational AI with application to healthcare, finance (banking & investment management), and insurance. Our proprietary ADL technology relies on Unsupervised Learning plus recent advances in Deep Learning and Natural Language Processing (NLP). Several informative examples with data visualization will be presented.
