Systems and methods are provided in which an artificial intelligence inference module identifies targeted information in large-scale unlabeled data, autonomously learns hierarchical representations from the data, and continually self-improves from self-labeled data points using a teacher model trained to detect known targets from combined inputs of a small hand labeled curated dataset prepared by a domain expert together with self-generated context features derived from the unlabeled dataset by unsupervised and self-supervised processes. The trained teacher model self-generates new weakly-supervised training samples that are self-refined and self-corrected, without human supervision, and then used as inputs to a noisy student model trained in a semi-supervised learning process. With each iteration, the noisy student model continually self-optimizes its learned parameters against a set of configurable validation criteria by replacing the learned parameter of the prior iteration teacher model with optimized learned parameters periodically used to update the artificial intelligence inference module.
AUTONOMOUS AND CONTINUOUSLY SELF-IMPROVING LEARNING SYSTEM