Data Curation and Labeling for Training Machine Learning Models to Support Operations in Complex Domains with Small Expert Populations and Limited Data Availability

Abstract

This disclosure relates generally to the fields of data management, collection, conditioning, and curation for driving advanced data analytics, artificial intelligence (AI) and machine learning (ML), and more particularly to contextualized data collection and curation for adaptive learning and for training advanced autonomy models in multi-actor applications in dynamic high-risk environments (e.g., multi-domain socio-technical work environments (MSWEs))—from mission control to military operations, from operating rooms to racecar engineering—where experts must effectively employ technologies to drive data-informed decisions based on situational assessment. This disclosure provides enabling technology for ML-based autonomy solutions to effectively utilize current and emerging analysis and debrief tools across relevant domains, which necessitates an inclusion of formal data contextualization processes. These processes support training the next generation of experts and simultaneously generate relevant contextually labeled data for adaptive learning and training advanced ML models for event-driven autonomy operations.

CPC Classifications