Probabilistic Foundations of Metacognition via Hybrid AI

This talk presents a new theory providing probabilistic foundations for metacognition in machine learning models, based on experiments with error detection and correction rules (EDCR). Learn about research findings from a recently-accepted paper for AAAI-MAKE 2025 that explains experimental results in metacognitive improvement for ML models. Discover how hybrid AI approaches combine symbolic methods with deep learning to enhance model performance through metacognitive capabilities. Access the preprint of this research at the provided link to understand the theoretical framework that supports these advancements in artificial intelligence.