Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence (2401.01001v1)

Abstract: This paper offers a novel conceptual framework comprising four essential cognitive mechanisms that operate concurrently and collaboratively to enable metalearning (knowledge and regulation of learning) strategy implementation in young children. A roadmap incorporating the core mechanisms and the associated strategies is presented as an explanation of the developing brain's remarkable cross-context learning competence. The tetrad of fundamental complementary processes is chosen to collectively represent the bare-bones metalearning architecture that can be extended to AI systems emulating brain-like learning and problem-solving skills. Utilizing the metalearning-enabled young mind as a model for brain-inspired computing, this work further discusses important implications for morally grounded AI.