Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913182840258560 |
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| author | Singh, Chaitanya |
| author_facet | Singh, Chaitanya |
| contents | 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 artificial intelligence (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01001 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence Singh, Chaitanya Neurons and Cognition Artificial Intelligence 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 artificial intelligence (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. |
| title | Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence |
| topic | Neurons and Cognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.01001 |