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Conceptual framework reveals how comparative cognition informs language acquisition across diverse systems, highlighting pathways toward more efficient artificial intelligence algorithms.
Recent advances in artificial intelligence have produced large language models that are proficient at processing and generating language. Lessons learned from comparative cognition will be pivotal to understanding the conditions under which different cognitive systems (both natural and artificial) might discover complex linguistic structures. Such lessons will fuel the development of more efficient learning algorithms. More important, they will help to advance a new science of intelligence that takes into account human, animal, and artificial systems.
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Juan M. Toro (2024) studied this question.
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