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Human-inspired Perspectives: A Survey on AI Long-term Memory

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arxiv 2411.00489 v2 pith:G7NA2K3H submitted 2024-11-01 cs.AI

Zihong He , Weizhe Lin , Hao Zheng , Fan Zhang , Matt W. Jones , Laurence Aitchison , Xuhai Xu , Miao Liu
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Per Ola Kristensson Junxiao Shen
This is my paper
classification cs.AI
keywords long-termmemorysystemscapabilitiescognitiveframeworkmappingmechanisms
verification ladder T0 review T1 audit T2 compute T3 formal
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With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant. These capabilities are crucial for enhancing the performance of AI systems across a wide range of tasks. However, there is currently no comprehensive survey that systematically investigates AI's long-term memory capabilities, formulates a theoretical framework, and inspires the development of next-generation AI long-term memory systems. This paper begins by introducing the mechanisms of human long-term memory, then explores AI long-term memory mechanisms, establishing a mapping between the two. Based on the mapping relationships identified, we extend the current cognitive architectures and propose the Cognitive Architecture of Self-Adaptive Long-term Memory (SALM). SALM provides a theoretical framework for the practice of AI long-term memory and holds potential for guiding the creation of next-generation long-term memory driven AI systems. Finally, we delve into the future directions and application prospects of AI long-term memory.

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Cited by 5 Pith papers

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