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

• URL: http://arxiv.org/abs/2411.00489v1
• Date: Fri, 01 Nov 2024 10:04:01 GMT
• Title: Human-inspired Perspectives: A Survey on AI Long-term Memory
• Authors: Zihong He, Weizhe Lin, Hao Zheng, Fan Zhang, Matt Jones, Laurence Aitchison, Xuhai Xu, Miao Liu, Per Ola Kristensson, Junxiao Shen,
• Abstract summary: This paper introduces the mechanisms of human long-term memory, then explores AI long-term memory mechanisms. We 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.
• Score: 46.33545299110207
• License:
• Abstract: 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 systematically 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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