从人类记忆出发，构建AI长期记忆的理论框架。
Human-inspired Perspectives: A Survey on AI Long-term Memory
- 对比人脑与AI记忆机制，建立映射关系。
- 提出自适应长期记忆认知架构（SALM）。
- 适合研究下一代智能系统长期记忆的学者。
随着人工智能系统的快速发展，其在长期内存储、检索和利用信息的能力——即长期记忆——变得愈发重要。这些能力对提升人工智能在各类任务中的表现至关重要。然而，目前尚无全面综述系统地研究人工智能长期记忆能力、构建理论框架并启发下一代长期记忆系统的发展。本文首先介绍人类长期记忆的机制，随后探讨人工智能长期记忆机制，并建立两者之间的映射关系。基于识别出的映射关系，我们拓展了现有认知架构，提出了自适应长期记忆认知架构（SALM）。SALM为人工智能长期记忆的实践提供了理论框架，有望指导下一代以长期记忆驱动的人工智能系统构建。最后，本文深入探讨了人工智能长期记忆的未来发展方向与应用前景。
原文摘要 · Abstract (English)
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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