التفاصيل البيبلوغرافية
العنوان: |
Human-inspired Perspectives: A Survey on AI Long-term Memory |
المؤلفون: |
He, Zihong, Lin, Weizhe, Zheng, Hao, Zhang, Fan, Jones, Matt W., Aitchison, Laurence, Xu, Xuhai, Liu, Miao, Kristensson, Per Ola, Shen, Junxiao |
سنة النشر: |
2024 |
المجموعة: |
Computer Science |
مصطلحات موضوعية: |
Computer Science - Artificial Intelligence |
الوصف: |
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. |
نوع الوثيقة: |
Working Paper |
URL الوصول: |
http://arxiv.org/abs/2411.00489 |
رقم الانضمام: |
edsarx.2411.00489 |
قاعدة البيانات: |
arXiv |