Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage
Hiroaki Yamanaka, Daisuke Miyashita, Takashi Toi, Asuka Maki, Taiga Ikeda, Jun Deguchi

TL;DR
This paper explores alternative memory storage approaches for AI, emphasizing retaining raw experiences for flexible, on-demand retrieval to enhance long-term memory and avoid information loss, supported by preliminary experiments.
Contribution
It introduces the 'store then on-demand extract' approach and discusses methods to improve experience sharing and insight discovery in AI memory systems.
Findings
Preliminary experiments support the effectiveness of the proposed approaches.
Retaining raw experiences can prevent information loss compared to extraction-based methods.
Sharing stored experiences improves collection efficiency.
Abstract
Driven by our mission of "uplifting the world with memory," this paper explores the design concept of "memory" that is essential for achieving artificial superintelligence (ASI). Rather than proposing novel methods, we focus on several alternative approaches whose potential benefits are widely imaginable, yet have remained largely unexplored. The currently dominant paradigm, which can be termed "extract then store," involves extracting information judged to be useful from experiences and saving only the extracted content. However, this approach inherently risks the loss of information, as some valuable knowledge particularly for different tasks may be discarded in the extraction process. In contrast, we emphasize the "store then on-demand extract" approach, which seeks to retain raw experiences and flexibly apply them to various tasks as needed, thus avoiding such information loss. In…
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Taxonomy
TopicsPersonal Information Management and User Behavior · Paranormal Experiences and Beliefs · Innovative Human-Technology Interaction
