Summary of Ai-native Memory: a Pathway From Llms Towards Agi, by Jingbo Shang et al.
AI-native Memory: A Pathway from LLMs Towards AGI
by Jingbo Shang, Zai Zheng, Jiale Wei, Xiang Ying, Felix Tao, Mindverse Team
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models have shown promise in achieving artificial general intelligence (AGI), but some startups may be overestimating their capabilities. Research suggests that existing LLMs have a significantly shorter effective context length than claimed, making it difficult to perform complex reasoning. This paper proposes a pathway to AGI through the integration of memory, where LLMs serve as core processors and store important conclusions derived from reasoning processes. The approach connects related information, simplifies inferences, and enables direct consumption by users. The authors also discuss the potential for AI-native memory to transform engagement, personalization, distribution, and social interactions in the AGI era. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial general intelligence (AGI) is a dream of many scientists. Some think that large language models can achieve this goal, but others are more skeptical. Research shows that these models are not as powerful as claimed, making it hard to do complex thinking. This paper proposes a new way to get closer to AGI by adding memory to language models. Memory will help them remember important things they’ve learned and make it easier for people to use the information. |
Keywords
» Artificial intelligence » Context length
