Summary of Converging Paradigms: the Synergy Of Symbolic and Connectionist Ai in Llm-empowered Autonomous Agents, by Haoyi Xiong et al.
Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents
by Haoyi Xiong, Zhiyuan Wang, Xuhong Li, Jiang Bian, Zeke Xie, Shahid Mumtaz, Anwer Al-Dulaimi, Laura E. Barnes
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper investigates the intersection of connectionist and symbolic artificial intelligence (AI), tracing historical debates to contemporary advancements. Recent large language models (LLMs) like ChatGPT and GPT-4 demonstrate the potential of neural networks in handling human language as a form of symbols, highlighting the convergence of paradigms. The study proposes Large Language Model-empowered Autonomous Agents (LAAs) that integrate neuro-symbolic AI principles for enhanced reasoning and decision-making capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence can work together to make better decisions. Usually, there are two kinds of AI: connectionist AI, which uses neural networks, and symbolic AI, which uses symbols and logic. New language models like ChatGPT show that these two types of AI can work together to understand human language. The researchers created a new type of AI called LAAs that combines the strengths of both approaches. This helps AI make better decisions and learn from large amounts of data. |
Keywords
» Artificial intelligence » Gpt » Large language model
