Summary of The Road to Artificial Superintelligence: a Comprehensive Survey Of Superalignment, by Hyunjin Kim et al.
The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment
by HyunJin Kim, Xiaoyuan Yi, Jing Yao, Jianxun Lian, Muhua Huang, Shitong Duan, JinYeong Bak, Xing Xie
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The emergence of large language models has sparked concerns about Artificial Superintelligence (ASI), which could surpass human intelligence. However, existing alignment paradigms struggle to guide such advanced AI systems. Superalignment aims to address two primary goals: scalability in supervision and robust governance to ensure alignment with human values. This survey examines scalable oversight methods for superalignment, exploring the concept of ASI, its challenges, and limitations of current alignment paradigms. We also review potential solutions, discuss key challenges, and propose pathways for safe and continual improvement of ASI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Superintelligent AI could surpass human intelligence, but existing alignment paradigms struggle to guide it. Superalignment aims to ensure AI aligns with human values at superhuman levels of capability. This paper looks at scalable oversight methods for superalignment. It explores the concept of ASI, its challenges, and limitations of current alignment paradigms. It also reviews potential solutions, discusses key challenges, and proposes pathways for safe and continual improvement. |
Keywords
» Artificial intelligence » Alignment
