The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913626564067328 |
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| author | Kim, HyunJin Yi, Xiaoyuan Yao, Jing Lian, Jianxun Huang, Muhua Duan, Shitong Bak, JinYeong Xie, Xing |
| author_facet | Kim, HyunJin Yi, Xiaoyuan Yao, Jing Lian, Jianxun Huang, Muhua Duan, Shitong Bak, JinYeong Xie, Xing |
| contents | The emergence of large language models (LLMs) has sparked the possibility of about Artificial Superintelligence (ASI), a hypothetical AI system surpassing human intelligence. However, existing alignment paradigms struggle to guide such advanced AI systems. Superalignment, the alignment of AI systems with human values and safety requirements at superhuman levels of capability aims to addresses two primary goals -- scalability in supervision to provide high-quality guidance signals and robust governance to ensure alignment with human values. In this survey, we examine scalable oversight methods and potential solutions for superalignment. Specifically, we explore the concept of ASI, the challenges it poses, and the limitations of current alignment paradigms in addressing the superalignment problem. Then we review scalable oversight methods for superalignment. Finally, we discuss the key challenges and propose pathways for the safe and continual improvement of ASI systems. By comprehensively reviewing the current literature, our goal is provide a systematical introduction of existing methods, analyze their strengths and limitations, and discuss potential future directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16468 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment Kim, HyunJin Yi, Xiaoyuan Yao, Jing Lian, Jianxun Huang, Muhua Duan, Shitong Bak, JinYeong Xie, Xing Machine Learning The emergence of large language models (LLMs) has sparked the possibility of about Artificial Superintelligence (ASI), a hypothetical AI system surpassing human intelligence. However, existing alignment paradigms struggle to guide such advanced AI systems. Superalignment, the alignment of AI systems with human values and safety requirements at superhuman levels of capability aims to addresses two primary goals -- scalability in supervision to provide high-quality guidance signals and robust governance to ensure alignment with human values. In this survey, we examine scalable oversight methods and potential solutions for superalignment. Specifically, we explore the concept of ASI, the challenges it poses, and the limitations of current alignment paradigms in addressing the superalignment problem. Then we review scalable oversight methods for superalignment. Finally, we discuss the key challenges and propose pathways for the safe and continual improvement of ASI systems. By comprehensively reviewing the current literature, our goal is provide a systematical introduction of existing methods, analyze their strengths and limitations, and discuss potential future directions. |
| title | The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.16468 |