The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment

• URL: http://arxiv.org/abs/2412.16468v3
• Date: Wed, 25 Dec 2024 07:10:21 GMT
• Title: The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment
• Authors: HyunJin Kim, Xiaoyuan Yi, Jing Yao, Jianxun Lian, Muhua Huang, Shitong Duan, JinYeong Bak, Xing Xie,
• Abstract summary: The emergence of large language models (LLMs) has sparked the possibility of about Artificial Superintelligence (ASI)<n>Superalignment aims to address two primary goals -- scalability in supervision to provide high-quality guidance signals and robust governance to ensure alignment with human values.<n>Specifically, we explore the concept of ASI, the challenges it poses, and the limitations of current alignment paradigms in addressing the superalignment problem.
• Score: 33.27140396561271
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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