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) 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. 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:
• 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.

Related papers

• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
  Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment. We introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML)
  arXiv  Detail & Related papers  (2024-06-13T16:03:25Z)
• Human-AI Safety: A Descendant of Generative AI and Control Systems Safety [6.100304850888953]
  We argue that meaningful safety assurances for advanced AI technologies require reasoning about how the feedback loop formed by AI outputs and human behavior may drive the interaction towards different outcomes. We propose a concrete technical roadmap towards next-generation human-centered AI safety.
  arXiv  Detail & Related papers  (2024-05-16T03:52:00Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• A Moral Imperative: The Need for Continual Superalignment of Large Language Models [1.0499611180329806]
  Superalignment is a theoretical framework that aspires to ensure that superintelligent AI systems act in accordance with human values and goals. This paper examines the challenges associated with achieving life-long superalignment in AI systems, particularly large language models (LLMs)
  arXiv  Detail & Related papers  (2024-03-13T05:44:50Z)
• On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models [77.86952307745763]
  Big models have achieved revolutionary breakthroughs in the field of AI, but they might also pose potential concerns. Addressing such concerns, alignment technologies were introduced to make these models conform to human preferences and values. Despite considerable advancements in the past year, various challenges lie in establishing the optimal alignment strategy.
  arXiv  Detail & Related papers  (2024-03-07T04:19:13Z)
• Incentive Compatibility for AI Alignment in Sociotechnical Systems: Positions and Prospects [11.086872298007835]
  Existing methodologies primarily focus on technical facets, often neglecting the intricate sociotechnical nature of AI systems. We posit a new problem worth exploring: Incentive Compatibility Sociotechnical Alignment Problem (ICSAP) We discuss three classical game problems for achieving IC: mechanism design, contract theory, and Bayesian persuasion, in addressing the perspectives, potentials, and challenges of solving ICSAP.
  arXiv  Detail & Related papers  (2024-02-20T10:52:57Z)
• The Alignment Problem in Context [0.05657375260432172]
  I assess whether we are on track to solve the alignment problem for large language models. I argue that existing strategies for alignment are insufficient, because large language models remain vulnerable to adversarial attacks. It follows that the alignment problem is not only unsolved for current AI systems, but may be intrinsically difficult to solve without severely undermining their capabilities.
  arXiv  Detail & Related papers  (2023-11-03T17:57:55Z)
• AI Alignment: A Comprehensive Survey [70.35693485015659]
  AI alignment aims to make AI systems behave in line with human intentions and values. We identify four principles as the key objectives of AI alignment: Robustness, Interpretability, Controllability, and Ethicality. We decompose current alignment research into two key components: forward alignment and backward alignment.
  arXiv  Detail & Related papers  (2023-10-30T15:52:15Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI. It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.