AGI 进展操作化的 AGI 水平分级
Levels of AGI for Operationalizing Progress on the Path to AGI
AI总结：
该文提出一个基于性能、通用性和自主性的 AGI 水平分级框架，用于比较模型、评估风险并指导安全部署。
摘要：
我们提出一个用于分类人工通用智能（AGI）模型及其前身的能力和行为的框架。该框架引入了 AGI 性能、通用性和自主性的水平分级，提供了一种通用语言，用于比较模型、评估风险并衡量通往 AGI 路径上的进展。为开发该框架，我们分析了现有的 AGI 定义，并提炼出六项有用的 AGI 本体应满足的原则。基于这些原则，我们根据能力的深度（性能）和广度（通用性）提出了“AGI 水平分级”，并反思了当前系统如何适配这一本体。我们讨论了未来基准测试在量化 AGI 模型相对于这些水平的行为和能力方面所面临的挑战性要求。最后，我们探讨了这些 AGI 水平如何与自主性和风险等部署考量相互作用，并强调了为负责任且安全地部署高能力 AI 系统而精心选择人机交互范式的重要性。
英文摘要：
We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy, providing a common language to compare models, assess risks, and measure progress along the path to AGI. To develop our framework, we analyze existing definitions of AGI, and distill six principles that a useful ontology for AGI should satisfy. With these principles in mind, we propose "Levels of AGI" based on depth (performance) and breadth (generality) of capabilities, and reflect on how current systems fit into this ontology. We discuss the challenging requirements for future benchmarks that quantify the behavior and capabilities of AGI models against these levels. Finally, we discuss how these levels of AGI interact with deployment considerations such as autonomy and risk, and emphasize the importance of carefully selecting Human-AI Interaction paradigms for responsible and safe deployment of highly capable AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
