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. It is
our hope that this framework will be useful in an analogous way to the levels
of autonomous driving, by 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. These principles include focusing on
capabilities rather than mechanisms; separately evaluating generality and
performance; and defining stages along the path toward AGI, rather than
focusing on the endpoint. 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.

我们提出了一个人工通用智能（AGI）模型及其前身的能力和行为分类框架，该框架引入了 AGI 性能、广泛性和自治程度的级别。希望这个框架类似于自动驾驶的级别，通过提供一个共同的语言来比较模型、评估风险并在 AGI 的道路上衡量进展。我们分析了现有的 AGI 定义，提炼出了一个有用的本体论所应具备的六个原则。这些原则包括关注能力而非机制；分别评估广泛性和性能；以及定义沿着 AGI 路径的阶段，而不是专注于终点。基于这些原则，我们提出了基于能力的 “AGI 级别”，并思考当前系统如何符合这个本体论。我们讨论了未来基准的挑战性要求，以便对这些级别的 AGI 模型的行为和能力进行量化。最后，我们讨论了这些 AGI 级别与自治和风险等部署考虑因素的相互作用，并强调了在负责任和安全地部署高度能力的 AI 系统时，谨慎选择人机交互范例的重要性。