通往通用人工智能的進展層級：在通往通用人工智能之路上的操作化進展

摘要

我們提出了一個框架，用於分類人工通用智能（AGI）模型及其前身的能力和行為。該框架引入了AGI性能、通用性和自主性的層次。我們希望這個框架能夠像自動駕駛的級別一樣，提供一種共同語言來比較模型、評估風險，並衡量通往AGI之路上的進展。為了發展我們的框架，我們分析了現有的AGI定義，並提煉出一個有用的AGI本体論應滿足的六個原則。這些原則包括專注於能力而非機制；分別評估通用性和性能；以及定義通往AGI的階段，而非專注於終點。憑藉這些原則，我們提出了基於能力的深度（性能）和廣度（通用性）的「AGI級別」，並反思當前系統如何符合這個本体論。我們討論了未來基準的挑戰性要求，以量化AGI模型的行為和能力與這些級別的對應。最後，我們討論了這些AGI級別如何與部署考量（如自主性和風險）互動，並強調慎重選擇人工智能與人類互動範式，以負責任且安全地部署高度能力的人工智能系統的重要性。

English

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.

通往通用人工智能的進展層級：在通往通用人工智能之路上的操作化進展

Levels of AGI: Operationalizing Progress on the Path to AGI

摘要

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