Levels of AGI for Operationalizing Progress on the Path to AGI
Authors: name: Meredith Ringel Morris, name: Jascha Sohl-dickstein, name: Noah Fiedel, name: Tris Warkentin, name: Allan Dafoe, name: Aleksandra Faust, name: Clement Farabet, name: Shane Legg

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.