2023 LevelsofAGIOperationalizingProg

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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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2023 LevelsofAGIOperationalizingProg	Shane Legg Meredith Ringel Morris Allan Dafoe Noah Fiedel Jascha Sohl-dickstein Tris Warkentin Aleksandra Faust Clement Farabet			Levels of AGI: Operationalizing Progress on the Path to AGI				10.48550/arXiv.2311.02462		2023