Levels of AGI for Operationalizing Progress on the Path to AGI
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.
6549e762f72772583aefc1eb
Meredith Ringel Morris
6549e762f72772583aefc1ec
Jascha Sohl-dickstein
admin_assigned
2023-11-07T15:38:50.668Z
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Jascha Sohl-Dickstein
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jaschasd
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Noah Fiedel
admin_assigned
2023-11-07T15:39:00.609Z
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Noah Fiedel
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nfiedel
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Tris Warkentin
admin_assigned
2023-11-07T15:39:37.173Z
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Tris Warkentin
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trisfromgoogle
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Allan Dafoe
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Aleksandra Faust
admin_assigned
2023-11-07T15:39:47.275Z
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Aleksandra Faust
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sandraorion
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Clement Farabet
admin_assigned
2023-11-07T15:39:55.000Z
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Clement Farabet
user
clmnt
6549e762f72772583aefc1f2
Shane Legg
Levels of AGI for Operationalizing Progress on the Path to AGI
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.
The framework proposes a classification system for AGI capabilities and behavior using levels of performance and generality, with principles to assess risks and measure progress.