title: Levels of AGI: Operationalizing Progress on the Path to AGI
summary: 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.
paper_authors._id: 6549e762f72772583aefc1eb
paper_authors.name: Meredith Ringel Morris
paper_authors._id: 6549e762f72772583aefc1ec
paper_authors.name: Jascha Sohl-dickstein
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paper_authors.fullname: Jascha Sohl-Dickstein
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paper_authors.name: Noah Fiedel
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paper_authors.fullname: Noah Fiedel
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paper_authors.user: nfiedel
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paper_authors.name: Tris Warkentin
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paper_authors.fullname: Tris Warkentin
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paper_authors.user: trisfromgoogle
paper_authors._id: 6549e762f72772583aefc1ef
paper_authors.name: Allan Dafoe
paper_authors._id: 6549e762f72772583aefc1f0
paper_authors.name: Aleksandra Faust
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-07T15:39:47.275Z
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paper_authors.fullname: Aleksandra Faust
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paper_authors.user: sandraorion
paper_authors._id: 6549e762f72772583aefc1f1
paper_authors.name: Clement Farabet
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-07T15:39:55.000Z
paper_authors._id: 638a689ef560ea995b57aad4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1670015109688-noauth.jpeg
paper_authors.fullname: Clement Farabet
paper_authors.type: user
paper_authors.user: clmnt
paper_authors._id: 6549e762f72772583aefc1f2
paper_authors.name: Shane Legg
paper_title: Levels of AGI: Operationalizing Progress on the Path to AGI
paper_summary: 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.
paper_ai_keywords: Artificial General Intelligence (AGI) Levels of AGI autonomous driving ontology capabilities mechanisms generality performance depth (performance) breadth (generality) benchmarks Human-AI Interaction paradigms responsible and safe deployment