Levels of AGI: 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. 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.
6549e762f72772583aefc1eb
Meredith Ringel Morris
6549e762f72772583aefc1ec
Jascha Sohl-dickstein
admin_assigned
2023-11-07T15:38:50.668Z
63d209ae8eaa4831006b3b0d
/avatars/51cc0f6dfab2233bddd73bfcd0c8aee3.svg
Jascha Sohl-Dickstein
user
jaschasd
6549e762f72772583aefc1ed
Noah Fiedel
admin_assigned
2023-11-07T15:39:00.609Z
62f82ebd870a3f98bbf9e62a
/avatars/3b03299e128abf2c8ba82db704855de7.svg
Noah Fiedel
user
nfiedel
6549e762f72772583aefc1ee
Tris Warkentin
admin_assigned
2023-11-07T15:39:37.173Z
64d51be85e5f05485ca50751
/avatars/5802fcc5a5f1e089df41232892f8f502.svg
Tris Warkentin
user
trisfromgoogle
6549e762f72772583aefc1ef
Allan Dafoe
6549e762f72772583aefc1f0
Aleksandra Faust
admin_assigned
2023-11-07T15:39:47.275Z
64a3b828225b2121f6b3beb3
https://cdn-avatars.huggingface.co/v1/production/uploads/64a3b828225b2121f6b3beb3/44AgAQENc7cJMOWcoK4g_.jpeg
Aleksandra Faust
user
sandraorion
6549e762f72772583aefc1f1
Clement Farabet
admin_assigned
2023-11-07T15:39:55.000Z
638a689ef560ea995b57aad4
https://cdn-avatars.huggingface.co/v1/production/uploads/1670015109688-noauth.jpeg
Clement Farabet
user
clmnt
6549e762f72772583aefc1f2
Shane Legg
Levels of AGI: 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. 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.
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