A Definition of AGI
Authors: name: Dan Hendrycks, name: Dawn Song, name: Christian Szegedy, name: Honglak Lee, name: Yarin Gal, name: Erik Brynjolfsson, name: Sharon Li, name: Andy Zou, name: Lionel Levine, name: Bo Han, name: Jie Fu, name: Ziwei Liu, name: Jinwoo Shin, name: Kimin Lee, name: Mantas Mazeika, name: Long Phan, name: George Ingebretsen, name: Adam Khoja, name: Cihang Xie, name: Olawale Salaudeen, name: Matthias Hein, name: Kevin Zhao, name: Alexander Pan, name: David Duvenaud, name: Bo Li, name: Steve Omohundro, name: Gabriel Alfour, name: Max Tegmark, name: Kevin McGrew, name: Gary Marcus, name: Jaan Tallinn, name: Eric Schmidt, name: Yoshua Bengio

The lack of a concrete definition for Artificial General Intelligence (AGI)
obscures the gap between today's specialized AI and human-level cognition. This
paper introduces a quantifiable framework to address this, defining AGI as
matching the cognitive versatility and proficiency of a well-educated adult. To
operationalize this, we ground our methodology in Cattell-Horn-Carroll theory,
the most empirically validated model of human cognition. The framework dissects
general intelligence into ten core cognitive domains-including reasoning,
memory, and perception-and adapts established human psychometric batteries to
evaluate AI systems. Application of this framework reveals a highly "jagged"
cognitive profile in contemporary models. While proficient in
knowledge-intensive domains, current AI systems have critical deficits in
foundational cognitive machinery, particularly long-term memory storage. The
resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 58%) concretely quantify
both rapid progress and the substantial gap remaining before AGI.