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Autores principales: Hendrycks, Dan, Song, Dawn, Szegedy, Christian, Lee, Honglak, Gal, Yarin, Brynjolfsson, Erik, Li, Sharon, Zou, Andy, Levine, Lionel, Han, Bo, Fu, Jie, Liu, Ziwei, Shin, Jinwoo, Lee, Kimin, Mazeika, Mantas, Phan, Long, Ingebretsen, George, Khoja, Adam, Xie, Cihang, Salaudeen, Olawale, Hein, Matthias, Zhao, Kevin, Pan, Alexander, Duvenaud, David, Li, Bo, Omohundro, Steve, Alfour, Gabriel, Tegmark, Max, McGrew, Kevin, Marcus, Gary, Tallinn, Jaan, Schmidt, Eric, Bengio, Yoshua
Formato: Preprint
Publicado: 2025
Materias:
Artificial Intelligence
Machine Learning
Acceso en línea:https://arxiv.org/abs/2510.18212
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  • 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 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.

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