Core Insights - The article discusses a new comprehensive and testable definition of Artificial General Intelligence (AGI) proposed by leading scholars and industry leaders, emphasizing that AGI should match or exceed the cognitive capabilities of well-educated adults [1][3][47]. Definition and Framework - The proposed framework defines AGI as an AI that exhibits cognitive multi-functionality and proficiency comparable to that of well-educated adults, moving beyond narrow specialization [3][4]. - The framework is based on the Cattell-Horn-Carroll (CHC) theory, which categorizes human intelligence into various broad and narrow abilities, providing a structured approach to assess AI systems [6][48]. Measurement of AGI - The framework introduces a standardized "General Intelligence Index" (AGI score) ranging from 0% to 100%, where 100% indicates full AGI capability [7]. - It identifies ten core cognitive components derived from the CHC theory, each weighted equally to emphasize the breadth of cognitive abilities [9][48]. Performance of Current Models - The article evaluates the performance of GPT-4 and GPT-5 across these cognitive components, revealing that both models scored below 10% in most areas, indicating a significant gap from true AGI [12][50]. - For instance, GPT-4 achieved an overall AGI score of 27%, while GPT-5 scored 58%, highlighting rapid progress yet substantial distance from achieving AGI [50]. Cognitive Structure and Limitations - The cognitive structure of contemporary AI systems is described as "jagged," showing high proficiency in certain areas like general knowledge and mathematics, but severe deficiencies in foundational cognitive mechanisms, particularly in long-term memory storage [25][49]. - The lack of continuous learning capabilities leads to a "memory loss" effect, limiting the practical utility of AI systems [25]. Capability Distortions - The uneven distribution of AI capabilities can lead to "capability contortions," where strengths in certain areas mask weaknesses in others, creating a false impression of general intelligence [27][28]. - For example, reliance on extensive context windows to compensate for poor long-term memory storage is inefficient and not scalable for tasks requiring prolonged context accumulation [29]. Interdependence of Cognitive Abilities - The article emphasizes the interdependence of cognitive abilities, noting that complex tasks often require the integration of multiple cognitive domains [37][38]. - This interconnectedness suggests that assessments of AGI should consider the holistic nature of intelligence rather than isolated capabilities [38]. Challenges to Achieving AGI - The article outlines significant challenges to achieving AGI, including the need for reliable long-term memory systems and the ability to learn dynamically from experiences [42][51]. - It stresses that current AI systems are far from achieving the cognitive breadth and depth required for AGI, with many foundational issues still unresolved [50][52].