This paper attempts to provide a comprehensive framework grounded in the Cattell-Horn-Carroll (CHC) theory of cognitive abilities, which breaks down general intelligence into ten core, equally weighted cognitive components, such as General Knowledge (K), Mathematical Ability (M), and various forms of Memory (WM, MS, MR). The paper details specific, measurable tasks and examples—often drawn from human-centric tests like AP exams or psychometric assessments—to evaluate AI performance in each area. Furthermore, the analysis reveals that current AI systems like GPT-4, despite excelling in some narrow tasks, lack core cognitive capabilities, relying on compensatory "capability contortions" (e.g., using large context windows for lack of long-term memory) that mask their overall deficiencies compared to the human benchmark.