Short Review
Quantifying Artificial General Intelligence: A CHC-Based Framework
This article introduces a novel, quantifiable framework to define and evaluate Artificial General Intelligence (AGI), addressing its ambiguity and the gap between specialized AI and human cognition. Grounded in Cattell-Horn-Carroll (CHC) theory, it operationalizes AGI by matching a well-educated adult's cognitive versatility. The framework dissects general intelligence into ten core cognitive domains, utilizing adapted human psychometric batteries. Application reveals a "jagged" cognitive profile in contemporary AI models, proficient in knowledge-intensive areas but with critical deficits in foundational cognition, particularly long-term memory storage. Quantified AGI scores highlight rapid progress and the substantial remaining gap.
Critical Evaluation of the AGI Assessment Methodology
Strengths of the AGI Evaluation Framework
This research significantly advances AGI evaluation, offering a quantifiable and systematic framework beyond vague definitions. Its foundation in Cattell-Horn-Carroll (CHC) theory provides strong empirical grounding, ensuring a human-centric, holistic diagnostic assessment. The detailed breakdown into ten core cognitive domains, with adapted human psychometric batteries, allows granular, objective analysis of AI's cognitive profile, highlighting strengths and weaknesses, and providing a clear roadmap for future AI development and a standardized benchmark.
Weaknesses and Methodological Considerations
While robust, the framework faces conceptual limitations. Defining AGI by a "well-educated adult" could introduce cultural biases. AI systems often employ "contortions" like extensive Working Memory (WM) or Retrieval-Augmented Generation (RAG) to mask foundational deficits, especially in long-term memory, complicating intrinsic ability assessment. Challenges in evaluating kinesthetic abilities and the equal weighting of cognitive domains are also noted.
Implications for AGI Development
The findings profoundly impact AGI research and development. By revealing the "jagged" cognitive profiles of advanced models, the study underscores the critical need to address foundational deficits, especially in long-term memory storage and retrieval. Quantified AGI scores provide tangible metrics for tracking progress and identifying bottlenecks, guiding researchers toward developing more versatile and proficient AI systems. This framework serves as an invaluable tool for benchmarking, fostering targeted innovation, and accelerating the journey towards human-level general intelligence.
Conclusion: Paving the Way for Measurable AGI Progress
This article delivers a crucial contribution to artificial intelligence by establishing a rigorous, quantifiable framework for Artificial General Intelligence (AGI) evaluation. Grounding its methodology in human cognitive theory and providing concrete metrics, it clarifies current AI capabilities and illuminates specific cognitive gaps. This work is indispensable for guiding future research, offering a standardized approach to measure progress, and ultimately accelerating the development of truly general and versatile AI systems. It sets a new standard for understanding and achieving human-level cognition in machines.
