On the universal definition of intelligence

2026-01-12

AI Summary

This study addresses the limitations of existing definitions of intelligence, which are often anthropocentric and empirically untestable, and lack a common framework for fair comparison between humans and artificial agents. Drawing on Carnap’s methodology of conceptual clarification, the paper systematically evaluates six prominent definitions and proposes the Extended Prediction Hypothesis (EPH), which defines intelligence as the capacity to accurately predict the future and derive benefit from such predictions. EPH innovatively distinguishes between spontaneous and reactive prediction and introduces the notion of “benefitability,” thereby offering a unified account of core dimensions such as creativity, learning, and planning. Theoretical analysis demonstrates that EPH surpasses existing definitions in similarity, precision, fruitfulness, and simplicity, providing a more universal and empirically tractable foundation for comparing human and artificial intelligence.

Technology Category

• Humans and AI: Emotional Intelligence
• Intelligent Robots: Embodied AI
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

This paper aims to propose a universal definition of intelligence that enables fair and consistent comparison of human and artificial intelligence (AI). With the rapid development of AI technology in recent years, how to compare and evaluate human and AI intelligence has become an important theoretical issue. However, existing definitions of intelligence are anthropocentric and unsuitable for empirical comparison, resulting in a lack of consensus in the research field. This paper first introduces four criteria for evaluating intelligence definitions based on R. Carnap's methodology of conceptual clarification: similarity to explicandum, exactness, fruitfulness, and simplicity. We then examine six representative definitions: IQ testing, complex problem-solving ability, reward optimization, environmental adaptation, learning efficiency, and predictive ability, and clarify their theoretical strengths and limitations. The results show that while definitions based on predictive ability have high explanatory power and empirical feasibility, they suffer from an inability to adequately explain the relationship between predictions and behavior/benefits. This paper proposes the Extended Predictive Hypothesis (EPH), which views intelligence as a combination of the ability to accurately predict the future and the ability to benefit from those predictions. Furthermore, by distinguishing predictive ability into spontaneous and reactive predictions and adding the concept of gainability, we present a unified framework for explaining various aspects of intelligence, such as creativity, learning, and future planning. In conclusion, this paper argues that the EPH is the most satisfactory and universal definition for comparing human and AI intelligence.

Problem

• intelligence
• artificial intelligence
• universal definition
• human-AI comparison
• predictive ability

Innovation

• Extended Predictive Hypothesis
• universal intelligence definition
• predictive ability
• gainability
• comparative AI evaluation