On the Definition of Intelligence

To engineer AGI, we should first capture the essence of intelligence in a species-agnostic form that can be evaluated, while being sufficiently general to encompass diverse paradigms of intelligent behavior, including reinforcement learning, generative models, classification, analogical reasoning, and goal-directed decision-making. We propose a general criterion based on $\mathit{\text{entity}}\mathit{\text{fidelity}}$: Intelligence is the ability, given entities exemplifying a concept, to generate entities exemplifying the same concept. We formalise this intuition as $\epsilon$-concept intelligence: it is $\epsilon$-intelligent with respect to a concept if no chosen admissible distinguisher can separate generated entities from original entities beyond tolerance $\epsilon$. We present the formal framework, outline empirical protocols, and discuss implications for evaluation, safety, and generalization.