Without an agreed-upon definition of intelligence, asking "is this system
intelligent?"" is an untestable question. This lack of consensus hinders
research, and public perception, on Artificial Intelligence (AI), particularly
since the rise of generative- and large-language models. Most work on precisely
capturing what we mean by "intelligence" has come from the fields of
philosophy, psychology, and cognitive science. Because these perspectives are
intrinsically linked to intelligence as it is demonstrated by natural
creatures, we argue such fields cannot, and will not, provide a sufficiently
rigorous definition that can be applied to artificial means. Thus, we present
an argument for a purely functional, black-box definition of intelligence,
distinct from how that intelligence is actually achieved; focusing on the
"what", rather than the "how". To achieve this, we first distinguish other
related concepts (sentience, sensation, agency, etc.) from the notion of
intelligence, particularly identifying how these concepts pertain to artificial
intelligent systems. As a result, we achieve a formal definition of
intelligence that is conceptually testable from only external observation, that
suggests intelligence is a continuous variable. We conclude by identifying
challenges that still remain towards quantifiable measurement. This work
provides a useful perspective for both the development of AI, and for public
perception of the capabilities and risks of AI.

通过提供关于智能的纯功能性黑盒定义以及对相关概念的区分，本研究在人工智能领域提供了一个可观察的、概念上可测试的连续性定义，并指出了在可量化测量方面所面临的挑战。