On a Functional Definition of Intelligence
This work addresses a foundational problem for AI researchers and the public by aiming to clarify what intelligence means for artificial systems, though it is incremental as it builds on existing philosophical and psychological perspectives.
The paper tackles the lack of a testable definition of intelligence in AI by proposing a purely functional, black-box definition that focuses on external observation rather than internal mechanisms, resulting in a formal definition that treats intelligence as a continuous variable.
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.