Abstract

Large language models (LLMs) invite a revision of the concept of intelligence, or at least a re-evaluation of how we understand human intelligence in contrast to machine intelligence. One approach to solve this issue is to ground intelligence in mental representations. More concretely, the representational view of intelligence (Dretske, Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition 71, 1993; Grzankowski, Inquiry 1–27, 2024) posits that real intelligence requires two conditions: a) the existence of mental representations with semantic content, and b) mental content having some causal role in producing behavior. This paper argues that the representational view is not well-founded. For this, I challenge the necessity of semantic representations as a requirement for intelligence, questioning whether such representations are truly essential or merely a reflection of anthropocentric thinking. As a second step, I argue that even if such representations take place, there are no reasons to think that their semantic contents causally explain behavior. In consequence, in the last section, I consider some observable and measurable criteria for attributing intelligence, drawing on scientific approaches from the cognitive sciences. I conclude that the emphasis on mental representation and the causal efficacy of content not only transfers the empirical problems of mental representation to intelligence but also imposes metaphysically problematic requirements for intelligence attribution, even to humans. Therefore, a more operational and deflationary view of intelligence is preferable for describing both humans and machines.