The Prison of Syntax? The Chinese Room, Contemporary AI, and the Limits of What We Can Rule Out

Abstract

Abstract This essay examines the scope of John Searle's Chinese room argument in light of trained neural networks and large language models. Its central claim is that the argument alone does not justify ruling out artificial understanding in general, and that assessments of phenomenal consciousness require further argument. Searle aims to show that program instantiation is not, by itself, sufficient for understanding, while leaving open the possibility that other physical systems could have intentionality if they possess the relevant causal powers. The essay therefore examines which additional premises are needed to move from this insufficiency to a negative judgment about a particular system. Studies of internal representations and proposals concerning referential grounding specify what can be investigated, but their significance for understanding requires a separate justification. Likewise, evidence for a mechanism's causal role must be distinguished from arguments for its significance for phenomenal experience. Theory-dependent indicators can contribute to warranted assessments without thereby being universal necessary conditions for consciousness. The need to justify both attributions and exclusions does not imply that their evidential standing is equivalent. Keywords: Chinese room, large language models, vector grounding, intentionality, artificial consciousness, John Searle.