The Human and the Mechanical: logos, truthfulness, and ChatGPT

TL;DR

The paper questions whether mechanical minds can host genuine logos and veridical judgment, arguing that true understanding requires grounding in the world and integration of endogenous beliefs with exogenous evidence. It develops a veridicality framework that splits truth assessment into world-referential evidence and subjective biases, and argues that ChatGPT lacks both referential grounding and internal priors. Through discussion of Turing, Searle, Chomsky, and Cantwell Smith, it contrasts strong AI with human-like cognition and concludes that AI cannot form beliefs or deliberate moral judgments. Consequently, ChatGPT cannot possess a mind in the Aristotelian sense, cannot deceive intentionally, and raises ethical concerns about assigning mind-like status to AI.

Abstract

The paper addresses the question of whether it is appropriate to talk about `mechanical minds' at all, and whether ChatGPT models can indeed be thought of as realizations of that. Our paper adds a semantic argument to the current debate. The act of human assertion requires the formation of a veridicality judgment. Modification of assertions with modals (John must be at home) and the use of subjective elements (John is obviously at home) indicate that the speaker is manipulating her judgments and, in a cooperative context, intends her epistemic state to be transparent to the addressee. Veridicality judgments are formed on the basis of two components: (i) evidence that relates to reality (exogenous evidence) and (ii) endogenous evidence, such as preferences and private beliefs. `Mechanical minds' lack these two components: (i) they do not relate to reality and (ii) do not have endogenous evidence. Therefore they lack the ability to form a belief about the world and a veridicality judgments altogether. They can only mimic that judgment, but the output is not ground in the very foundations for it.