Why machines do not understand: A response to S{\o}gaard

TL;DR

This paper critiques the claim that machines understand language by highlighting the fundamental differences between human language use and inert symbol sequences stored digitally.

Contribution

It clarifies the distinction between human language understanding and machine symbol processing, challenging assumptions about machine semantics and learning.

Findings

Machines process inert symbol sequences, not understanding.

Human language involves contextual and experiential understanding.

Current AI systems lack genuine semantic comprehension.

Abstract

Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, S{\o}gaard has argued in this journal for a thesis of this sort, on the basis of the idea (1) that where there is semantics there is also understanding and (2) that machines are not only capable of what he calls `inferential semantics', but even that they can (with the help of inputs from sensors) `learn' referential semantics \parencite{sogaard:2022}. We show that he goes wrong because he pays insufficient attention to the difference between language as used by humans and the sequences of inert of symbols which arise when language is stored on hard drives or in books in libraries.