Why machines do not understand: A response to Søgaard
This addresses a philosophical debate about AI capabilities, but it is incremental as it responds to existing arguments without new empirical results.
The paper critiques Søgaard's claim that machines can understand language by arguing that he overlooks the distinction between human language use and inert symbol storage, concluding that machines lack genuine understanding.
Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, Søgaard 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.