# Why machines do not understand: A response to Sogaard

arXiv: https://arxiv.org/abs/2307.04766 · HTML: https://mlsanity.com/paper/2307.04766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Making AI meaningful again](https://mlsanity.com/paper/1901.02918/) (2019)

## Abstract (arXiv)

Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, Sogaard 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 parencitesogaard: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.