# Making AI meaningful again

arXiv: https://arxiv.org/abs/1901.02918 · HTML: https://mlsanity.com/paper/1901.02918/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023)
- [Meaning and understanding in large language models](https://mlsanity.com/paper/2310.17407/) (2023)
- [Can AI and humans genuinely communicate?](https://mlsanity.com/paper/2402.09494/) (2024)
- [An argument for the impossibility of machine intelligence](https://mlsanity.com/paper/2111.07765/) (2021)
- [Why machines do not understand: A response to Sogaard](https://mlsanity.com/paper/2307.04766/) (2023)

## Abstract (arXiv)

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy.