# Making AI meaningful again

## TL;DR

This paper critiques current AI approaches, especially in language processing, highlighting underlying issues and proposing an alternative perspective that incorporates philosophical insights to make AI more meaningful.

## Contribution

It identifies fundamental problems in current language-centric AI and introduces a novel approach that integrates philosophical considerations.

## Key findings

- Current AI faces serious conceptual problems.
- Deep learning successes have not addressed core issues.
- Philosophy offers valuable insights for AI development.

## Abstract

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.