## 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.

## Keywords

Artificial intelligence Deep neural networks Semantics Logic Basic formal ontology (BFO)## Notes

### Acknowledgements

We would like to thank Prodromos Kolyvakis, Kevin Keane, James Llinas and Kirsten Gather for helpful comments.

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