Making AI meaningful again
This addresses foundational issues in AI for researchers and practitioners, but it is incremental as it builds on existing critiques without presenting new empirical results.
The paper tackles the problem of current AI views being flawed, especially in language processing, by proposing an alternative approach that incorporates philosophy to make AI more meaningful.
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.