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

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