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Contemporary natural language processing (NLP) emphasizes comparing machine language performances to standards defined by static corpora of human text. However, despite some successes, current models remain weak in areas such as pragmatics. Using scholarship on neosentience as a foundation, this essay proposes an alternative view of machine language that emphasizes generativity rather than stasis and draws on historical work on computational reflection in artificial intelligence to outline an alternative architecture for conversational systems. It concludes by proposing an “android linguistics” that takes human-machine linguistic communication as its object of study.
Evan Donahue (Tue,) studied this question.
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