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Natural Language Translation at the Intersection of AI and HCI

By Spence Green, Jeffrey Heer, Christopher D. Manning

Communications of the ACM, Vol. 58 No. 9, Pages 46-53

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The fields of artificial intelligence (AI) and human-computer interaction (HCI) are influencing each other like never before. Widely used systems such as Google Translate, Facebook Graph Search, and RelateIQ hide the complexity of large-scale AI systems behind intuitive interfaces. But relations were not always so auspicious. The two fields emerged at different points in the history of computer science, with different influences, ambitions, and attendant biases. AI aimed to construct a rival, and perhaps a successor, to the human intellect. Early AI researchers such as McCarthy, Minsky, and Shannon were mathematicians by training, so theorem-proving and formal models were attractive research directions. In contrast, HCI focused more on empirical approaches to usability and human factors, both of which generally aim to make machines more useful to humans. Many attendees at the first CHI conference in 1983 were psychologists and engineers. Presented papers had titles such as "Design Principles for Human-Computer Interfaces" and "Psychological Issues in the Use of Icons in Command Menus," hardly appealing fare for mainstream AI researchers.

Since the 1960s, HCI has often been ascendant when setbacks in AI occurred, with successes and failures in the two fields redirecting mindshare and research funding.14 Although early figures such as Allen Newell and Herbert Simon made fundamental contributions to both fields, the competition and relative lack of dialogue between AI and HCI are curious. Both fields are broadly concerned with the connection between machines and intelligent human agents. What has changed recently is the deployment and adoption of user-facing AI systems. These systems need interfaces, leading to natural meeting points between the two fields.


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