Interacting with an inferred world: the challenge of machine learning for humane computer interaction

A. Blackwell
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引用次数: 27

Abstract

Classic theories of user interaction have been framed in relation to symbolic models of planning and problem solving, responding in part to the cognitive theories associated with AI research. However, the behavior of modern machine-learning systems is determined by statistical models of the world rather than explicit symbolic descriptions. Users increasingly interact with the world and with others in ways that are mediated by such models. This paper explores the way in which this new generation of technology raises fresh challenges for the critical evaluation of interactive systems. It closes with some proposed measures for the design of inference-based systems that are more open to humane design and use.
与推断世界交互:机器学习对人性化计算机交互的挑战
用户交互的经典理论已经与规划和解决问题的符号模型相关联,部分回应了与人工智能研究相关的认知理论。然而,现代机器学习系统的行为是由世界的统计模型决定的,而不是明确的符号描述。用户越来越多地以这种模型为中介的方式与世界和他人进行交互。本文探讨了这种新一代技术如何为交互式系统的关键评估提出新的挑战。最后提出了一些建议措施,以设计更人性化的设计和使用基于推理的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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