以人为中心的机器学习的4个视角

Carlos Guestrin
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引用次数: 0

摘要

在过去的十年里,机器学习(ML)在世界各地产生了巨大的影响。当我们想到机器学习解决复杂的任务时,有时会达到超人的水平,我们很容易忘记,没有人类的参与就没有机器学习。人类定义任务和指标,开发和编程算法,收集和标记数据,调试和优化系统,并且(通常)是我们正在开发的基于ml的应用程序的最终用户。在本次演讲中,我们将介绍机器学习开发过程中的4个以人为中心的观点,以及方法和系统,以使人类能够最大限度地发挥基于机器学习的应用程序的最终影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
4 Perspectives in Human-Centered Machine Learning
Machine learning (ML) has had a tremendous impact in across the world over the last decade. As we think about ML solving complex tasks, sometimes at super-human levels, it is easy to forget that there is no machine learning without humans in the loop. Humans define tasks and metrics, develop and program algorithms, collect and label data, debug and optimize systems, and are (usually) ultimately the users of the ML-based applications we are developing. In this talk, we will cover 4 human-centered perspectives in the ML development process, along with methods and systems, to empower humans to maximize the ultimate impact of their ML-based applications.
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