论文标题
人类在循环的机器学习：宏观微透明的观点
Human-in-the-loop Machine Learning: A Macro-Micro Perspective
论文作者
论文摘要
尽管人工智能和机器学习的技术进步使许多有前途的智能系统都能通过机器智能无法完全完成许多计算任务。由人类和机器智力的互补性激励，新兴趋势是让人类参与机器学习和决策的循环。在本文中，我们提供了对人类在循环机器学习的宏观评论。我们首先描述了主要的机器学习挑战，可以通过循环中的人类干预来解决。然后，我们仔细研究了将人类引入机器学习生命周期的每个步骤的最新研究和发现。最后，我们分析当前的研究差距并指出未来的研究方向。
Though technical advance of artificial intelligence and machine learning has enabled many promising intelligent systems, many computing tasks are still not able to be fully accomplished by machine intelligence. Motivated by the complementary nature of human and machine intelligence, an emerging trend is to involve humans in the loop of machine learning and decision-making. In this paper, we provide a macro-micro review of human-in-the-loop machine learning. We first describe major machine learning challenges which can be addressed by human intervention in the loop. Then we examine closely the latest research and findings of introducing humans into each step of the lifecycle of machine learning. Finally, we analyze current research gaps and point out future research directions.
