人工智能质量面临的知识管理挑战

Xiaozhou Li, Sergio Moreschini, Aleksandra Filatova, D. Taibi
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引用次数: 1

摘要

开发基于人工智能的系统具有独特的挑战性,因为它需要跨多个领域的知识。尽管项目团队被要求是多才多艺的,但他们的技能可能无法涵盖系统的所有需求,这将导致对软件质量的损害。因此,制定有效的团队知识管理策略,发现有价值的“未知”,优化“已知”任务分配,扩大团队知识库至关重要。此外,用数据驱动的方法支持这一过程更为有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge Management Challenges for AI Quality
Developing an AI-based system is uniquely challenging as it requires knowledge across multiple domains. Though the project team is required to be versatile, it is possible that their repertoire cannot cover all of the requirements of the system, which results in damage to the software quality. Therefore, it is critical to have an effective team knowledge management (KM) strategy to detect the valuable “unknown”, optimize the “known” task assignment, and enlarge the team knowledge base. Moreover, it is more effective to support the process with data-driven approaches.
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