通过检测误报减少案例，揭示工业AI研究方法的七大缺陷
Towards Improved Research Methodologies for Industrial AI: A case study of false call reduction
- 识别出当前AI研究中七个常见方法缺陷
- 实验证明主流方法在此场景下会失败
- 强调需以业务目标为导向设计评估指标
当前人工智能研究方法是否足以支撑成功、高效且盈利的AI应用？本文以自动化光学检测中的误报减少为例，揭示了相关学术工作中普遍存在的七项方法论缺陷，并通过实验展示了其后果。研究发现，现有最佳实践方法在此用例中将失效。文章主张应引入需求感知的评估指标，明确成功标准，并深入分析实验数据的时间动态特征。本工作呼吁研究者对自身方法进行批判性审视，以推动更成功的应用型AI研究。
原文摘要 · Abstract (English)
Are current artificial intelligence (AI) research methodologies ready to create successful, productive, and profitable AI applications? This work presents a case study on an industrial AI use case called false call reduction for automated optical inspection to demonstrate the shortcomings of current best practices. We identify seven weaknesses prevalent in related peer-reviewed work and experimentally show their consequences. We show that the best-practice methodology would fail for this use case. We argue amongst others for the necessity of requirement-aware metrics to ensure achieving business objectives, clear definitions of success criteria, and a thorough analysis of temporal dynamics in experimental datasets. Our work encourages researchers to critically assess their methodologies for more successful applied AI research.
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