可解释性是人工智能在工业5.0环境中采用人工智能的关键因素
Carlos Agostinho1,2, Zoumpolia Dikopoulou3, Eleni Lavasa4
1Center of Technology and System (CTS), Instituto de Desenvolvimento de Novas Tecnologias (UNINOVA), Intelligent Systems Associate Laboratory (LASI), Caparica, Portugal.
Frontiers in artificial intelligence
|December 26, 2023
概括
可解释的人工智能 (XAI) 通过实现透明的人机协作来增强制造业. XMANAI平台构建可解释的AI模型,在复杂的工业环境中提高信任和运营效率.
科学领域:
- 人工智能的人工智能
- 制造业 制造技术 制造技术
- 人与计算机的交互
背景情况:
- 黑盒人工智能模型缺乏透明度和可解释性,在制造业等关键部门构成挑战.
- 制造业需要可靠的AI来进行复杂的决策和人机协作.
- 现有的AI解决方案往往无法平衡性能与可解释性的需求.
研究的目的:
- 推出XMANAI平台,旨在促进制造业中透明和可信的人机协作.
- 发送地址 发送地址
- 透明度悖论就是一个矛盾.
- 在人工智能中,通过允许在不影响性能的情况下构建可解释的AI模型.
- 证明平台能够满足制造业特定需求,包括生命周期管理,安全和可靠的AI资产共享.
主要方法:
- 利用可解释的人工智能 (XAI) 的进展.
- 促进数据科学家和领域专家之间的快速合作.
- 开发具有高透明度和高性能的可解释AI模型.
- 实施生命周期管理,安全和可信共享AI资产的功能.
主要成果:
- 在XMANAI平台上,可以构建可解释的AI模型.
- 该平台成功解决了与人工智能透明度相关的技术挑战.
- 开发了一个评估框架来衡量XAI解决方案的性能.
- 证明的好处包括在制造业决策过程中提高透明度.
结论:
- XMANAI平台为制造业的AI透明度悖论提供了一个可行的解决方案.
- 它促进了人类和机器之间的信任和合作.
- 该方法提高了制造业的运营效率,并优化了制造业的商业价值.
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