论文标题
在值得信赖的自主系统中开发和操作人工智能模型
Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems
论文作者
论文摘要
在自主系统（AS）中处理人工智能（AI）模型的公司遇到了几个问题，例如用户缺乏对不良条件或未知条件的信任，软件工程和AI模型开发之间的差距以及在不断变化的操作环境中运行。该过程中的工作旨在通过定义协调这两种活动的方法来弥合基于可信赖的AI的开发和操作之间的差距。在工业环境中，我们综合了基于AI的主要挑战。我们反思克服这些挑战所需的研究工作，并提出了一种新颖的，整体的DevOps将其付诸实践。我们详细介绍了四个研究方向：（a）通过监视基于AI的AS并确定在关键情况下的自我适应需求来增加用户的信任； （b）AI模型的开发和演变的综合敏捷过程以及AS； （c）在AS的分布式设置中连续部署AI模型的不同上下文特定实例； （d）基于AI的AS的基于DevOps的整体生命周期。
Companies dealing with Artificial Intelligence (AI) models in Autonomous Systems (AS) face several problems, such as users' lack of trust in adverse or unknown conditions, gaps between software engineering and AI model development, and operation in a continuously changing operational environment. This work-in-progress paper aims to close the gap between the development and operation of trustworthy AI-based AS by defining an approach that coordinates both activities. We synthesize the main challenges of AI-based AS in industrial settings. We reflect on the research efforts required to overcome these challenges and propose a novel, holistic DevOps approach to put it into practice. We elaborate on four research directions: (a) increased users' trust by monitoring operational AI-based AS and identifying self-adaptation needs in critical situations; (b) integrated agile process for the development and evolution of AI models and AS; (c) continuous deployment of different context-specific instances of AI models in a distributed setting of AS; and (d) holistic DevOps-based lifecycle for AI-based AS.
