Artificial intelligence (AI) and human-machine interaction (HMI) are two keywords that usually do not fit embedded applications. Within the steps needed before applying AI to solve a specific task, HMI is usually missing during the AI architecture design and the training of an AI model. The human-in-the-loop concept is prevalent in all other steps of developing AI, from data analysis via data selection and cleaning to performance evaluation. During AI architecture design, HMI can immediately highlight unproductive layers of the architecture so that lightweight network architecture for embedded applications can be created easily. We show that by using this HMI, users can instantly distinguish which AI architecture should be trained and evaluated first since a high accuracy on the task could be expected. This approach reduces the resources needed for AI development by avoiding training and evaluating AI architectures with unproductive layers and leads to lightweight AI architectures. These resulting lightweight AI architectures will enable HMI while running the AI on an edge device. By enabling HMI during an AI uses inference, we will introduce the AI-in-the-loop concept that combines AI's and humans' strengths. In our AI-in-the-loop approach, the AI remains the working horse and primarily solves the task. If the AI is unsure whether its inference solves the task correctly, it asks the user to use an appropriate HMI. Consequently, AI will become available in many applications soon since HMI will make AI more reliable and explainable.

翻译：人工智能（AI）与人机交互（HMI）是两个通常难以适配嵌入式应用的关键词。在将AI用于解决特定任务所需的步骤中，AI架构设计与模型训练阶段通常缺失HMI。从通过数据分析进行数据选择与清洗再到性能评估，人类参与循环的概念贯穿AI开发的其他所有环节。在AI架构设计阶段，HMI能够即时标记出架构中无效的网络层，从而轻松创建适用于嵌入式应用的轻量级网络架构。我们证明，通过使用该HMI，用户可立即判断应优先训练和评估哪种AI架构（因其可能对任务实现高精度）。该方法通过避免训练和评估含有无效层的AI架构，减少了AI开发所需的资源，并产生轻量级AI架构。这些轻量级AI架构将在边缘设备运行AI时实现HMI。通过在AI进行推理时启用HMI，我们引入"循环中的人工智能"概念——融合AI与人类的优势。在循环中的人工智能方法中，AI作为主力工作单元主要解决任务。当AI对推理结果能否正确解决问题存疑时，它通过合适的HMI向用户发出询问。由此，HMI将使AI更可靠且可解释，AI将很快在众多应用中普及。