  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.
