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    Teaching Assistant Robots in Various Fields: Natural Sciences, Medicine and Specific Non-Deterministic Conditions
    (CEUR-WS, 2023) Hasko, R.; Hasko, O.; Kutucu, H.
    The article describes an approach to creating a Robot Assistant as an intelligent system that uses generative artificial intelligence (GAI) and has a physical body and a voice interface for communication. To understand the actions of the assistant and increase trust in the system, Explainable AI is used, and for better efficiency - Federated Learning, that is, the robot learns on distributed data without the need for centralized collection of this data. XAI allows users to understand what algorithms and data are driving the decision-making assistant. Access to Large Language Models means that the robot assistant uses powerful language models such as GPT-3.5 to understand and generate language. This allows the assistant to understand user requests and provide information and recommendations based on a large amount of knowledge and texts. Robot interactions with the real world are becoming better and more predictable thanks to Embodied AI. It can perform tasks related to moving physical objects, understand the gestures and movements of users, and interact with objects in real-time, moving in different places and environments. The software solution is based on ROS2 with the necessary extensions for the listed technologies. All this together makes the work of the assistant effective, providing understanding and explanation of decisions, protecting data privacy and providing natural communication through the voice interface. © 2023 Copyright for this paper by its authors.