Scheidt, AlexanderPulver, TimAlt, FlorianBulling, AndreasDöring, Tanja2019-08-222019-08-222019https://dl.gi.de/handle/20.500.12116/24565Here we present Any-Cubes, a prototype toy with which children can intuitively and playfully explore and understand machine learning as well as Internet of Things technology. Our prototype is a combination of deep learning-based image classification [9] and machine-to-machine (m2m) communication via MQTT. The system consists of two physical and tangible wooden cubes. Cube 1 ("sensor cube") is inspired by Google’s teachable machine [11,12]. The sensor cube can be trained on any object or scenery. The machine learning functionality is directly implemented on the microcontroller (Raspberry Pi) by a Google Edge TPU Stick. Via MQTT protocol, the microcontroller sends its current status to Cube 2, the actuator cube. The actuator cube provides three switches (relays controlled by an Arduino board) to which peripheral devices can be connected. This allows simple if-then functions to be executed in real time, regardless of location. We envision our system as an intuitive didactic tool for schools and maker spaces.enMachine LearningTransfer LearningEdge AIEducationText/Conference Paper10.1145/3340764.3345375