Augmented Intelligence for Quality Control of Manual Assembly Processes using Industrial Wearable Systems

Empowered by machine learning and artificial intelligence innovations, IoT devices have become a leading driver of digital transformation. A promising approach are augmented intelligence solutions which seek to enhance human performance in complex tasks. However, there are no turn-key solutions for developing and implementing such systems. One possible avenue is to complement multi-purpose hardware with flexible AI solutions which are adapted to a given task. We illustrate the bottom-up development of a machine learning backend for an augmented intelligence system in the manufacturing sector. A wearable device equipped with highly sensitive sensors is paired with a deep convolutional neural network to monitor connector systems assembly processes in real-time. Our initial study yields promising results in an experimental environment. While this establishes the feasibility of the suggested approach, further evaluations in more complex test cases and ultimately, in a real-world assembly process have to be performed

SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models

Large language models (LLMs) have recently garnered significant accomplishments in various exploratory tasks, even surpassing the performance of traditional reinforcement learning-based methods that have historically dominated the agent-based field. The purpose of this paper is to investigate the efficacy of LLMs in executing real-time strategy war tasks within the StarCraft II gaming environment. In this paper, we introduce SwarmBrain, an embodied agent leveraging LLM for real-time strategy implementation in the StarCraft II game environment. The SwarmBrain comprises two key components: 1) a Overmind Intelligence Matrix, powered by state-of-the-art LLMs, is designed to orchestrate macro-level strategies from a high-level perspective. This matrix emulates the overarching consciousness of the Zerg intelligence brain, synthesizing strategic foresight with the aim of allocating resources, directing expansion, and coordinating multi-pronged assaults. 2) a Swarm ReflexNet, which is agile counterpart to the calculated deliberation of the Overmind Intelligence Matrix. Due to the inherent latency in LLM reasoning, the Swarm ReflexNet employs a condition-response state machine framework, enabling expedited tactical responses for fundamental Zerg unit maneuvers. In the experimental setup, SwarmBrain is in control of the Zerg race in confrontation with an Computer-controlled Terran adversary. Experimental results show the capacity of SwarmBrain to conduct economic augmentation, territorial expansion, and tactical formulation, and it shows the SwarmBrain is capable of achieving victory against Computer players set at different difficulty levels

Community space in complex learning communities : lessons learnt

Highly complex learning communities where diverse participants collaborate to achieve multiple aims through synergy have the potential to be highly creative and productive. However the diversity and multiple aims can also mean the advantages of a community - share understand, trust and direction - are difficult to achieve, resulting in few if any of the aims being realised. We review two case studies, where the learning community is trying to achieve multiple aims, in order to explore how virtual and physical space are employed to support collaborative learning and enhance synergistic potential. The analysis shows that high levels of diversity have influenced these spaces and trends towards differentiation and holistically designed hybrid, virtual and physical, collaboration space. The characteristics of theses cases are sufficiently general to lead us to draw insights for the building of collaborative space in multi-purpose complex learning communities. These are equably applicable to learning communities which share features such as heterogeneity, multiple locations or a mixture of spaces