Global Workspace

The Global Workspace (GW) theory posits a central, shared representational space enabling communication and integration of information across multiple specialized modules, mirroring aspects of human cognition. Current research focuses on applying GW principles to improve artificial intelligence models, particularly in multimodal learning and Mixture-of-Experts architectures, by enhancing robustness, generalization, and interpretability. This work has implications for various fields, including robotics (human-robot collaboration and sensor integration), reinforcement learning (cross-modal transfer), and the development of more efficient and explainable AI systems.