A Machine Consciousness architecture based on Deep Learning and Gaussian Processes

Recent developments in machine learning have pushed the tasks that machines can do outside the boundaries of what was thought to be possible years ago. Methodologies such as deep learning or generative models have achieved complex tasks such as generating art pictures or literature automatically. Machine Consciousness is a field that has been deeply studied and several theories based in the functionalism philosophical theory like the global workspace theory have been proposed. In this work, we propose an architecture that may arise consciousness in a machine based in the global workspace theory and in the assumption that consciousness appear in machines that have cognitive processes and exhibit conscious behaviour. This architecture is based in processes that use the recent Deep Learning and generative process models. For every module of this architecture, we provide detailed explanations of the models involved and how they communicate with each other to create the cognitive architecture. We illustrate how we can optimize the architecture to generate social interactions between robots and genuine pieces of art, both features correlated with machine consciousness. As far as we know, this is the first machine consciousness architecture that use generative models and deep learning to exhibit conscious social behaviour and to retrieve pictures and other subjective content made by robots.