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. On the other
hand, symbolic resources have also been developed further and behave well in
problems such as the ones proposed by common sense reasoning. Machine
Consciousness is a field that has been deeply studied and several theories
based in the functionalism philosophical theory like the global workspace
theory or information integration have been proposed that try to explain the
ariseness of consciousness in machines. 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 has cognitive processes and exhibit conscious behaviour. This architecture
is based in processes that use the recent developments in artificial
intelligence models which output are these correlated activities. For every one
of the modules of this architecture, we provide detailed explanations of the
models involved and how they communicate with each other to create the
cognitive architecture.
