  Recent advances in deep learning have allowed Artificial Intelligence (AI) to
reach near human-level performance in many sensory, perceptual, linguistic or
cognitive tasks. There is a growing need, however, for novel, brain-inspired
cognitive architectures. The Global Workspace theory refers to a large-scale
system integrating and distributing information among networks of specialized
modules to create higher-level forms of cognition and awareness. We argue that
the time is ripe to consider explicit implementations of this theory using deep
learning techniques. We propose a roadmap based on unsupervised neural
translation between multiple latent spaces (neural networks trained for
distinct tasks, on distinct sensory inputs and/or modalities) to create a
unique, amodal global latent workspace (GLW). Potential functional advantages
of GLW are reviewed, along with neuroscientific implications.
