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.

翻译：最近深层学习的进展使人工智能(AI)在许多感官、感知、语言或认知任务方面接近人的水平性能,然而,越来越需要新的、由大脑启发的认知结构。全球工作空间理论是指在专门单元网络之间整合和传播信息的大规模系统,以创造更高层次的认知和认识形式。我们主张,现在时机已经成熟,应考虑利用深层学习技术明确实施这一理论。我们提出了一个路线图,其基础是多种潜在空间(受过不同任务、不同感官投入和/或模式培训的神经网络)之间未经监督的神经转化,以创建一个独特、现代的全球潜在工作空间(GLW),审查全球工作空间的潜在功能优势以及神经科学影响。