DeepAI AI Chat
Log In Sign Up

Deep Learning and the Global Workspace Theory

by   Rufin VanRullen, et al.

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.


Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning

In order perform a large variety of tasks and to achieve human-level per...

A Machine Consciousness architecture based on Deep Learning and Gaussian Processes

Recent developments in machine learning have pushed the tasks that machi...

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in ...

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning

In complex systems, we often observe complex global behavior emerge from...

Informing Artificial Intelligence Generative Techniques using Cognitive Theories of Human Creativity

The common view that our creativity is what makes us uniquely human sugg...

How can deep learning advance computational modeling of sensory information processing?

Deep learning, computational neuroscience, and cognitive science have ov...

Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks

Increasingly, cognitive scientists have demonstrated interest in applyin...