A Machine Consciousness architecture based on Deep Learning and Gaussian Processes
This work addresses the foundational challenge of machine consciousness for AI researchers, but it appears incremental as it builds on existing theories without demonstrating new empirical gains.
The authors tackled the problem of achieving machine consciousness by proposing an architecture based on the global workspace theory, integrating deep learning and Gaussian processes to model cognitive processes and conscious behavior, but no concrete results or numbers are provided.
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.