Deep Learning Approaches to Machine Consciousness in ACM

How can advanced machine learning techniques contribute to machine consciousness? This paper by Eduardo C. Garrido-Merchán and Martín Molina introduces a cognitive architecture combining deep learning and Gaussian processes for conscious-like behaviors.

A Machine Consciousness Architecture Based on Deep Learning and Gaussian Processes, authored by Eduardo C. Garrido-Merchán and Martín Molina, describes how recent advancements in AI can inform the development of cognitive processes and behaviors associated with consciousness.

Key Highlights

• Cognitive Architecture: Proposes a model inspired by Global Workspace Theory, integrating deep learning and symbolic reasoning.
• Gaussian Processes: Utilizes Gaussian processes for modeling uncertainty and learning from limited data.
• Practical Applications: Demonstrates how the architecture can simulate cognitive processes and conscious-like behaviors in machines.

Connection to ACM

The Artificial Consciousness Module (ACM) aligns with this study by:

• Advanced Learning Models: Leveraging deep learning and probabilistic models to enhance adaptability in simulations.
• Workspace Theory Integration: Drawing insights from cognitive frameworks for information processing and decision-making.
• Uncertainty Modeling: Adopting methodologies to handle uncertainty and improve AI adaptability in dynamic environments.

For a detailed exploration of the architecture and its applications, access the full paper here.