Consciousness in AI: Insights from Science of Consciousness

Can current AI systems be considered conscious? This paper by Patrick Butlin and colleagues examines the possibility through neuroscientific theories of consciousness and computational indicators.

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness, authored by Patrick Butlin, Robert Long, Eric Elmoznino, Yoshua Bengio, and others, applies theories like Global Workspace Theory, Recurrent Processing, and Predictive Processing to evaluate AI systems against indicators of consciousness.

Key Highlights

• Indicator Properties: Identifies computational indicators derived from neuroscientific theories of consciousness.
• Assessment of Current AI: Concludes that no existing systems are conscious but suggests technical barriers are surmountable.
• Future Systems: Discusses how future AI could implement properties associated with consciousness.

Connection to ACM

The Artificial Consciousness Module (ACM) aligns with this research through:

• Neuroscientific Theories: Applying theories like Global Workspace and Predictive Processing to develop consciousness indicators.
• Evaluative Frameworks: Using computational benchmarks to test and refine artificial consciousness.

For a detailed exploration of the methodologies and conclusions, access the full paper here.