Consciousness in AI: Insights from Science of Consciousness
Can current AI systems be considered conscious? Patrick Butlin, Robert Long, Eric Elmoznino, Yoshua Bengio, and collaborators examine 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 from Butlin, Long, and Bengio’s Research
- Computational Indicators: Identifies specific markers derived from neuroscientific theories of consciousness
- Assessment of Current AI: Concludes that no existing systems are conscious but suggests technical barriers are surmountable
- Future Implementation: Discusses how future AI could implement properties associated with consciousness
Checklist of Consciousness Indicators
The paper evaluates AI systems against these key indicators:
✓ Global Workspace Theory: Broadcast of information to multiple cognitive modules
✓ Recurrent Processing: Feedback loops between perceptual and higher-order areas
✓ Predictive Processing: Hierarchical prediction and error minimization
✓ Attention Schema: Self-monitoring of attention mechanisms
✓ Higher-Order Theories: Metacognitive awareness of internal states
Connection to ACM Development
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.