Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

arxiv:2308.08708

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AI safety and consciousness · 2023-08-17

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Read in parts

1. Part 1
2. Part 2
3. Part 3
4. Part 4
5. Part 5
6. Part 6
7. Part 7
8. Part 8
9. Part 9
10. Part 10
11. Part 11
12. Part 12
13. Part 13
14. Part 14

Contents

• 1 Introduction
• 1.1 Terminology
• 1.2 Methods and Assumptions
• 1.2.1 Computational functionalism
• 1.2.2 Scientific theories of consciousness
• 1.2.3 Theory-heavy approach
• 2 Scientific Theories of Consciousness
• 2.1 Recurrent Processing Theory
• 2.1.1 Introduction to recurrent processing theory
• 2.1.2 Evidence for recurrent processing theory
• 2.1.3 Indicators from recurrent processing theory
• 2.2 Global Workspace Theory
• 2.2.1 Introduction to global workspace theory
• 2.2.2 Evidence for global workspace theory
• 2.2.3 Indicators from global workspace theory
• 2.3 Higher-Order Theories
• 2.3.1 Introduction to higher-order theories
• 2.3.2 Computational HOTs and GWT
• 2.3.3 Indicators from computational HOTs
• 2.4 Other Theories and Conditions
• 2.4.1 Attention Schema Theory
• 2.4.2 Predictive Processing
• 2.4.3 Midbrain Theory
• 2.4.4 Unlimited Associative Learning
• 2.4.5 Agency and Embodiment
• 2.4.5(a) Agency
• 2.4.5(b) Embodiment
• 2.4.5(c) Agency and embodiment indicators
• 2.4.6 Time and Recurrence
• 2.5 Indicators of Consciousness
• 3 Consciousness in AI
• 3.1 Implementing Indicator Properties in AI
• 3.1.1 Implementing RPT and PP
• 3.1.2 Implementing GWT
• 3.1.3 Implementing PRM
• 3.1.4 Implementing AST
• 3.1.5 Implementing agency and embodiment
• 3.2 Case Studies of Current Systems
• 3.2.1 Case studies for GWT
• 3.2.2 Case studies for embodied agency
• 4 Implications
• 4.1 Attributing Consciousness to AI
• 4.1.1 Under-attributing consciousness to AI
• 4.1.2 Over-attributing consciousness to AI
• 4.2 Consciousness and Capabilities
• 4.3 Recommendations
• Glossary