Learning to Predict Requires Integrated Information

Overview

Join us as we explore the fascinating world of Integrated Information Theory and its role in quantifying consciousness within neural networks. This event dives deep into the connection between this theory and the development of embodied intelligence and predictive learning in artificial intelligence agents.

Perfect for those interested in the cross-section of AI and consciousness studies, this talk is a must-see for anyone eager to stay at the forefront of artificial intelligence research and its practical applications in predictive learning.

Syllabus

• Introduction to Integrated Information Theory (IIT)
Overview of Consciousness Theories
Historical Context and Development of IIT
Core Principles of IIT
• Quantifying Consciousness in Neural Networks
Metrics and Measures in IIT
Practical Applications of IIT in AI
Case Studies: Consciousness Levels in Neural Systems
• Embodied Intelligence
Definition and Components of Embodied AI
Interactions between Body, Environment, and Mind
Case Studies: Embodied Agents in Real-World Scenarios
• Predictive Learning in AI Agents
Understanding Predictive Coding in AI
Models and Algorithms for Prediction
Applications in Various AI Domains
• Integration of IIT with Embodied Intelligence
Synergies between IIT and Embodied AI
Enhanced Predictive Abilities through Integrated Information
Case Studies: Implementing IIT in Embodied AI Systems
• Practical Implementation
Tools and Frameworks for Modeling IIT
Designing Experiments for Measuring Integrated Information
Challenges and Solutions in Real-World Applications
• Future Directions and Ethical Considerations
Emerging Trends in Integrated Information and AI
Ethical Implications of Conscious AI
Future Research Opportunities and Open Questions

---

Subjects

Conference Talks