The quest for Artificial General Intelligence (AGI) presents unique challenges that require innovative approaches to mimic human-like cognitive flexibility and adaptability. In this session, we will explore Hybrid Approaches to AGI, focusing on the integration of both symbolic and connectionist (neural network-based) methods to create more robust and versatile AGI systems.

Key topics include:

• Symbolic AI and Connectionism: A deep dive into the fusion of symbolic reasoning—based on structured knowledge representation—and neural network-based learning, which allows systems to learn from data. How can these two paradigms complement each other to create AGI systems that reason, learn, and adapt in more human-like ways?

• Bridging the Gap: Exploring how hybrid models can overcome the limitations of each individual approach. While symbolic systems excel at abstract reasoning and logic, connectionist models thrive in pattern recognition and learning from vast amounts of unstructured data. Together, they can bring about AGI that possesses both reasoning power and the ability to learn from experience.

• Real-World Applications of Hybrid AGI: Discussing the practical applications of hybrid AGI in fields like robotics, natural language processing, autonomous systems, and decision-making. How can these integrated systems perform better in complex, dynamic environments where flexibility and adaptability are key?

• Integration Challenges and Solutions: Addressing the technical challenges of integrating symbolic and connectionist approaches, including issues related to scalability, interpretability, and system coherence. What are the latest breakthroughs and strategies for making hybrid AGI a reality?

• Theoretical Foundations: Examining the underlying theories that support hybrid AGI, drawing from cognitive science, neuroscience, and AI research. How can we model human cognition more effectively by merging different AI approaches?

• Ethical and Safety Considerations: Considering the ethical implications and safety concerns of developing hybrid AGI systems. How do we ensure that these systems remain aligned with human values and are safe for deployment in complex real-world applications?

This session offers an exciting opportunity for researchers, practitioners, and enthusiasts to delve into the future of AGI by exploring how combining symbolic reasoning with neural networks could push the boundaries of what artificial intelligence can achieve. By bridging the strengths of both approaches, Hybrid AGI could unlock new potentials for solving the complex problems of tomorrow.