This session explores the exciting field of biologically inspired AGI, where we draw upon the principles of biological systems to develop intelligent machines. By understanding how the human brain learns, adapts, and perceives the world, we can create AGI systems with more flexible, efficient, and resilient capabilities.

Key topics include:

• Neural Networks and Brain-Inspired Systems: How the structure and function of biological neural networks influence the design of AGI algorithms capable of complex learning and decision-making.

• Cognitive Processes and Neuro-Inspired Learning: Understanding how processes like memory, attention, and perception inform AGI development and improve its problem-solving abilities.

• Evolutionary Algorithms: Applying principles from biological evolution to AGI systems, enabling them to adapt and improve autonomously.

• Redundancy and Resilience in AGI: Exploring how biological redundancy can inspire AGI systems that are robust and adaptable in dynamic environments.

• Self-Organizing Systems: Investigating how AGI systems can self-organize and evolve to achieve higher-level tasks, similar to natural biological processes.

This session brings together experts to share innovative developments in biologically inspired AGI, offering a glimpse into how nature’s intelligence can shape the future of artificial systems.