What are the theoretical and practical challenges in achieving general AI (AGI), and how do they differ from those associated with narrow AI?

Achieving AGI involves creating systems with the ability to understand, learn, and apply knowledge across a wide range of tasks, unlike narrow AI, which is specialized for specific tasks. Theoretical challenges include defining and measuring general intelligence, understanding the nature of consciousness, and creating architectures that can generalize knowledge. Practical challenges encompass building scalable and efficient learning algorithms, ensuring robust and safe behavior in diverse environments, and addressing the computational and data requirements for AGI development.