What are the key goals, approaches, and challenges in AGI research?

What are the key goals, approaches, and challenges in artificial general intelligence (AGI)research?

Artificial General Intelligence (AGI)research aims to create software possessing human-level intelligence. Unlike current AI systems which excel at specific tasks (Narrow AI), AGI's goal is to build systems capable of self-teaching and performing tasks they haven't been explicitly trained for – exhibiting adaptability and generalization akin to human cognitive abilities. This ambitious goal necessitates diverse approaches.

Several theoretical approaches guide AGI research. The symbolic approach uses logical networks to represent knowledge, enabling higher-level reasoning. However, it struggles with subtle cognitive tasks like perception. The connectionist approach, conversely, models the brain's neural networks, striving for low-level cognitive capabilities like those seen in Large Language Models (LLMs). A hybrid approach attempts to combine the strengths of both symbolic and connectionist methods. Other approaches, like the whole organism architecture, propose integrating AI with physical embodiment to facilitate learning through interaction with the environment. Each approach aims to solve the complex problem of creating truly generalizable intelligence in different ways.

Despite considerable progress in areas like deep learning and generative AI (Generative AI), significant challenges remain. A major hurdle is achieving the ability to transfer knowledge across domains, a skill humans effortlessly employ. Current AI models often require extensive retraining for new tasks. Further challenges lie in replicating crucial aspects of human intelligence such as emotional intelligence and nuanced sensory perception. The development of genuinely creative AI, capable of novel problem-solving, is also a significant obstacle.