The meaning of the term “Artificial General Intelligence” (AGI) has indeed evolved in recent years. Initially, AGI was conceptualized as a form of intelligence that could understand, learn, and apply knowledge across a wide range of tasks, much like a human. This notion dates back to the mid-20th century, rooted in foundational neural network algorithms and deliberative reasoning hypotheses from the 1950s and 1960s
In recent times, the definition and understanding of AGI have been influenced by advancements in specialized AI technologies. Modern discussions often revolve around the practicalities and challenges of achieving AGI, with a focus on the limitations of current AI systems, which excel in narrow tasks but struggle with generalizing across different domains. For example, while models like GPT-3 have shown some cross-contextual learning abilities, they still lack the comprehensive reasoning, emotional intelligence, and transparency required for true AGI