Artificial General Intelligence (AGI) and Levels of AGI

Artificial General Intelligence (AGI) is a highly debated topic in the field of Artificial Intelligence (AI) research. It refers to the creation of machines capable of achieving goals in a wide range of environments, much like humans. However, the distinction between AGI and Human-Level Artificial Intelligence (HLAI) is often overlooked, leading to confusion about the capabilities and implications of AGI. This paper aims to provide a comprehensive overview of AGI, its levels, and the current state of research in this field.Definition and DistinctionAGI is often used interchangeably with HLAI, but this equivalence is unjustified. Humans are not general intelligences, and AGI should not be considered as simply a machine capable of achieving human-level intelligence. Instead, AGI should be defined as a machine that can achieve goals in a wide range of environments, without being limited to specific domains or tasks.Levels of AGIThe levels of AGI are not well-defined and are often debated among researchers. However, a common framework is to categorize AGI into three levels based on its capabilities:

1. Narrow or Weak AI: This level of AI is limited to performing specific tasks, such as image recognition, natural language processing, or playing chess. It lacks the ability to generalize and adapt to new situations.
2. General or Strong AI: This level of AI is capable of achieving goals in a wide range of environments, but it is still limited to a specific domain or task. It can generalize and adapt, but it lacks human-like intelligence.
3. Artificial Superintelligence (ASI): This level of AI is capable of achieving goals in a wide range of environments, across multiple domains and tasks. It has human-like intelligence and can adapt and generalize in complex and dynamic environments.

Current Research and ChallengesCurrent research in AGI is focused on developing the SP Theory of Intelligence (SPTI) and its realisation in the SP Computer Model. This theory aims to address 20 significant problems in AI research, including the tendency of deep neural networks to make major errors in recognition, the need for a coherent account of generalisation, and the need for transparency in the representation and processing of knowledge.Another area of research is the impact of AGI on the economy. A recent study found a positive Pearson correlation between the growth of AGI and real GDP, indicating that AGI has a significant economic effect. However, this study also highlights the need for further research into the quantitative relations between AGI and economic parameters.ConclusionIn conclusion, AGI is a complex and multifaceted topic that requires a nuanced understanding of its definition, levels, and implications. While current research is focused on developing the capabilities of AGI, it is essential to recognize the distinction between AGI and HLAI and to address the challenges and limitations of AGI. Further research is needed to fully understand the potential of AGI and its impact on society.