Artificial General Intelligence (AGI) Level

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A Artificial General Intelligence (AGI) Level is a AI classification framework for the capabilities and behavior of AGI model.

  • Context:
    • It can provide a structured approach to evaluate AI systems' performance and generality.
    • It can assist in comparing different AI models based on their cognitive abilities.
    • It can support the identification of progress milestones in AI development.
    • It can range from assessing emerging AI systems to evaluating superhuman-level AI, depending on the sophistication of their capabilities.
    • ...
  • Example(s):
    • The five-level classification proposed by Google DeepMind researchers, which includes:
      • Emerging AGI: AI systems with initial general capabilities.
      • Competent AGI: AI outperforming 50% of skilled adults in various non-physical tasks.
      • Expert AGI: AI matching or exceeding expert human performance in specific domains.
      • Virtuoso AGI: AI demonstrating superior performance across multiple domains.
      • Superhuman AGI: AI surpassing human capabilities in nearly all cognitive tasks.
    • ...
  • Counter-Example(s):
  • See: Artificial General Intelligence, Artificial Narrow Intelligence, Artificial Superintelligence, AGI Measure.

References

2024

2024

  • Perplexity.ai
    • Q: Search for classification frameworks for evaluating Artificial General Intelligence (AGI) systems? I'm interested in understanding how different levels of AGI are categorized, from basic capabilities onward. This should include how current AI systems fit into these levels, what capabilities define each level, and how this framework helps in comparing different AI models. Also, how do these classification systems help track progress in AI development, and what are the key characteristics that distinguish one level from another?"
    • Artificial General Intelligence (AGI) refers to AI systems capable of performing any intellectual task that a human can achieve. Organizations like Google DeepMind have proposed structured frameworks to classify AGI systems into levels, providing a roadmap for tracking their development and capabilities. While specific frameworks from organizations like OpenAI or Anthropic were not detailed, their research contributes broadly to the field.
      1. AGI Classification Framework

The classification framework defines five distinct levels of AGI:

1. **Emerging AGI**: 

  - AI systems with basic general capabilities but significant limitations.
  - Examples: Current large language models like ChatGPT and LLaMA 2.
  - Represents entry-level systems with broad but shallow abilities.

2. **Competent AGI**: 

  - Outperforms 50% of skilled adults in non-physical tasks.
  - Demonstrates reliable performance across multiple domains with consistent problem-solving abilities.

3. **Expert AGI**: 

  - Exceeds average human performance in cognitive tasks.
  - Demonstrates deep expertise across specialized fields, with strong reasoning and application of knowledge.

4. **Virtuoso AGI**: 

  - Surpasses human expertise in most domains.
  - Exhibits exceptional creativity, analytical abilities, and problem-solving innovation.

5. **Superhuman AGI (Artificial Superintelligence)**: 

  - Outperforms 100% of humans across all cognitive tasks.
  - Represents unprecedented levels of intelligence, creativity, and adaptability.
      1. Capabilities Defining Each Level

- **Cognitive Flexibility**: The ability to apply knowledge across varying contexts. - **Problem-Solving Skills**: Progression from basic solutions in emerging AGI to innovative capabilities in virtuoso AGI. - **Learning and Adaptation**: Autonomous improvement and application of knowledge increase across levels.

      1. Historical Context and Key Framework Characteristics

- The term "artificial general intelligence" first appeared in 1997, with early formalisms like AIXI proposed in 2000. - Modern frameworks emphasize measurable benchmarks, such as outperforming humans in cognitive tasks or demonstrating cross-domain expertise.

      1. Applications and Utility

Classification frameworks enable researchers and developers to: - Compare AI systems using structured performance benchmarks. - Identify gaps in current capabilities to guide research priorities. - Track progress toward AGI goals with clearly defined thresholds.

      1. Key Characteristics Distinguishing Levels

- **Performance Thresholds**: Benchmarks relative to human performance. - **Scope of Abilities**: The range of tasks the system can perform effectively. - **Innovative Capacity**: The ability to generate novel solutions and ideas.

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