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We developed a human-agnostic metric for AI evaluation, grounded in Algorithmic Information Theory. This approach reveals limitations in current large language models and suggests symbolic AI integration for true artificial general intelligence (AGI) and artificial super intelligence (ASI).

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Area of Science:

  • Artificial Intelligence
  • Algorithmic Information Theory
  • Metrology

Background:

  • Existing AI evaluation metrics are often human-centric or rely on pattern matching.
  • Claims of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI) require robust, objective evaluation methods.
  • Algorithmic Information Theory (AIT) provides a formal framework for understanding abstraction and prediction in intelligent systems.

Purpose of the Study:

  • To introduce a novel, human-agnostic metric for evaluating foundational and frontier AI models.
  • To assess AI models, including large language models (LLMs), against objective benchmarks based on AIT principles.
  • To explore the potential of hybrid neuro-symbolic approaches for achieving higher intelligence.

Main Methods:

  • Developed an increasing-complexity, open-ended metric based on randomness and optimal inference.
  • Applied the metric to benchmark leading LLMs and other AI models.
  • Utilized principles of AIT to define Universal Intelligence (UAI) targets and trends.
  • Evaluated a hybrid neuro-symbolic approach using compression-based abstraction and sequence prediction.

Main Results:

  • Leading LLMs show strong performance but exhibit regressions in later versions, deviating from estimated UAI trends.
  • A hybrid neuro-symbolic approach outperformed specialized models in a relevant compression-based prediction task.
  • Predictive power is directly proportional to algorithmic compression, not statistical compression.

Conclusions:

  • Current LLMs, despite advancements, are not consistently progressing towards AGI/ASI as measured by AIT.
  • Future AI progress necessitates the integration of symbolic approaches, complementing current deep learning methods.
  • Algorithmic Information Theory offers a powerful metrological framework for objective AI evaluation and development.