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empirical studyChapter 1arXiv · 2023

Sparks of Artificial General Intelligence: Early experiments with GPT-4

Sébastien Bubeck (Microsoft Research), Varun Chandrasekaran (Microsoft Research)

Abstract

We investigate an early version of GPT-4 and argue that it exhibits sparks of AGI. We demonstrate GPT-4's capabilities across diverse domains and analyze its limitations.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper established the capability baseline that made Zone III workflows conceivable. Understanding both the capabilities and limitations of frontier models is essential for realistic Zone III planning.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Comprehensive GPT-4 capability analysis
  • →AGI sparks identification
  • →Limitation analysis

Topics

GPT-4AGIcapability evaluationLLM capabilities