In this paper, we argue that current AI research operates on a spectrum between two different underlying conceptions of intelligence: Intelligence Realism, which holds that intelligence represents a single, universal capacity measurable across all systems, and Intelligence Pluralism, which views intelligence as diverse, context-dependent capacities that cannot be reduced to a single universal measure. Through an analysis of current debates in AI research, we demonstrate how the conceptions remain largely implicit yet fundamentally shape how empirical evidence gets interpreted across a wide range of areas. These underlying views generate fundamentally different research approaches across three areas. Methodologically, they produce different approaches to model selection, benchmark design, and experimental validation. Interpretively, they lead to contradictory readings of the same empirical phenomena, from capability emergence to system limitations. Regarding AI risk, they generate categorically different assessments: realists view superintelligence as the primary risk and search for unified alignment solutions, while pluralists see diverse threats across different domains requiring context-specific solutions. We argue that making explicit these underlying assumptions can contribute to a clearer understanding of disagreements in AI research.

翻译：本文认为，当前人工智能研究基于两种不同的智能观念展开：智能现实主义主张智能是一种单一、普适的能力，可在所有系统中进行统一度量；而智能多元主义则认为智能是多样化的、依赖于情境的能力，无法被简化为单一的普适度量标准。通过对当前人工智能研究争论的分析，我们揭示了这些观念虽大多隐含未明，却从根本上塑造了跨广泛领域的实证证据解读方式。这两种观念在三个研究领域催生了根本不同的研究路径：在方法论层面，它们导致模型选择、基准测试设计和实验验证的不同取向；在解释层面，它们对同一实证现象（从能力涌现到系统局限性）产生相互矛盾的解读；在人工智能风险层面，它们形成截然不同的评估——现实主义者将超级智能视为主要风险并寻求统一的校准方案，而多元主义者则认为不同领域存在多样化威胁，需要针对具体情境的解决方案。我们主张，明确这些潜在假设将有助于更清晰地理解人工智能研究中的分歧。