让AI像孩子一样成长，通过模拟人类认知发展提升实际问题解决能力
The Philosophical Foundations of Growing AI Like A Child
- 借鉴人类认知发展，用合成数据训练模型建立基础认知结构
- 现有语言模型缺乏核心知识，导致真实场景下表现差、推理不鲁棒
- 适合关注通用人工智能与认知科学融合的研究者
尽管当前语言模型在高层推理上表现优异，但在真实场景中缺乏鲁棒性，且在人类本能掌握的基础问题解决任务上表现不佳。本文指出，这些问题源于人类与机器认知发展的核心差异：两者虽都依赖表征能力的提升，但语言模型缺少人类具有的核心知识与基础认知结构，导致复杂技能无法建立在简单技能之上，难以实现真正泛化。文章分析了人类核心知识的实证证据，揭示语言模型未能获取这些知识的原因，并论证该限制并非架构本质缺陷。最后提出可行方案：通过认知原型策略大规模生成合成训练数据，系统性地将核心知识融入未来的多模态语言模型。
原文摘要 · Abstract (English)
Despite excelling in high-level reasoning, current language models lack robustness in real-world scenarios and perform poorly on fundamental problem-solving tasks that are intuitive to humans. This paper argues that both challenges stem from a core discrepancy between human and machine cognitive development. While both systems rely on increasing representational power, the absence of core knowledge, foundational cognitive structures in humans, prevents language models from developing robust, generalizable abilities, where complex skills are grounded in simpler ones within their respective domains. It explores empirical evidence of core knowledge in humans, analyzes why language models fail to acquire it, and argues that this limitation is not an inherent architectural constraint. Finally, it outlines a workable proposal for systematically integrating core knowledge into future multi-modal language models through the large-scale generation of synthetic training data using a cognitive prototyping strategy.
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