The Philosophical Foundations of Growing AI Like A Child

TL;DR

The paper argues that robustness gaps and Moravec’s Paradox in large language models arise from a lack of core knowledge that humans acquire through developmental growth rather than mere scaling. It analyzes how humans build complex cognition by grounding simple, domain-specific priors and examines why current LLMs fail to acquire similar core knowledge through scale alone. The authors propose a practical path—cognitive prototypes and large-scale synthetic data generated via physics-based simulations—to instill core knowledge in multi-modal models and to scaffold robust, generalizable reasoning. This child-like growth approach aims to deliver more reliable real-world intelligence without sacrificing scalability, offering a concrete research agenda for next-generation AI systems.

Abstract

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.