Recent advances in general-purpose AI systems with attention-based transformers offer a potential window into how the neocortex and cerebellum, despite their relatively uniform circuit architectures, give rise to diverse functions and, ultimately, to human intelligence. This Perspective provides a cross-domain comparison between the brain and AI that goes beyond the traditional focus on visual processing, adopting the emerging perspecive of world-model-based computation. Here, we identify shared computational mechanisms in the attention-based neocortex and the non-attentional cerebellum: both predict future world events from past inputs and construct internal world models through prediction-error learning. These predictive world models are repurposed for seemingly distinct functions -- understanding in sensory processing and generation in motor processing -- enabling the brain to achieve multi-domain capabilities and human-like adaptive intelligence. Notably, attention-based AI has independently converged on a similar learning paradigm and world-model-based computation. We conclude that these shared mechanisms in both biological and artificial systems constitute a core computational foundation for realizing diverse functions including high-level intelligence, despite their relatively uniform circuit structures. Our theoretical insights bridge neuroscience and AI, advancing our understanding of the computational essence of intelligence.

翻译：近期基于注意力机制的Transformer通用人工智能系统的进展，为理解新皮层和小脑如何凭借相对均一的电路结构实现多样化功能并最终产生人类智能提供了潜在的窗口。本文从跨领域视角比较大脑与人工智能，超越传统视觉处理的局限，采纳基于世界模型的计算这一新兴观点。我们发现，基于注意力的新皮层与非注意力机制的小脑共享计算机制：二者均依据过往输入预测未来世界事件，并通过预测误差学习构建内部世界模型。这些预测性世界模型被重新用于看似不同的功能——感知处理中的理解与运动处理中的生成——使大脑得以实现多领域能力及类人的适应性智能。值得注意的是，基于注意力的人工智能已独立地趋同于类似的学习范式及基于世界模型的计算。我们得出结论：尽管生物与人工系统均具有相对均一的电路结构，这些共享机制构成了实现多样化功能（包括高级智能）的核心计算基础。我们的理论洞见连接了神经科学与人工智能，推进了对智能计算本质的理解。