The brain-AI convergence: Predictive and generative world models for general-purpose computation

TL;DR

This paper compares brain and AI systems, highlighting shared predictive world models and learning mechanisms that underpin diverse functions and intelligence across both biological and artificial domains.

Contribution

It introduces a cross-domain perspective on world-model-based computation, revealing shared mechanisms in the neocortex and cerebellum and in attention-based AI systems.

Findings

Shared predictive mechanisms in brain regions and AI models

Unified computational framework for diverse cognitive functions

Convergence of brain and AI learning paradigms

Abstract

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.