AI Model Mimics Child Learning to Explain Language

Researchers at Wits University used deep linear neural networks to study how language structure evolves across generations ^[1]. The study, published in the journal PNAS, found that language naturally becomes more structured and easier to learn over time ^[2]. This process mimics the cognitive development observed in human children during early growth ^[1][3].

The research offers new insights into how large-scale AI language models operate and improve ^[2]. By simulating generational learning, the team demonstrated that structural complexity is a byproduct of the need for learnability ^[1][2]. These findings help bridge the gap between human linguistics and machine learning architectures ^[3].