Modeling Language Learning Like a Baby

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Researchers explore whether artificial intelligence (AI) can learn language akin to a human infant by utilizing footage from a child’s early years to train an AI system.

Training AI with Baby’s Perspective

A team from New York University (NYU) recorded videos from a baby’s perspective, covering the period from six months to two years of age. Despite capturing only one percent of the child’s waking hours, this data was used to train a neural network, a computational model capable of learning patterns from input data.

Effective Language Learning with Limited Data

The study, published in the journal Science, demonstrates that the AI system, trained on a single child’s naturalistic experiences, successfully associates words with their visual counterparts. Wai Keen Vong, a research scientist at NYU, highlights the potential of this approach to reshape understanding of early language acquisition.

Insights into Language Learning

While top-tier AI systems typically train on vast text datasets, children are exposed to a much smaller linguistic environment annually. By employing AI models to study language learning, researchers can delve into debates surrounding the necessary components for word acquisition, from language-specific biases to associative learning.

Training and Testing the Model

Researchers utilized 60 hours of footage, containing approximately 250,000 words spoken to the child. This data was used to train a model, named the Child’s View for Contrastive Learning (CVCL), through contrastive learning, a machine learning method for associating visual and linguistic cues. The model’s performance was evaluated by presenting it with words and images, akin to how babies learn words.

Promising Results and Implications

The CVCL model successfully learned many words from the child’s daily experiences and demonstrated the ability to apply some words to novel images, akin to children’s learning patterns. These findings suggest that word learning from naturalistic data, coupled with generic learning mechanisms, holds promise for advancing our understanding of language acquisition.


SOURCE: Ref Image from Neuroscience News

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