AI models that learn like kids could reveal the secrets of our early years and inspire more efficient machine minds.

Babies are linguistic geniuses. Born into a noisy, chaotic world, they gradually distill incomprehensible sounds into words. Within two years, they can recognize thousands of words, follow complex sentences, and shout out their needs.

How they achieve this feat has long puzzled scientists. Babies develop language skills from an intimate world—the voices of their parents, chatter from strangers, nightly bedtime stories. A few hours of speech is enough to get them started.

This is a stark contrast to large language models (LLMs), which need to devour billions of words to mimic human conversation. Chatbots and AI agents are transforming how we interact with the digital sphere. But they guzzle water and energy, and signs that read “no data centers” now line neighborhood streets.

Building AI models that learn language the way babies do could reveal the secrets of our early years—including what happens when learning goes awry—and inspire more efficient machine minds.

An unusual study from Princeton University takes us a step closer. Using recordings from cameras and microphones from eight households, the team captured everyday sounds surrounding children from birth to roughly three years of age. These sonic diaries created a dense record of their waking lives. The researchers then built an AI learner for each child. Like a digital clone, each model experienced the child’s speech environment as it unfolded over time, rather than being trained on all the data in one fell swoop.

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