Kids outlearn AI—and we still don’t know why

Large Language Models (LLMs) require significantly more data than children to learn language, with models like Meta’s Llama 3.1 processing 15 trillion tokens during pretraining. This disparity highlights a key challenge for AI, as the availability of training data may diminish by the 2030s. Researchers are exploring how LLMs, despite their data demands, can serve as powerful simulations to test hypotheses about human language acquisition, leading to initiatives like the BabyLM competition, which focuses on training models with smaller datasets.