AI researchers looking for better ways to train large language models are turning to the masters of language acquisition – children – to find out how it's done.
"The best AI systems learn from this astronomical amount of text mined from the web and all over the place. The best systems now train on trillions of words in order to learn language," Brenden Lake, a psychologist at New York State University studying human and artificial intelligence, told."It's remarkable that they do become fluent in language, but of course, children don't need nearly that much experience.
The model attempts to associate video footage from the child's perspective with the words spoken by the tyke's caregiver in a manner similar to how OpenAI's Clip model works tocaptions to images, he explained. Clip can take an image as input, and output a suggested descriptive caption, based on its training data of image-caption pairs.
The experiment, he explained, isn't whether a model can be trained to match objects in images to the corresponding word – that's already been done by OpenAI and others. Instead, researchers hope to understand whether a model can actually learn to identify objects using nothing more than the incredibly sparse dataset available to a child.
"It didn't learn everything that a child would know. That's why the project is unfinished," he stressed."It was only about 60 hours of annotated speech. So that's only about one percent of the experience a child would have gotten in that two-year period. We need more data in order to get a better sense of what's learnable."Lake also admits that the methodology used by the first model introduced certain limitations.