Kid Teaches GAI How Humans Learn Language!

A group of New York University Researchers has used a neural network and a single baby to train a Generative AI model to acquire language more like humans do!

A single baby has been able to teach Generative AI (GAI) how humans learn language!

Generative AI solutions like ChatGPT that leverage Large Language Models to communicate more like humans have been revolutionizing all industries, not the least of which is healthcare. It takes millions of data points to train these GAI applications to “speak” as humans do, and even then, they often still fall short in understanding the nuances of human language.

Children, on the other hand, have access to only a tiny fraction of that data, yet by age three, they’re communicating in quite sophisticated ways. This prompted a team of researchers at New York University to wonder if AI could learn like a baby. What could an AI model do when given a far smaller data set—the sights and sounds experienced by a single child learning to talk?

For this experiment, the researchers relied on 61 hours of video from a helmet camera worn by a child who lives near Adelaide, Australia. After feeding that data into the AI model, it managed to match words to the objects they represent. “There’s enough data even in this blip of the child’s experience that it can do genuine word learning,” says Brenden Lake, a computational cognitive scientist at New York University and an author of the study. This work, published in Science Today, not only provides insights into how babies learn but could also lead to better AI models.

The child, Sam, wore the helmet cam on and off from the time he was about six months old until he was speaking rather fluently at two. The camera captured the things Sam looked at and paid attention to during about 1% of his waking hours. It recorded Sam’s two cats, his parents, his crib and toys, his house, his meals, and much more. “This data set was totally unique,” Lake says. “It’s the best window we’ve ever had into what a single child has access to.”

To train their AI model, Lake, and his colleagues used 600,000 video frames paired with the phrases that were spoken by Sam’s parents or other people in the room when the image was captured—37,500 “utterances” in all. Sometimes, the words and objects matched. Sometimes they didn’t. For example, in one still, Sam looks at a shape sorter, and a parent says, “You like the string.” In another, an adult hand covers some blocks, and a parent says, “You want the blocks too.”

The team gave the model two cues. When objects and words occur together, that’s a sign that they might be linked. But when an object and a word don’t occur together, that’s a sign they likely aren’t a match. “So we have this sort of pulling together and pushing apart that occurs within the model,” says Wai Keen Vong, a computational cognitive scientist at New York University and an author of the study. “Then the hope is that there are enough instances in the data where when the parent is saying the word ‘ball,’ the kid is seeing a ball,” he says.

Matching words to the objects they represent may seem like a simple task, but it’s not. To give you a sense of the scope of the problem, imagine the living room of a family with young children. It has all the normal living room furniture but also kid clutter. The floor is littered with toys. Crayons are scattered across the coffee table. There’s a snack cup on the windowsill and laundry on a chair. If a toddler hears the word “ball,” it could refer to a ball. But it could also refer to any other toy, or the couch, or a pair of pants, or the shape of an object, or its color, or the time of day. “There’s an infinite number of possible meanings for any word,” Lake says.

AI models that can pick up some of the ways in which humans learn language might be far more efficient at learning; they might act more like humans and less like “a lumbering statistical engine for pattern matching,” as the linguist Noam Chomsky and his colleagues once described large language models like ChatGPT.

Beyond that, creating models that can learn more like children will not only improve AI but help researchers better understand human learning and development, which could have major implications for treating learning disorders such as autism.

How BigRio Helps Bring LLM and Advanced AI Solutions to Healthcare

We share the belief with the NYU researchers in the transformative power of GAI, particularly in the areas of medical research and the delivery of healthcare. But, when it comes to leveraging GAI and LLMs for healthcare, there are two primary approaches: building your own model like these researchers did or utilizing existing models developed by big tech companies like GPT.

Of course, it is much easier to use an off-the-shelf LLM solution; however, while these “open source” GAI/LLM solutions like ChatGPT have gained significant attention across various fields, including healthcare, they are limited by their need to be non-specific in scope and ability.

What if you could build an LLM model for your healthcare organization’s unique targets and needs? You can, with BigRio’s Help!

Creating a large language model from scratch requires extensive resources, the expertise of AI developers and data scientists, the MLOps team, and computational power. It involves training the model on massive datasets, fine-tuning it through multiple iterations, and optimizing its performance. This process demands substantial time, expertise, and computational resources, including high-performance hardware and storage systems. The good news is that the BigRio team can offer you all of the above and more!

BigRio has long been a facilitator and incubator in leveraging AI to improve healthcare delivery, originally in the field of diagnostics and research. We have recently been focusing our efforts on supporting startups and developing our own solutions that use LLMs and GAI to improve those areas of healthcare as well as in direct patient interactions and customer relationship management.

You can read much more about how AI is redefining healthcare delivery and drug discovery in my new book Quantum Care: A Deep Dive into AI for Health Delivery and Research. It’s a comprehensive look at how AI and machine learning are being used to improve healthcare delivery at every touchpoint.

Another article you might find interesting: https://bigr.io/transforming-the-healthcare-industry-with-large-language-models/

Rohit Mahajan is a Managing Partner with BigRio. He has particular expertise in the development and design of innovative solutions for clients in Healthcare, Financial Services, Retail, Automotive, Manufacturing, and other industry segments.

BigRio is a technology consulting firm empowering data to drive innovation and advanced AI. We specialize in cutting-edge Big Data, Machine Learning, and Custom Software strategy, analysis, architecture, and implementation solutions. If you would like to benefit from our expertise in these areas or if you have further questions on the content of this article, please do not hesitate to contact us.