summary: Despite the large processing power of AI, children still outperform machines in learning languages, and the new framework can help explain why. Unlike AI systems that passively absorb texts, children learn through multisensory exploration, social interaction, and self-directed curiosity.
Their language learning is active, embodied, deeply connected to motor, cognitive, and emotional development. These insights may not only reconstruct how we understand early childhood, but also guide future designs of AI systems, such as more human.
Important facts:
- Evolutionary learning: Children use their vision, sound, movement and touch to build language in a rich, interactive world.
- Active Explore: Children create moments of learning by pointing out, raw and engaged with their surroundings.
- AI vs. human learning: The machine processes static data. Children adapt dynamically in real-time social and sensory contexts.
sauce: Max Planck Institute
Even the smartest machines cannot match the younger mind in language learning. Researchers share new findings about how children go ahead of AI and why it is important.
It would take 92,000 years if a human learns a language that he learned at the same speed as ChatGpt. Machines can crunch large datasets at lightning speeds, but when it comes to natural language acquisition, children leave artificial intelligence in the dust.
Newly published framework Cognitive Science Trends Professor Caroline Rowland of the Max Planck Institute for Psycholinguistry, working with colleagues at the ESRC Lucid Center in the UK, presents a new framework to explain how children achieve this incredible feat.
Explosion of new technology
Scientists can observe how children interact with caregivers and the environment in unprecedented details.
However, despite the rapid growth of data collection methods, theoretical models explaining how this information is translated into a fluent language are lagging behind.
The new framework addresses this gap. A research team that integrates a wide range of evidence from computational science, linguistics, neuroscience and psychology suggests that the key to understanding how children learn language much faster than AI is not the amount of information they receive, but how they learn from it.
Children vs chatgpt: What's the difference?
Unlike machines that learn primarily from written texts and passively learn, children acquire language through active, constantly changing developmental processes driven by growing social, cognitive, and motor skills.
Children see, hear, smell, smell, hear and touch every sense of the world and build language skills. This world provides them with rich and coordinated signals from multiple senses, giving them a diverse and synchronized cue that helps them understand how language works.
And the kids don't just sit down and wait for the language to come to them. They actively explore their surroundings and continuously create new opportunities to learn.
“AI system process data…but kids really live that,” Roland points out. “Their learning is embodied, interactive, deeply embedded in social and sensory contexts. They seek experience and dynamically adapt learning accordingly. They explore objects with their hands and mouths, raw towards new, exciting toys, or point to interesting things.
Meaning beyond childhood
These insights do not only reconstruct our understanding of child development. It has a widespread impact on research in the evolution of artificial intelligence, adult language processing, and even the human language itself.
“AI researchers have been able to learn a lot from babies,” says Roland. “If you want to learn languages and humans in machines, you probably need to rethink how you design them from scratch.”
About this neurodevelopment and AI language learning research news
author: Anniek Corporaal
sauce: Max Planck Institute
contact: Anniek Corporaal – Max Planck Institute
image: This image is credited to Neuroscience News
Original research: Open access.
“The Bot's Brain: Why Toddlers Still Breaking AI in Learning Languages,” Caroline Roland et al. Cognitive Science Trends
Abstract
Brain on a Bot: Why do infants still beat AI in learning languages?
Explaining how children build language systems is a central goal of research in language acquisition and has a wide impact on language evolution, adult language processing, and artificial intelligence (AI).
Here we propose a constructivist framework for future theories construction in language acquisition.
It is based on a wide range of evidence that explains the four components of constructivism and argues that theories based on these components are suitable for explaining developmental changes.
Adopting a constructivist framework demonstrates how to provide plausible answers to old questions (e.g., how children construct linguistic representations from input) and generate new questions (e.g., how children adapt to affordances provided by different cultures and languages).
