AI Isn’t Smarter Than a Baby—Yet
Babies are tremendous learning machines, and key advances for AI may soon be found in the architecture of their little brains.
If you think an artificial intelligence model running on thousands of cutting-edge computer chips is smart, allow me to introduce you to the concept of a 1-year-old.
OK, so babies might not be able to write computer programs, solve advanced math problems, or debate philosophical ideas. But unlike today’s AI models, which consume an ocean’s worth of training data and as much energy as a small country , babies learn to make sense of the world with amazing efficiency. They identify new objects after seeing them once or twice, and they learn through fleeting observation and physical interaction.
When it comes to improving AI, babies—and the architecture of their brains—might hold crucial insights. Building a more baby-like version of AI could make frontier models less costly and less energy intensive, and it might also be valuable if AI-powered robots are to learn about their environments in a more natural way.
To explore this bold new frontier, researchers at Meta, Stanford University, the University of Tokyo, and France’s École Normale Supérieure developed a new test that highlights the learning skills of babies and pushes AI researchers to design algorithms that match them.
The EgoBabyVLM Challenge judges how well vision language models, or VLMs, which learn from both text and imagery, can make sense of the world as a baby sees it. It requires a model to describe the world after ingesting about a thousand hours of video collected from cameras strapped to the heads of infants and toddlers. (Yes, really.)
It turns out that the cutting-edge models fail miserably when fed this realistic and messy footage, which suggests there may be something different about the design of the baby brain that enables it to learn so rapidly from so little information.
