Researchers are developing computational models based on “baby brainpower” or cognition exhibited in babies and very young children in hopes to boost artificial intelligence that have traditionally had difficulty handling nuances and uncertainty.

"Children are the greatest learning machines in the universe," Alison Gopnik, a developmental psychologist at the University of California at Berkeley, said in a statement. "Imagine if computers could learn as much and as quickly as they do.”

Researchers have previously known that a newborn brain contains a lifetime's supply of some 100 billion neurons, and as the baby develops and matures, these brain cells form a vast network of synapses or connections, totaling to about 15,000 by the age of two or three. These new connections allow young children to learn languages and social skills, while simultaneously learning how to survive and thrive in their environment.

Unlike adults who focus more on information that is most applicable to their goals, the minds of children are more flexible and driven by imagination and endless possibilities, researchers said, which is why baby cognition may be the most ideal for teaching computers new tricks.

"We need both blue-sky speculation and hard-nosed planning," Gopnik said, and her and her research team plan on building a computational model after the blueprint of cognitive steps children use to solve problems in laboratory experiments.

Previous experiments using different-colored lollipops, spinning toys or music makers, suggest that babies, toddlers and preschoolers are able to accurately estimate statistical odds and come to conclusions based on old and new evidence.

If researchers are able to capture the essence of childlike exploratory and "probabilistic" reasoning, computers will not only become smarter, but also more adaptable and more human, according to the scientists.

"Young children are capable of solving problems that still pose a challenge for computers, such as learning languages and figuring out causal relationships," Tom Griffiths, director of UC Berkeley's Computational Cognitive Science Lab, said in a news release. "We are hoping to make computers smarter by making them a little more like children."

Machines or computers programmed with the cognitive intellect exhibited in children would be able to interact more intelligently and responsively with humans in a variety of applications like computer tutoring programs and phone-answering robots.

"Your computer could be able to discover causal relationships, ranging from simple cases such as recognizing that you work more slowly when you haven't had coffee, to complex ones such as identifying which genes cause greater susceptibility to diseases," said Griffiths referring to the Bayesian probability theory of statistical method in children that would enable researchers to translate the calculations children make during learning tasks.

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