“Child is 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 he do,” said Gopnik, author of the books “The Scientist in the Crib” (William Morrow, 2000) and “The Philosophical Baby” (Picador, 2010).
Scientists such as Gopnik have known a healthy newborn brain contains a lifetime’s supply of some 100 billion neurons; as a baby matures, these brain cells grow a vast network of synapses or connections (about 15,000 by the age of 2 or 3), which allow tots to learn languages and social skills, all the while figuring out how to survive and thrive in their environment.
Adults, meanwhile, tend to focus more on the goal at hand rather than letting their powers of imagination run wild. It’s this combination — goal-minded adults and open-minded children — that may be ideal for teaching computers new tricks, the researchers suspect.
“I need both blue-sky speculation and hard-nosed planning,” said Gopnik.
Gopnik and her colleagues are tracking the cognitive steps that child use to solve problems in the lab, and then turning the blueprint into computational models.
Their various experiments, whether using different-colored lollipops, spinning toys or music makers, suggest babies, toddlers and preschoolers are already testing hypotheses, estimating statistical odds, and coming to conclusions based on old and new evidence. This childlike exploratory and “probabilistic” reasoning could make computers not just smarter, but more adaptable and more human, the team says.
“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 statement. “We are hoping to make computers smarter by making them a little more like…