Studies of toddlers hoist artificial intelligence out of infancy

To address recurring difficulties in artificial intelligence, such as handling uncertainty, UC Berkeley scientists have turned to a new resource: young children. The group has been utilizing a statistical method known as Bayesian probability theory to analyze the ways that children test hypotheses, detect statistical patterns and adapt to changes. “Young children are capable of solving problems that still pose a challenge for computers, such as learning languages and figuring out causal relationships,” said Tom Griffiths, director of UC Berkeley’s Computational Cognitive Science Lab. “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.