Baby brains bring smarter computers closer to reality
Researchers are tapping the cognitive smarts of babies, toddlers and preschoolers in their bid to facilitate computers to think more like humans.
Washington: Researchers are tapping the cognitive smarts of babies, toddlers and preschoolers in their bid to facilitate computers to think more like humans.
If replicated in machines, the computational models based on baby brainpower could give a major boost to artificial intelligence, historically has had difficulty handling nuances and uncertainty, UC Berkeley researchers said.
“Children are the greatest learning machines in the universe. Imagine if computers could learn as much and as quickly as they do,” said Alison Gopnik a developmental psychologist at UC Berkeley and author of The Scientist in the Crib and The Philosophical Baby.”
In a wide range of experiments involving lollipops, flashing and spinning toys, and music makers, among other props, UC Berkeley researchers are finding that children – at younger and younger ages – are testing hypotheses, detecting statistical patterns and drawing conclusions while constantly adapting 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.
“We are hoping to make computers smarter by making them a little more like children.”
For example, researchers said, computers programmed with kids’ cognitive smarts could interact more intelligently and responsively with humans in applications such as computer tutoring programs and phone-answering robots.
And that’s not all.
“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.
He is applying a statistical method known as Bayesian probability theory to translate the calculations that children make during learning tasks into computational models.
This spring, to consolidate their growing body of work on infant, toddler and preschooler cognition, Gopnik, Griffiths and other UC Berkeley psychologists, computer scientists and philosophers will launch a multidisciplinary centre at the campus’s Institute of Human Development to pursue this line of research.
A growing body of child cognition research at UC Berkeley suggests that parents and educators put aside the flash cards, electronic learning games and rote-memory tasks and set kids free to discover and investigate.
“Spontaneous and ‘pretend play’ is just as important as reading and writing drills,” Gopnik said.Of all the primates, Gopnik said, humans have the longest childhoods, and this extended period of nurturing, learning and exploration is key to human survival.
The healthy newborn brain contains a lifetime’s supply of some 100 billion neurons which, as the baby matures, grow a vast network of synapses or neural connections – about 15,000 by the age of 2 or 3 – that enable children to learn languages, become socialized and figure out how to survive and thrive in their environment.
Adults, meanwhile, stop using their powers of imagination and hypothetical reasoning as they focus on what is most relevant to their goals, Gopnik said. The combination of goal-minded adults and open-minded children is ideal for teaching computers new tricks.
“We need both blue-sky speculation and hard-nosed planning,” Gopnik said.
Researchers aim to achieve this symbiosis by tracking and making computational models of the cognitive steps that children take to solve problems in the following and other experiments.