In what some may see as a role reversal, scientists are studying the way children learn in an effort to improve artificial intelligence systems used in computers.
Although computers are highly efficient at sorting and aggregating information, current information systems have problems handling nebulous and conflicting scenarios.
“Children are the greatest learning machines in the universe. Imagine if computers could learn as much and as quickly as they do,” said Dr. Alison Gopnik, a developmental psychologist at UC Berkeley.
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 Dr. 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.
“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.
Griffiths is attempting to use a statistical method known as Bayesian probability theory to translate the calculations that children make during learning tasks into computational models.
As an outgrowth of this research, the Berkeley scientists recommend for parents to return to the basics when helping their child. Scientists recommend 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, each of which goes on to 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.
Gopnik is studying the “golden age of pretending,” which typically happens between ages 2 and 5, when children create and inhabit alternate universes. In one of her experiments, preschoolers sing “Happy Birthday” whenever a toy monkey appears and a music player is switched on.
When the music player is suddenly removed, preschoolers swiftly adapt to the change by using a wooden block to replace the music player so the fun game can continue.
Earlier experiments by Gopnik — including one in which she makes facial expressions while tasting different kinds of foods to see if toddlers can pick up on her preferences — challenge common assumptions that young children are self-centered and lack empathy, said Gopnik, and indicate that, at an early age, they can place themselves in other people’s shoes.
Scientists have also discovered that babies do most of their learning as they “play.” In some games, children would follow a pattern but then as options became apparent, they were able to look at new possibilities — a trait investigators say would be useful for computers — to look at new possibilities for cause and effect based on changing odds.
Overall, the UC Berkeley researchers say they will apply what they have learned from the exploratory and “probabilistic” reasoning demonstrated by the youngsters in these and other experiments to make computers smarter, more adaptable — and more human.
Source: UC Berkeley