In the future, according to some scientists, robots will turn up as nannies, teachers and as dogs that keep track of your health. Researchers have also invented a robot juror that can help decide whether or not an offender should be given the death penalty.

Celeste Biever writes in New Scientist about a robot dog that monitors your health by measuring how much you eat, reminding you to stick to your diet and keeping track of how much you exercise. The robot’s inventors hope it will be a major weapon in the war against obesity. The robot won’t talk, but it give you a response every time you ask the question, “How am I?” If you’ve kept to your diet, the robot, called Aibo, will jump up and down for joy, wag his tail, play fast music and flash his lights. If you’ve gone off your diet and exercise program, he’ll move slowly and play sad music.

A robot known as RUBI teaches at the Early Childhood Education Center at the University of California at San Diego. Three-foot-tall RUBI is soft and plump, with human features. She is mounted on four wheels and has two cameras for eyes, as well as a third for peripheral vision, mounted on an antenna on her back, in order to keep track of student misbehavior going on behind her. Successful human teachers manage to have the same kind of peripheral vision, but without the antenna.

RUBI teaches the children songs and, with the touch-screen on her stomach, lets them play interactive games that teach them colors and shapes. She is one possible solution for the teacher shortage.

In order to study the death penalty, criminologist Dee Wood Harper helped create an artificial neural network (ANN), which is a computing system that works the way our biological nervous system processes information. ANNs are capable of learning on their own or by example through a process that involves adjustments to the connections that exist between the neurons.

Harper and computer scientist Stamos Karamouzis reconstructed the profiles of more than 1,300 death row inmates from a national population by using simple attributes such as the inmates’ race, sex, age and highest year of education completed at the time of first imprisonment for the capital offense. Harper says, “We took a thousand of those profiles and used them to ?train? the network.”

“We then tested the ANN using 300 profiles that the network never witnessed before,” says Karamouzis. “The network was capable of correctly predicting execution/non-execution at a rate higher than 90%.”

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