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A robot that survives Day Care by crying when it's hurt

By John Published: October 18, 2011

The Dana Foundation has a fascinating update to the story of Rubi, a robot teacher's aide.

Last week was the first day of school for Rubi, the social robot being tested as a teacher's aide in a San Diego early-childhood center, and Rubi's chief developer was as nervous as a parent. After all, Rubi's predecessor lasted only a couple of hours before the 18- to 24-month-olds at the centerapparently mistaking Rubi for a super-size Mrs. Potato Headyanked off its sophisticated robotic arm and set the project back by months.

To solve the problem, researchers equipped Rubi with sensors that detected when a child might be getting too rough and triggered the robot to cry, digital tears and all. Seeing this, the children backed off, just as they typically do in interactions with one another.


Read the whole story and be sure to click on the link to Dana's report two years ago about the development of  Rubi. Readers of this blog know I've been fascinated with the emergence of a new science of learning, drawing from neuroscience and cognitive psychology. When Terry Sejnowski of the Salk Institute talks about the  science of learning, however, he's talking about something even more cutting edge. Here's what he told Dana in a Q & A.
The new science of learning describes an emerging discipline that is applying sophisticated computational models to more traditional approaches to understanding learning, with the ultimate goal of improving educational practice.The old science of learning has been largely based on animal behavior and child development. Neuroscientists have uncovered the brain mechanisms underlying several different kinds of learning, including motor learning, implicit learning, and how knowledge of facts and events is attained. We've parsed the different memory systems and are now beginning to understand how they work together synergistically to affect behavior, including how it affects children's ability to learn efficiently – or, as in some cases, not learn well.

Over the last ten years, we've also witnessed the unexpected emergence of a theory of learning, a mathematical framework that encompasses learning in machines and people. Machine learning is an area of mathematics and engineering that has been made possible by the great advances in digital computers, which have incredibly fast processors and enormously large memory capacities. This gives us, for the first time, the capability to sort through enormous amounts of data collected over decades and explore constraints on how systems could learn. Now we can use that same approach, including the very same theories and algorithms, to try to understand how biological learning fits in, and specifically, how we might improve learning in the classroom.

There has been a real convergence of a very powerful body of mathematics and theories inspired by psychology. Initially developed to solve engineering problems, the convergence of the two fields was then applied to understanding biological mechanisms in human brain. That's what we mean by the new science of learning. I think this is one of the great success stories in all of neuroscience and engineering over the last decade.

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