Global Technology - August 2015

Comments Off on Global Technology - August 2015
Global Technology - August 2015 LoadingADD TO FAVORITES

Recently, at the International Conference on Robotics and Automation in Seattle, a team of researchers from the University of California at Berkeley demonstrated algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn. This represents a major milestone in the field of artificial intelligence.

The team had a robot complete various tasks, like putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more, without preprogrammed details about its surroundings.

The major implication is that when a robot is faced with something new, humans won't have to reprogram it. The exact same software, which encodes how the robot can learn, was used to allow the robot to learn all the different tasks the researchers gave it.

Most robotic applications are in controlled environments, where objects are in predictable positions. The challenge of putting robots into real-life settings, like homes or offices, is that those environments are constantly changing. The robot must be able to perceive and adapt to its surroundings.

To achieve this, the UC Berkeley researchers turned to a new branch of artificial intelligence known as "deep learning," which is loosely inspired by the neural circuitry of the human brain when it perceives and interacts with the world.

For all our versatility, humans are not born with a repertoire of behaviors that can be deployed like a Swiss army knife, and we do not need to be programmed. Instead, we learn new skills over the course of our lives from experience and from other humans.

This learning process is so deeply rooted in our nervous system that we cannot even communicate to another person precisely how the resulting skill should be executed. We can at best hope to offer pointers and guidance as they learn it on their own.

In the world of artificial intelligence, deep learning programs create "neural nets" in which layers of artificial neurons process overlapping raw sensory data, such as sound waves or image pixels. This helps the robot recognize patterns and categories among the data it is receiving.

In the experiments, the UC Berkeley researchers worked with a Willow Garage...

To continue reading, become a paid subscriber for full access.
Already a Business Briefings subscriber? Login for full access now.

Subscribe for as low as $135/year

  • Get 12 months of Business Briefings that will impact your business and your life
  • Gain access to the entire Business Briefings Research Library
  • Optional Business Briefings monthly CDs in addition to your On-Line access
  • If you do not like what you see, you can cancel anytime and receive a 100% pro-rata refund