This technology could be deployed in a range of ways, from industrial uses to consumer robots in the home. Nathan Benaich, a venture capitalist at Playfair Capital in London who focuses on AI, sees potential applications on factory assembly lines, in warehouses for delivery and logistics, and as components of household robot assistants.

Though the CMU experiment is ambitious, there are some caveats. The robot is limited to a table-top environment, which means that only objects small enough to fit on a table-top were included. These included things such as atomizers, staplers, and plush toys. The research has also not yet been peer-reviewed (it’s available as a pre-print currently), although the team has submitted it to be presented at the European Conference on Computer Vision in October.

Google’s research division has devised a similar experiment with robotic arms, with the aim of teaching the robots “hand-eye coordination.” The Google robots can only perform one gesture, grasping, but the lab set up 14 arms to collect lots of grasping data, feeding that information into a convolutional neural network to determine the optimal way for robots to pick things up.

Unsupervised learning

Experiments like the CMU researchers’ and Googles’ are ambitious attempts to address two problems simultaneously: the data-collection problem, which is needed to train AIs; and improvements in ”unsupervised learning,”which allows AIs to make sense of any data without training. Instead of relying on manually assembled data-sets fed in by humans, the idea is to create robots that can obtain the data, and then feed them to AIs.

“The overall idea of robots learning from continuous interaction is a very powerful one,” said Sergey Levine, a professor at the University of Washington who works on AI projects with Google. ”Robots collect their own data, and in principle they can collect as much of it as they need, so learning from continuous active interaction has the potentially to enable tremendous progress in machine perception, autonomous decision making, and robotics.”

Robots put in the service of collecting data for AIs, then, could help artificial intelligences learn in a more human way.

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