The arrangement declared in the account Science is a computer archetypal "that captures humans' different adeptness to apprentice new concepts from a individual example," the abstraction said.
"Though the archetypal is alone able of acquirements handwritten characters from alphabets, the access basal it could be broadened to accept applications for added symbol-based systems, like gestures, ball moves, and the words of announced and active languages."
Joshua Tenenbaum, a abettor at the Massachusetts Institute for Technology (MIT), said he capital to body a apparatus that could actor the brainy abilities of adolescent children.
"Before they get to kindergarten, accouchement apprentice to admit new concepts from just a individual example, and can even brainstorm new examples they haven't seen," said Tenenbaum.
"We are still far from architecture machines as acute as a animal child, but this is the aboriginal time we accept had a apparatus able to apprentice and use a ample chic of real-world concepts -- even simple beheld concepts such as handwritten characters -- in means that are harder to acquaint afar from humans."
The arrangement is a alleged a "Bayesian Program Learning" (BPL) framework, breadth concepts are represented as simple computer programs.
Researchers showed that the archetypal could use "knowledge from antecedent concepts to acceleration acquirements on new concepts," such as architecture on ability of the Latin alphabet to apprentice belletrist in the Greek alphabet.
"The authors activated their archetypal to over 1,600 types of handwritten characters in 50 of the world's autograph systems, including Sanskrit, Tibetan, Gujarati, Glagolitic -- and even invented characters such as those from the television alternation Futurama," said the study.
Since bodies crave actual little abstracts to apprentice a new concept, the analysis could advance to new advances in bogus intelligence, the abstraction authors said.
"It has been actual difficult to body machines that crave as little abstracts as bodies if acquirements a new concept," said Ruslan Salakhutdinov, an abettor assistant of computer science at the University of Toronto.
"Replicating these abilities is an agitative breadth of analysis abutting apparatus learning, statistics, computer vision, and cerebral science."
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