New Training Method Enables AIs to Learn Directly from Human-Defined Rules ENGINEERING.com

Your smartphone may soon be able to give you an honest answer, thanks to a new machine learning algorithm designed by engineering researchers at the University of Toronto. The researchers trained their algorithm to identify people's hair in photographs--a much more challenging task for computers than it is for humans. The team designed an algorithm that learns directly from human instructions, rather than an existing set of examples, and outperformed conventional methods of training neural networks by 160 percent. More surprisingly, their algorithm also outperformed its own training by nine percent--it learned to recognize hair in pictures with greater reliability than that enabled by the training, marking a significant leap forward for artificial intelligence. "Our algorithm learned to correctly classify difficult, borderline cases--distinguishing the texture of hair versus the texture of the background," said researcher Parham Aarabi.