Imagine if every time you learned something new, you completely forgot how to do a thing you'd already learned.
Finally figured out that taxi-hailing whistle? Now you can't tie your shoes anymore. Learn how to moonwalk; forget how to play the violin. Humans do forget skills, of course, but it usually happens gradually.
Computers forget what they know more dramatically. Learning cannibalizes knowledge. As soon as a new skill is learned, old skills are crowded out. It's a problem computer scientists call "catastrophic forgetting." And it happens because computer brains often rewire themselves—forging new and different connections across neural pathways—every time they learn. This makes it hard for a computer to retain old lessons, but also to learn tasks that require a sequence of steps.
"Researchers will need to solve this problem of catastrophic forgetting for us to get anywhere in terms of producing artificially intelligent computers and robots," said Jeff Clune, an assistant professor of computer science at the University of Wyoming. "Until we do, machines will be mostly one-trick ponies."
Catastrophic forgetting also stands in the way of one of the long-standing goals for artificial intelligence: to create computers that can compartmentalize different skills in order to solve diverse problems.