Christian Science Monitor (Dec. 8) --
How does one articulate a nebulous concept like “good” to a machine? “Machine learning is good at generating and evaluating variations,” says Ranjitha Kumar, a computer science professor at Illinois. “(But) you don’t really understand the problem definition, the constraints or the criteria for goodness until you’ve built a bunch of things and tried them out.”