“A computer algorithm equipped with a form of artificial curiosity can learn to solve tricky problems even when it isn’t immediately clear what actions might help it reach this goal. Researchers at the University of California, Berkeley, developed an “intrinsic curiosity model” to make their learning algorithm work even when there isn’t a strong feedback signal. The curiosity model developed by this team sees the AI software controlling a virtual agent in a video game seek to maximize its understanding of its environment and especially aspects of that environment that affect it. There have been previous efforts to give AI agents curiosity, but these have tended to work in a more simplistic way. The trick may help address a shortcoming of today’s most powerful machine-learning techniques, and it could point to ways of making machines better at solving real-world problems.”