A new competition heralds what is likely to become the future of cybersecurity and cyberwarfare, with offensive and defensive AI algorithms doing battle.

The contest, which will play out over the next five months, is run by Kaggle, a platform for data science competitions. It will pit researchers’ algorithms against one another in attempts to confuse and trick each other, the hope being that this combat will yield insights into how to harden machine-learning systems against future attacks.

“It’s a brilliant idea to catalyze research into both fooling deep neural networks and designing deep neural networks that cannot be fooled,” says Jeff Clune, an assistant professor at the University of Wyoming who studies the limits of machine learning.

The contest will have three components. One challenge will involve simply trying to confuse a machine-learning system so that it doesn’t work properly. Another will involve trying to force a system to classify something incorrectly. And a third will involve developing the most robust defenses. The results will be presented at a major AI conference later this year.

Machine learning, and deep learning in particular, is rapidly becoming an indispensable tool in many industries. The technology involves feeding data into a special kind of computer program, specifying a particular outcome, and having a machine develop its own algorithm to achieve the outcome. Deep learning does this by tweaking the parameters of a huge, interconnected web of mathematically simulated neurons.