Early last year, a computer achieved world-class performance in the game Go—years before most people believed such a feat would be possible.
That’s impressive, but our ambitions should be set higher. Computer science could help provide what the world critically needs: tools that enable all of us to reach beyond what we thought we were capable of. Reinforcement learning—an integral part of the Go success—can accelerate that process (see “10 Breakthrough Technologies: Reinforcement Learning”).
Reinforcement learning is a way of making a computer learn through experience to make a series of decisions that yield positive outcomes—even without any prior knowledge of how its actions will affect its immediate environment. A software-based tutor, for example, would alter its activities in response to how students perform on tests after using it.
The main challenge in creating artificial teaching agents using reinforcement learning is that there may not always be enough, or the right kind of data. That is the problem Emma Brunskill and her team at Stanford University are working to solve by developing algorithms and statistical techniques to help computers make more informed decisions with less data. Of course, allowing humans to step in and augment the work of the computer can expand the possibilities in curriculum and teaching methods. “Such human-computer collaborations could help students to learn using approaches we can’t yet imagine,” writes Brunskill.