If an algorithm is set to learning some policy about how to interact with the world to achieve a specific task, what is there to be learned from the algorithm's solution? For example, have new physical or biological principles governing the robustness of - or tradeoffs in locomotion strategies been learned from analysis of the learned movement patterns of the Deepmind walkers? I'm a biology PhD student and I've been wondering how my field could take advantage of advances in machine learning to move biology forward. It's one thing to be able to make predictions, but it seems to me that reinforcement learning approaches offer the potential for machines to act as scientists themselves.

In October 2017, researchers at Google DeepMind published a paper on an artificial intelligence (AI) program called AlphaGo Zero. Unlike previous incarnations of AlphaGo, this updated version mastered the game of Go through self-play alone. Talking about the achievement, lead researcher David Silver explained that AlphaGo Zero had invented "its own variants which humans don't even know about or play at the moment." And it's here that a new and exciting use for AI comes to light. Could it be that AI might teach humans about the world around us?