This is a good question. I think lots of funding incentive to build integrated systems (like self-driving cars, but for other domains) and enough talent pipeline to start making that stuff happen and create incremental improvements. People in general underestimate the systems engineering aspect of getting artificial intelligent agents to work in practice even in fairly limited settings like car driving.
Go is a hard game, but it is a toy problem in a way that dealing with the real world isn't. I am worried about economic incentives making it worth people's while to keep throwing money and people and iterating on real actual systems that do intelligent things in the world. Even fairly limited things at first.
Go is a hard game, but it is a toy problem in a way that dealing with the real world isn't.
What do you mean by this exactly? That real world has combinatorics problems that are much wider, or that dealing with real world does not reduce well to search in a tree of possible actions?
DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.