But I feel that contrarians, such as myself, have an ethical commitment to young people to voice our doubts and criticisms, so that they can avoid making a long journey down a career/research path that leads to a dead end. That being said, I think this project leads in a very unpromising direction. Here are some reasons:
1. Games aren't a good testbed for studying intelligence. In a game the main challenge is to map an input percept to an output action (am I drifting off the side of the road? Okay swerve right). The real challenge of intelligence is to find hidden abstractions and patterns in large quantities of mostly undifferentiated data (language, vision, and science all share this goal).
2. This platform is not going to help "democratize" AI. To succeed in one of these domains, contestants will need to use VAST amounts of computing power to simulate many games and to train their DL and/or RL algos. DeepMind and others will sufficient CPU/GPU power will almost certainly dominate in all of these settings.
3. Deep Learning, as it is practiced, isn't intellectually deep. With a few exceptions, there is nothing comparable to the great discoveries of physics, not even anything comparable to the big ideas of previous AI work (A*, belief propagation, VC theory, MaxEnt, boosting, etc). Progress in DL mostly comes from architecture hacking: tweak the network setup, run the training algo, and see if we get a better result. The apparent success of DL doesn't depend on any special scientific insight, but on the fact that DL algos can run on the GPU. That, combined with the fact that, except for the GPU, Moore's Law broke down roughly 10 years ago, means that relative to everything else, DL looks amazingly successful - because all other approaches to AI are frozen in time in terms of computing power.