The cynic in me would guess it's because he's not a big fan of neural nets, and prefers a more symbolic/statistical machine learning approach. Personally, I do

The cynic in me would guess it's because he's not a big fan of neural nets, and prefers a more symbolic/statistical machine learning approach. Personally, I don't think this is a dead end. Reinforcement learning with an effective method of representation learning is pretty much all you need for a general AI. Here, deep neural nets are able to learn how to represent the massive state space for vision quite well, but there's a bit of a mismatch in games where actions have to be taken in a specific order (e.g., where extended pathfinding is required) because for reinforcement learning to work well, your features need to capture that sort of temporally extended state information. That's still a hard problem, but not insurmountable, and there's already strides in that direction. From the RL side, there's things like option models for taking series of actions, and from the deep learning side there's things like long short term memory and DeepMind's own neural Turing machines. Knowing the group at DeepMind, they'll be able to crack it, and I think Mason is being entirely too pessimistic about the timeline in any event. Fifty years to control a drone?