I'm always amazed how much attention supervised learning algorithms get when Q-learning is often better suited for real-world problems that go beyond just classifying things, works closer to how humans actually learn, and is much easier to implement (requires way less code and no training data). I wonder how much of this disparity is because big companies like Google and FB who hold all the training data benefit from only talking about ML techniques that nobody can compete with them on.

At Hiome (hiome.com), we use Q-learning to learn people's habits and automatically program their smart home, and it's insanely effective. Since there's no training data required, we don't even have to violate our users' privacy to aggregate their data, so everything stays local in their home.

I believe similar techniques will get us closer to true AGI than neural nets.