[yonkshi]

I think you are only thinking of supervised learning's capabilities, which I assume is the field of your AGI research? I'm working with reinforcement learning and DRL research, and RL was born to address the short coming of supervised learning. DeepMind is arguably the forerunner in RL right now, and AlphaZero is the crystalline of their RL research.

Yes, AlphaGo uses NN and other SL techniques, but the core is very much DQN based RL. No amount of SL can effectively play go and invent new moves. RL can already solve a large number of real world problems with a rather simple algorithm, from self driving cars to video games to NLP. RL can tackle all those problems with pretty much the same core algorithm. The question lies less in IF RL can solve more general AI problems, but rather HOW to solve it. From a high level view, we are having a lot of trouble with its convergence properties mathematically and its extreme sample inefficiency. This is the reason why Boston Dynamics doesn't use much RL, Waymo doesn't use much RL, simply because they can do much better with current techniques without going RL.

AlphaGo is still a major step forward regardless, because it's one of the biggest leap in RL we've taken in the recent years. It suddenly lets RL stably converge on solutions more than we could ever before. AlphaGo's contribution is more than just that it built a specialized Go bot, but rather a much more stable RL algorithm that lets us approximate non linear functions (majority of the real world applications are non linear). If I were to put my money, AI could very well be entering a new era with AlphaGo and their DQN.