| So, AlphaZero doesn't mean we're closer to _general_ AI. Quite the contrary: it's a very specialised form of AI that will be very difficult to use in any different task than chess, shoggi or go. This is a very true statement and one that I think a lot of people who aren't in ML/DL, but are "worried" about AGI, miss. There is however a common thread with everyone in AI, that they tend to think of AGI as "One algorithm to rule them all." As a practitioner and AGI researcher however I think that AGI is more of a system of specialized or narrow AI tasks that can together solve all tasks. At the risk of oversimplifying and anthropomorphizing, this type of problem solving is functionally how we do it as humans. So having a corpus of solved narrow systems (discrete known rule space in the sense of AlphaGo etc...) that is "activated" by an executive function which can recognize the problem set and then pass subsets of a larger problem to the narrow solutions. Those solutions are then "backpropagated" and synthesized into the general problem solution. In that sense, I would argue that narrow solutions like AlphaGo etc... do get us closer to General AI because they grow the corpus of solution paths for the general problems. |
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.