I don't think that approach is going to work. For any clearly bounded and delineated task, such as a game, the most optimal, lowest energy and lowest cost solution is not AGI but a custom tuned specialist solver. This is why I don't think Deep Blue or Alphago are paths towards AGI. They are just very highly advanced single-task solvers.
Now Alphago and it's implementation framework are much more sophisticated than Deep Blue. It's actually a framework for making single-task solvers, but that's all. The fact it can make more than one single-task solver doesn't making it general in the sense we mean it in the term AGI. AlphaGo didn't learn the rules of Go. It has no idea what those rules are, it's just been trained through trial and error not to break them. That's not the same thing. It's not approaching chess or Go as an intelligent thinking being, learning the rules and working out their consequences. It's like an image classifier that can identify an apple, but has no idea what an apple is, or even what things are.
To build an AGI we need a way to genuinely model and manipulate objects, concepts and decisions. What's happened in the last few decades is we've skipped past all that hard work, to land on quick solutions to specific problems. That's achieved impressive, valuable results but I don't think it's a path to AGI. We need to go back to the hard problems of “computer models of the fundamental mechanisms of thought.”[0]
[0]https://www.theatlantic.com/magazine/archive/2013/11/the-man...
Now Alphago and it's implementation framework are much more sophisticated than Deep Blue. It's actually a framework for making single-task solvers, but that's all. The fact it can make more than one single-task solver doesn't making it general in the sense we mean it in the term AGI. AlphaGo didn't learn the rules of Go. It has no idea what those rules are, it's just been trained through trial and error not to break them. That's not the same thing. It's not approaching chess or Go as an intelligent thinking being, learning the rules and working out their consequences. It's like an image classifier that can identify an apple, but has no idea what an apple is, or even what things are.
To build an AGI we need a way to genuinely model and manipulate objects, concepts and decisions. What's happened in the last few decades is we've skipped past all that hard work, to land on quick solutions to specific problems. That's achieved impressive, valuable results but I don't think it's a path to AGI. We need to go back to the hard problems of “computer models of the fundamental mechanisms of thought.”[0]
[0]https://www.theatlantic.com/magazine/archive/2013/11/the-man...