[kchamplewski]

> Their “general learning” tech doesn’t even generalize to barely modified variants of the original games it has claimed to master. I call bullshit.

But the point I was making is precisely that the "general learning" tech is in fact somewhat general. AlphaGo and certainly AlphaZero's learning tech generalises to Go, chess, and a few other games. That's relatively general in the domain of board games, in my humble opinion.

The reason this isn't close to AGI is because it's not the agent doing the learning, and so while a relatively general learning algorithm produces the agent, the agent itself is not general even in the field of board games.

[mindgam3]

You appear to be completely missing the point of my root comment, which is that AlphaGo’s tech isn’t nearly as general as it’s made out to be, even if you stick to Go.

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board.

It doesn’t even generalize to the same game with a different board shape. Whereas a human Go master could easily do so.

DeepMind is essentially hacking the common usage of the word “general” in order so that they can make claims about “general” intelligence. And it’s working!

[kchamplewski]

But the training process does generalise. The same training process produces an agent that works on a 19x19 board, or a standard Go board, or even a game of chess.

How is that not general? Sure it doesn't work for all problems but in the domain of board games it definitely feels very general.

The agent the training algorithm produces may not be general, but out of what I've read I've only ever seen DeepMind claim generality of the learning algorithm, not the agent.