[mindgam3]

> They are designed to play one combinatorial game really well.

Maybe, but they’re certainly not described that way by whoever is in charge of publishing DeepMind’s research:

“A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play”

https://deepmind.com/research/publications/general-reinforce...

[kchamplewski]

I think it's quite important to look at the distinction between the actual agent in play and the learning algorithm used.

The learning algorithm AlphaGo uses is somewhat general, and can handle different games (e.g. you can put chess or Go through the algorithm and it functions well for either).

The output of this algorithm, however, is a specialised agent. The agent is not general. If I create a chess agent and give it Go or chess with different rules, it will perform very poorly.

Creating general learning algorithms is arguably a somewhat easier task than creating a general agent, since learning algorithms are typically run for a long time while an agent often has to make time constrained decisions.

The holy grail of AGI is to make the learning algorithm and the agent the same thing, and have them be general. Then you have an agent which can rapidly adapt to its environment and self-modify as needed. We are still a long way off a system that would do this in terms of current research.

[mindgam3]

The distinction you’re making between agent and algorithm is meaningless for the point I was trying to make, which is that the only connection between this DeepMind research (agent, algorithm, whatever) and AGI have in common is the word “general”.

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

[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.