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Comment by Joof | Hacker News Reader

AlphaGo mostly plays against itself, meaning it learns in a very separate environment. It certainly might come up with novel strategies.

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I suppose that's possible. However Demis Hassabis has said many people have noted that AlphaGo makes human-like moves. He commented that it made sense since AlphaGo taught itself from the games of human players.

It could also be that human-looking moves are already close to the best possible. Neither AlphaGo nor human players can play god's hand, but they're approaching the same location.

Is Go-Space understood well enough to know either way?

The OMG-AI people claim that AGI would be dangerous because it would reliably innovate in new spaces and out-predict humans.

So a true super-AGI would make go moves that were unexpected and incomprehensible with some percentage of misleading fake-outs, but it would still win most or all of the time.

If the human exploration of Go-Space is close to the god's hand bounds, this can't be true.

My intuition (and it's really only just that) is that Go space is large enough that AI would be able to outplay humans while still not even beginning to approach "perfect" play. If so, then I would also expect that humans should be able to follow the lead of AI into new areas of Go space, and outplay the AI (at least until the AI has a chance to learn and catch up).

We'll know if this is the case in a couple of years, if the competition between human and AI goes back-and-forth (unlike Chess, where after AI was good enough to beat humans, it could do so reliably).

Either way, it's interesting to note that AlphaGo had literally thousands of games to learn from to find weaknesses in human play, but Lee Sedol seems to have only needed 3 before he was able to find weaknesses in AlphaGo's play.

> Either way, it's interesting to note that AlphaGo had literally thousands of games to learn from to find weaknesses in human play, but Lee Sedol seems to have only needed 3 before he was able to find weaknesses in AlphaGo's play.

To be fair we can't know how many games Sodol played in his own head to figure this out.

Yes, the starting kernel of its learning this time was a collection of human games. This has caused the Policy Network (which says "given this board state, these moves are worth investigating in more detail") to be biased towards more human-like moves.

But they're already working on a new version of AlphaGo which isn't trained on any human data at all. It starts by making truly random moves and improves from there. This will require much more processing time and probably an order of magnitude more "self-play", but it will probably result in truly novel strategies that aren't part of the current human metagame.

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