Comment by jballanc | Hacker News Reader

Comment by jballanc | Hacker News Reader

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Well, that's where AlphaGo and the progress in Go AI that it represents is so exciting! The game of Go is so fluid with such a huge number of possible positions that players tend to adopt certain styles of play en masse. I've heard it said that you can identify a Go player's mentor or "house" just by the style of play they use.

This has also resulted in larger shifts in playing style over time. Studying very old (and I mean very old...700+ years old) games can be entertaining and even educational in the abstract, but you won't want to directly adopt the style of play because the game has evolved.

It's already been mentioned a couple of times that AlphaGo almost certainly represents just such a shift. Top players will learn from it, and I'd even be willing to bet they will beat it with some regularity once they do!

Ultimately, what sets apart Go geniuses is their ability to play creatively in the face of seemingly insurmountable challenges. So the big question is how "creative" AlphaGo can be. Is it merely synthesizing strong play from known positions? Can it introduce novel strategies? And if it does, will it be able to adjust as other Go masters adjust to it and bring their own brand of creativity to play?

To answer your original question, this very well could introduce a new era of more aggressive play to the world of Go. Only time will tell...

AlphaGo will learn from any new styles and apply them effectively without mistakes from fatigue or inattention.

This incarnation of AI is not creative, it wont generate new play styles, that is still the domain of top human players for now. But it will ruthlessly learn and adopt any new and improved strategies. That's really the point to take away from its success so far.

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

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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