gowan wrote:
This is perhaps a bit OT for this thread but I find it annoying to see discussions about "AlphaGo" or "Master" as if they were people. Of course they aren't people but what are they?
I don't mind the anthropomorphising of AlphaGo (and indeed do it myself): it makes language use more natural/concise. For example I prefer saying "Master likes to press if you ignore its low approach to 3-4" to "The current iteration of the neural network weight coefficients give rise to a strong likelihood to play the press ...".
gowan wrote:
And it isn't perfect.
Indeed. I don't think I or anyone claims it is. If someone takes me using a phrase talking about "winning probability" to mean an objective win probability based on perfect play, rather than "AlphaGo's estimation of winning probability (which is good but not perfect, though actually is more like a score than an actual probability)" then again that's making concise language.
gowan wrote:
Can it determine what is the best first move in a go game? I doubt it.
Determine as in provide a rigourous mathematical proof? No. Just "This very strong player likes [there I go anthropomorphising again] to start here". I did find it interesting though when Michael Redmond asked about the first moves David Silver said AlphaGo likes to start with the standard human corner moves on the 3rd and 4th lines, not crazy centre moves. I wonder how much of that is a leftover bias from the human training, versus it learning on its own what moves work best (what percentage of self-play games didn't start in the corners, I suspect very low).
gowan wrote:
And there seems to be some question about its endgame performance.
The much talked about "problem" of losing points when it's winning doesn't concern me much, because it is just following its objective function of maximising win probability. When Diana Koszegi 1p from BIBA raised this issue on facebook I replied:
Diana Koszegi wrote:
It's really hard to believe that playing bad in the end game gives Alphago a better percentage to win the game....
So actually it feels like he was set to win by 1.5 point or half a point.... (well, as Black, maybe 0.5 or 2.5 since they use Chinese rules..)
Actually Lee Sedol just commented on Baduk TV, that he feels like they set this up on purpose to make amateur players believe that it was a close game
I wrote:
I can believe about the giving up points to increase win percentage and don't think it's a trick: it's a natural consequence of Monte-Carlo tree search. For example with the team game and that unnecessary capture of the 3 stones at the end then imagine in the game tree with the variation where it cuts off the 2-2 then in some playouts of plausible moves from the policy network it won't make the throw in and thus lose the semeai on the left and lose the game. But with its move there's no way that loses. As it's a probabilistic system this makes it choose the safer but point-losing route. That's not to say they couldn't put in effort to fix this perceived problem, either by bolting on some "give extra komi and find move that still wins" approach or remaking the whole program with a different objective function, but it's likely that will have unintended consequences (as neural networks are essentially black boxes of magic) and make the program weaker in other areas, and be a lot of work. So unless AlphaGo actually loses a game following slack endgame in which it misjudged the status of something (like DeepZen vs Park Junghwan) it's simply not a priority to change this aspect of AlphaGo.
gowan wrote:
Probably there are questionable things about its play in the middlegame, too.
Yup, let's try to find them! It will be hard though as I suspect a lot will come down to positional judgement, which it seems to be better at than top humans. Guo Juan did recommend reviewing pro games with the aim of finding their mistakes as a way to focus study. It might sound arrogant, but you have each player's opponent to help you.
gowan wrote:
Alphago can't explain why its moves are good and perhaps we should be cautious about imitating its moves, just as we advise weaker players to play moves they understand.
Yes, imitating moves you don't understand can make you lose. But experimenting and losing and learning is also a good way to improve in the long run. (If I just wanted to win rather than have fun and play interesting games I'd always play the Kobayashi opening as it seems to be very effective against low-mid dans, but I don't as I find it overused and deathly dull). That pros are willing to experiment with its new ideas like the early 3-3 invasions, even if they sometimes don't work out well, is a good thing to me for adding creativity and variety (though perhaps you could argue such imitation is not creative if they only play moves given the seal of approval by the authority of AlphaGo, though I think we are also seeing more willingness to experiment).