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Was it AlphaGo losing the game, or Lee Sedol winning it?
by _snydly 11y ago
Was it AlphaGo losing the game, or Lee Sedol winning it?
- shadowmint 11y agoGiven the number of errors by the AlphaGo in the last 10 minutes, probably the former.
- nanofortnight 11y agoThose moves look like AlphaGo had calculated a loss way ahead of time.
- calvins 11y agoErrors when already lost don't mean much.
- greenyoda 11y agoI think Lee Sedol won the game earlier by destroying AlphaGo's territory in the center. The commentator (Michael Redmond) was quite impressed with what he did there.
- hoonah 11y agoGiven the genius of LSD, probably the latter.
- techdragon 11y agoSometimes its just not possible to stop the snowball rolling down hill. You cant always turn a retreat into an advance or flanking move, sometimes the first step backwards just turns into a full on rout. I got the impression (possibly incorrectly) that AlphaGo was trying to throw curve-balls and be 'unexpected' in a way that might have 'forced' a mistake it could exploit.
- Crazywater 11y agoAccording to the head of DeepMind, AlphaGo made a mistake in evaluating move 79: https://twitter.com/demishassabis/status/708928006400581632 https://twitter.com/demishassabis/status/708928006400581632
- HardyLeung 11y agoLee Sedol winning, and keeping his cool and not make any mistakes. AlphaGo, on the other hand, went bonker especially towards the end but it got into bad territory not because of silly mistakes but brilliant play by Lee Sedol. Could it possibly be that both of the mistakes were bugs? Perhaps it suggested a non-sensical position such as (25.23, 13.15), and it was snapped to (19, 13) :D
- emcq 11y agoI dont think they were bugs in a traditional sense. I think AlphaGo picked moves to try and maximize the probability of winning, and at some point that was only by the opponent making a suboptimal response. I remember reading somewhere most of it's training data is from amateur games. The model doesn't have a prior that AlphaGo is playing a professional who won't make a bad response. It probably would have resigned a lot earlier with that prior :) Another thing to keep in mind is that AlphaGo has no "memory", so every turn it looks at the board fresh. This means if the probabilities are very close you could have it jump around a bit either due to numerical noise from floating point calculations, model errors, or just tiny differences in probability making the behavior appear erratic and quick to change "strategy".
- eru 11y ago> Perhaps it suggested a non-sensical position such as (25.23, 13.15), and it was snapped to (19, 13) :D Alphago doesn't work like that..
- moistgorilla 11y agoHonestly, I think that is a meaningless distinction unless AlphaGo actually broke down in the middle of the match