Exploring the mysteries of Go with AlphaGo and China’s top players
deepmind.com
deepmind.com
This makes me wonder. Maybe the human brain never computers for global optimas. It gets to a point where it's good enough and then stops. Clearly, if they play vs AI and then start improving their strategy again, the brain might have thought defeating most human is a good enough strategy.
This is also see in other games, someone who plays in "easy" difficulty or low ranked games in multi player games are stuck with "bad habits". As in they do really well in those thresholds but perform poorly when introduced to higher difficulty. But as soon as there is a need, good players rapidly adjust. (Example: In MOBA games like leauge of legend or Dota, higher ranked players adapt at a much higher rate to new emergent strategies while lower level player tend to not manage this and find things unbalanced.)
My point is, as we discover more of these `slighly better than human AI`, we'll also discover that human brain might be capable of much more. It just never saw the need to pour more resources in it till it found a healthy competition. Our only (soon to be) sentient friend, the AI.
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That sounds a lot like Herbert Simon's idea of "satisficing"[1].
Satisficing is a decision-making strategy or cognitive heuristic that entails searching through the available alternatives until an acceptability threshold is met. <snip> Simon used satisficing to explain the behavior of decision makers under circumstances in which an optimal solution cannot be determined. He maintained that many natural problems are characterized by computational intractability or a lack of information, both of which preclude the use of mathematical optimization procedures. Consequently, he observed in his Nobel Prize speech that "decision makers can satisfice either by finding optimum solutions for a simplified world, or by finding satisfactory solutions for a more realistic world. Neither approach, in general, dominates the other, and both have continued to co-exist in the world of management science.
[1] https://rjlipton.wordpress.com/2015/10/06/depth-of-satisfici...
I fully expect human ability to drastically increase in several areas when computers start finding new working patterns.
Alpha go learns just like humans it is not standing still for humans to catch up.
However, the era of everyone being able to have a super-human Go teacher should certainly make things more fun and raise the level of amateur players.
Stockfish figures out any blunders I do automatically. I just set it to "search depth 20" (which normally takes ~20 seconds per turn... although some complicated positions can be ~1+ minute per turn) and have it analyze my games. In less than 10 minutes, I have a very strong analysis over the course of an entire game.
It led me to another: you know how AI can imitate painting styles? Well, perhaps an AI could also figure out a style, by itself (i.e. given constraints, but no style to follow.) i.e. it figures out a new one! But which has a novel effect, on humans and as judged by a computer. Once we have been shown the style, humans could improve by starting to use that! We might like the effect, be able to judge that it is good, but just never saw that way before.
Imagine if AI had invented pointillism, impressionism, cubism, etc! Imagine if it figures out the next "global optimum" from putting splotches of paint down, showing human artists a new way to see. Wow.
But if the AI had brain-like features, couldn't it be its own judge? Couldn't it have as an emergent property, its own sense of aesthetics or meaning, which it could then approach using its own naive approaches. Which humans could say "Wow" to.
AI as the ultimate naive painter. What a concept!
I realize it presupposes a lot that may never come to pass.
Thank you, you are welcome. We'll get along wonderfully!
There are still positions that people evaluate correctly but computers misjudge, but those are pretty rare.
Next year: https://www.chess.com/news/view/stockfish-outlasts-nakamura-...
And computers were only getting better since then.
If I recall correctly, Nakamura used an older version of a weaker chess engine (Rybka). He also tried to force a relatively equal position (since he was behind in points anyway) which led him to lose because of risky play.
I wouldn't be surprised at all if Stockfish would lose to Stockfish + Nakamura or any of the leading chess players. These people have been using chess engines for analyses for quite a while now and certainly know their limitations.
Granted it's all conjecture since no large competitions took place, but the mere fact that no such competitions took place suggests that the outcome probably won't be pleasant for humans.
I do see your point about position evaluation though. And yes, it would be interesting to see a tournament.
It used to be the case before about 2012-2014, but now the gap is so large that a human can only make things worse.
[1] - https://qz.com/877721/the-ai-master-bested-the-worlds-top-go...
The training that AlphaGo underwent between fall of 2015 and the match against Lee Seedol in March 2016 seemed quite effective.
https://www.reddit.com/r/baduk/comments/64hmu2/watch_live_pr...
> Last year, the AlphaGo that played against Lee Sedol was v18. The Master that achieved 60-0 was v25. Which version will play this time?
> Scott Beaumont: The exact version of AlphaGo will have to be introduced by the experts working on the project, but it will be the newest version. Defeating Ke Jie is no easy task, and AlphaGo has been continually improving. Also, we hope to continually improve Go as an art, through the wisdom of the players and AI in cooperation.
(Magist/Master was the handle under which AlphaGo played 60 online blitz games last December: https://en.wikipedia.org/wiki/AlphaGo#Unofficial_online_matc... )
I think that for the match with Lee Sedol it was offloading computation to (some form of) Google Cloud no?
Also (if this is optimized) the information exchange back and forth should be minimum (board position and next move selected by AlphaGo takes very little space)
BTW, it seems we are experiencing here in HN some kind of AI fundamentalism since the downvote rush doesn't correlate with this normal comment. I am not arguing against the downvotes, just that sych reactionary behavior was not expected.
[1] https://en.m.wikipedia.org/wiki/Advanced_Chess
[2] http://m.dw.com/en/world-chess-champion-magnus-carlsen-the-c...
I don't understand; he would get destroyed. Do you mean just to see what it would look like?
Let's wait until the end of May but maybe we're already at that point with go as well.
(We don't know yet if I can win a running race against a Ferrari, still I'm pretty sure about the outcome.)
Plus, he can play against an engine any time he wants. If he could beat them in some magically surprising way, would it make any sense for him to be keeping this amazing skill a secret from the world? No.
Sources?
https://www.google.com.au/search?q=chess+engine+elo+rating
And we don't know "with 100% certainty", but we can calculate a projected win/loss probability using those ELO ratings[1]
A 3300 rated chess engine vs Carlsen at 2850 gives a 93% win probability to the chess computer.
That's roughly the same probability of an International Master raned player (ELO 2300) beating Carlsen.
The top ranked engine is Stockfish, rated 3500+
As far as Im aware no engines compete against humans regularly enough to get an ELO score (presumably because watching a computer thrash human players for a few hundred games isnt that interesting).
There's little doubt they are much stronger than humans though. There are enough online games that if this wasn't so it would show up.
Just because it was not calibrated with humans does not mean it is not an "Elo" system.
https://en.m.wikipedia.org/wiki/Human–computer_chess_matches
He may only win if computer starts with a serious handicap.
http://support.stockfishchess.org/discussions/questions/865-...