401–410 of 471 posts
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Perhaps still better than my friend, who thinks suffering is what makes humans special. Discussions with him are, well, interesting.
Maybe introduce your friend to buddhist ideas of ending suffering via meditation. Might be another interesting discussion.
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Sure, but AlphaGo will probably evolve much faster. In a few years it will run on much smaller devices, as happened to chess programs.
Exactly - the watts comparison is a bad one. Stockfish running on an iPhone (~5 watts?) can play world class chess.
1920 CPUs (a 4-core haswell from 2013 is around 170Gflops). 280 GPUs (previous gen Nvidia K series peaks at around 5200GFLOPS). That's 1,782,400Gflops or around 150,000x more processing power. If they were running latest-gen hardware, then the would be closer to 200,000x faster.
Given that Moore's law is slowing down and the size of the system, we're a long way from considering putting that in a smartphone.
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> Frankly many pre-modern, pre-civilizarion humans were assholes. While it's somewhat news to me, I'm very glad to hear we've moved past that.
I hope that was irony.
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He made $30k for losing that game, so I don't feel too sorry for him.
If it really was about money, I don't think he would have spent so much of his life dedicated to Go ...
After AlphaGo won the first three games, I wondered not if the computer had reached and surpassed human mastery, but instead how many orders of magnitude better it was. Given today's result, it may be only one order, or even less. Perhaps the best human players are relatively close to the maximum skill level for go, and that the pros of the future will not be categorically better than Lee Sedol is today.
Yudkowski argues that on the scale of intelligence, Einstein and a village idiot are basically right next to each other [1]. So once artificial intelligence gets close to matching the village idiot, it is not far from completely thrashing Einstein. Now if that same picture held for Go, then a situation like this would seem to be impossible. Either the computer should be much worse than a human player, or much better.…
Another way to look at this is just how efficient the human brain is for the same amount of computation. On one hand, we have racks of servers (1920 CPUs and 280 GPUs) [1] using megawatts (gigawatts?) of power, and on the other hand we have a person eating food and using about 100W of power (when physically at rest), of which about 20W is used by the brain. [1] http://www.economist.com/news/science-and-technology/216…
Move 78 gives us hope in the war against the machines. 78 could come to symbolize humanity. What a special moment.
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As far as I can tell from watching Redmond's commentary at the time, there were other options for white in the area.
I was on the other stream; Myungwan Kim and Hajin Lee went way deeper than Redmond typically goes (since they have access to an SGF editor instead of a clumsy demo board, and they aren't performing for the camera as much). They seemed pretty confident in their conclusion that L10 killed white.
Sure, maybe Alphago missed a winning move. But the situation was fluid both strategically and tactically, which might have been why that machine chose it's losing move and moreover, why I don't think it was just a matter of the machine failing to find a kill - especially since I think computers have been able to beat humans on pure tesuji for a while now.
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But he called move 79 a mistake. So did he learn from a human commentator that it was a mistake, or was that AlphaGo's assessment? Because it is actually trivial in MCTS to reevaluate moves already taken once you have a better assessment of the positions that follow it.
I would love to hear from the team for sure, but I suspect that they have, by now, fed the game state as of move 78 into AlphaGo, had it look exhaustively at possibilities (way more than in the few minutes it took during the match), and determined that in hindsight move 79 was indeed not an optimal response.
Anyway I'm pretty sure what happened; I have actually implemented MCTS myself and know that you can trivially update evaluations of old nodes in MCTS as the game continues and you get a better estimate of the line of play actually taken, although you wouldn't normally have a reason to do so. Basically, it can see in hindsight that a move it took was bad, but without additional calculation you wouldn't know whether the alternatives were any better, or also are worse than estimated at the time.