511–520 of 596 posts
there should be like a North American Go Nationals or something like that televised on twitch
Anyone putting money down on Sedol? He said it will be either 5-0 or 4-1 in his favor.
I was at the 2003 match of Garry Kasparov vs Deep Junior -- the strongest chess player of all time vs what was at that point the strongest chess playing computer in history. Kasparov drew that match, but it was clear it was the last stand of homo sapiens in the man vs machine chess battle. Back then, people took solace in the game of Go. Many boldly and confidently predicted we wouldn't see a computer beat the Go wor…
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What does a lot of hardware means? The human brain has about 100B neurons with about 100+ dendrites per neuron, while AlphaGo has about 1K CPUs with about 2B transistors per CPU.
I am wondering how much the amount of hardware they used had an effect on the bottom line, compared to the wisdom of their algorithm. Everybody knows it's a great step in AI. But how much? How much their algorithm is smart? Or simply put did they overfit by throwing a lot of layers and GPUs to the task? Or the algorithm is truly smart? What is the ratio of that. It is the same question for the data they used. Faceboo…
Basically, the ("lots of hardware") distributed implementation gets ~3100 points in the Elo rating against ~2900. ~2900 is still sufficient to win against Fan Hui. So I would say, that yes, this algorithm has most of the merit here.
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In chess there are various pieces with differing powers, with Go it seems like they all have similar powers. Again, I don't really know the game and might be wrong, but that was my impression.
One thing that's cool about Go is that the pieces get their value and power not from the rules, but from how they are used. In an actual game of Go, you will find groups of ten pieces that are casually thrown away, and you will find single pieces that the whole game revolve around. In the rulebook, they are the same piece, but in actual effort expended to save/attack them, you'll see they're valued vastly differently…
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I've never felt playing against what is suppose to be an entire room of machines (wether Deep Blue or Watson) to be fair. What would be fair is to limit the total mass of the computer to say 200kg and leave it at that. What is effectively happening is AlphaGo is running on a distributed system of many, many machines. Even Watson took an entire room. Google is paying a premium to push AlphaGo to win.
Chess engines and processing power have since then advanced to a point where my phone can now reliably beat Carlsen. There is no reason to suppose Go is different in that respect. In 10 years, DeepMind will fit into a phone.
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Why wouldn't the same be true for Go? Because the search space of good moves is larger?
Totally could be the same for Go, but yeah the search space seems much bigger. I feel in chess there are a few good moves on every turn whereas for Go it seems there are so many. But then, it may be because I'm less good at Go and I don't see the "obvious" move, not that I necessarily see it in chess but you know what I mean.
Some people were downplaying the victory of AlphaGo over the European champion because he was only a 2p player. I wonder what they have to say now.
Congratulations to the team at Deepmind, and I'm wishing good luck for Sedol in the remaining matches - if he wins we would certainly get to see a second series rematch some months down the line, and that would be very exciting for go fans everywhere.
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It's trivial today. It would have been interesting perhaps twenty years ago?
No, because even handcrafted computer vision systems from 20 years ago would be able to parse a Go board (edge detection + check the color contrast).
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It's easy to say that, in the same way that people now wouldn't be surprised if we could factor large numbers in linear time if we had a functional quantum computer !! 10 years ago no one believed it was possible to train deep nets[1]. It wasn't until the current "revolution" that people learned how important parameter initialization was. Sure, it's not a new algorithm, but it made the problem tractable. So far as al…
It's only difficult because no one threw money at it. It's like saying going to Mars is difficult. It is - but most of the technology is there already, just need money to improve what was used to go to the moon. If you asked people 10 years ago before the moon landing if it was possible, I too would agree it's impossible. But after that breakthrough it opened up the realm is possibilities. I see AlphaGo more of an in…
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Starcraft in many ways is a much easier game for an AI to beat top pros at than Go.
They say Starcraft is still 5-10 years out for AI to beat pros: http://www.newyorker.com/tech/elements/deepmind-artificial-i... (ctrl+f for Starcraft at the bottom of this article)
Some units are balanced by the fact that no human can manipulate them to their full potential. Once you remove that restriction, the AI can abuse the speed of execution, acting as a force multiplier that will cover any strategic lackings.
If they want DeepMind to really "play" Starcraft in the traditional sense, i.e. make it win based on decision making and reasoning about the game, then they'll need to artificially rate limit its APM.