231–240 of 596 posts
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I know absolutely nothing about Go, and I enjoyed the deepmind channel and found the commenting very good. I was actually thinking about playing a game with another total noob, just for fun, since the rules can be explained in 1 minute (unlike chess).
Chess rules can be explained in 1 minute - you just have to talk really, really fast.
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And? This does not address my argument that the complexity is beyond-combinatorially explosive (infinite spaces). I'm not talking about the space of possible board states. I'm talking about merely the set of all possible actions. EDIT: clarified my language to address below reply.
Go is combinatorially explosive, too.
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I watched most of the game on the Deepmind Youtube channel. Although I barely know the rules of Go, it was really nice that explained a lot of the strategies, although aside from the basic explanations most of the rest still flew over my head. I was still hooked, though. However it was infuriating that many times they switched randomly between video feeds, so I couldn't actually see what the commentators were talking…
I've been finding it pretty unwatcheable. Does anyone know of a version that doesn't have the technical issues? (I'm very happy with the commentary, but the video keeps cutting to this 0 minutes screen and audio is patchy).
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Why does Teodolfo even need a citation for that? Are you really suggesting that there is no group of people who believe AI is not a threat to worry about, and believed that AlphaGo would win?
I'm sure there's someone on the planet. Which is a very different state of affairs from the one that would obtain if a well-known AI researcher had, say, made a $1500 public bet on AlphaGo winning, while also loudly declaring that worrying about AGI was like worrying about overpopulation on Mars, in the company of other researchers declaring similar combined expectations. What actually happened was the reverse of tha…
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But the computer certainly does not win on the amount of training samples required. The human is at the same level as the computer now for Go, but the computer has had much more training samples as Lee Sedol could process in his lifetime. The next step is to reduce the training time/samples for the computer to get the same performance.
That's silly. Why would you want to put human limitations on the computer? We don't artificially put computer limitations on the human.
Of course, if there are many samples, the computer can go through those faster, but if there are no samples already and the computer has to learn example by example as humans do as well, humans may still have an advantage.
Of course, this advantage will diminish as well as AI advances.
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1202 CPUs and 176 GPUs apparently edit: according to the livestream
1202 CPUs and 176 GPUs is the figure mentioned in the Nature paper. But it's important to understand that this is the computer used to train the networks used by the algorithm. It took about 30+ days worth of wallclock to train it. That's about 110 megawatt-hours (MWh) worth of energy required! During the play, the computational requirements are vastly less (but I don't know the figures). It's still probably more tha…
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For AlphaGo, a "pixel" is a point on the board. It uses essentially the same convolutional neural networks (CNNs) that are in state-of-the-art machine vision systems. But yes, the overall architecture is rather different from the Atari system, due to the integration of that CNN with Monte Carlo Tree Search.
Sorry, you're off base a bit. The Atari system did use a Deep Neural Network / Reinforcement algorithm, but as the original poster was trying to point out, the rules of Go were very much hard coded into AlphaGo. From what this [1] says, multiple DNNs are learning how to traverse Monte Carlo trees of Go games. The reinforcement piece comes in choosing which of the Go players is playing the best games. While the higher…
Can someone explain why this is more impressive than a computer beating top chess players over a decade ago? I'm not very familiar with Go, and while there were far more squares on a Go board, it seems less sophisticated than chess to me. Maybe Go has way more moves possible and emergent strategies or something I'm not taking into account.
Here's a way to measure the sophistication of a game of skill. Consider two players A and Z. A is a ten-year-old who has just been told the rules; Z is God. Now, in between them, put a series of other players B, ..., Y, where B beats A 2/3 of the time, C beats B 2/3 of the time, ..., Z beats Y 2/3 of the time. (We assume God doesn't use his magical divine powers to cheat by, e.g., making Y play bad moves.) Unfortunat…
A has 150 more Elo rating than B in chess. Elo says A has a 2/3 EV on the game result, and B has 1/3.
In tenchess, A will get 20/3 points on average and B will get 10/3 points. A will have more points than B in 79% of tenchess games, but the Elo ratings will not change. Elo doesn't consider winning and losing as binary. (This is why draws behave sensibly.) Just as a tie between these players cause A to lose rating, so too would a marginal win from A.
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The human can run off resources that are available "in the wild", self-repair, and self-replicate at better than 1:1 (that is, a group of n humans produce >n offspring), whereas the smartphone needs a huge amount of infrastructure to repair it and produce new ones. I don't think any mass comparison is really meaningful, mind, but it's not that simple.
Advanced chess players require a society which produces enough surplus to afford enough leisure to allow someone to not only produce a brain not damaged by starvation, but to allow them to use that brain to learn chess at a high level. It took a very long time for humans to get to that level, even though chess is a fairly old game. My point is, humans "in the wild" likely didn't have any equivalent to chess, because…
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Agreed. People don't realize that all of the huge algorithmic innovations (LSTMs, Convolutional neural networks, backpropagation) were invented in past neural net booms. I can't think of any novel algorithms of the same impact and ubiquity (e.g. universally considered to be huge algorithmic leaps) that have been invented in this current boom. The current boom started due to GPUs.
Something being invented previously doesn't mean that it existed as a matter of engineering practicality; improved performance is some but not all of that. Just describing something in a paper isn't enough to make it have impact, many things described in papers simply don't work as described. A decade ago I was trying and failing to build multi-layer networks with back-propagation-- it doesn't work so well. More mode…