4 ms·

Forest for the trees. Monte Carlo Tree Search was necessary and itself a massive improvement over minimax but not sufficient for creating a Go program to chall

Forest for the trees. Monte Carlo Tree Search was necessary and itself a massive improvement over minimax but not sufficient for creating a Go program to challenge professional players. The true innovation here is the neural networks. Without those networks to guide it AlphaGo plays far worse than existing programs. The fact that those networks are sufficient is pretty incredible. We already knew that by inventing them we had created a very pure form of pattern recognition, but it's surprising that the pattern recognition coupled with some tree search seems to be all you need to play Go as well as humans. It's not impressive to you that we've now reproduced a piece of human intelligence, "intuition", which was previously considered out of reach?

panic 11y ago
It's totally impressive! I'm excited for the future of DeepMind and to see what other kinds of things we can build from neural networks. I'm just saying that we have to be careful with what kind of intelligence we ascribe to an algorithm like this. An AI is not going to take over the world by impressing us with its game-playing skills.
- nl 11y ago
  DeepMind is the company/lab Google bought. The algorithm is more properly called AlphaGo.
- JabavuAdams 11y ago
  > An AI is not going to take over the world by impressing us with its game-playing skills. What if you train an AI to play an RTS where matter, energy, and time are the resources and the goal is to take over the world?
  - argonaut 11y ago
    I'm not going to fear an AI whose idea of "the world" is a computer game, an AI that isn't even aware of the existence of the real world, and isn't even aware of the existence of the set of real world actions.
    - PascalsMugger 11y ago
      I think, almost by definition, you won't be afraid of anything until it's already coming for you. By that time it will already have the capability to decide your future.
      - argonaut 11y ago
        The thing is you are essentially making an unfalsifiable argument, invoking the existence of something that is merely imaginary.
    - JabavuAdams 11y ago
      I should have put "RTS" in quotes. I'm referring to the real world -- the original RTS.
taneq 11y ago
> Forest for the trees. Heh, nice one.
nazka 11y ago
> but it's surprising that the pattern recognition coupled with some tree search seems to be all you need to play Go as well as humans. Is it really all we need? Or it is more that they threw a lot of hardware to it? What if if a part of its efficiency is because they threw a lot of GPUs with a huge network, rather than having a NN efficient by itself? We see that: "AlphaGos Elo when it beat Fan Hui was 3140 using 1202 CPUs and 176 GPUs. Lee Sedol has an equivalent Elo to 3515 on the same scale (Elos on different scales aren't directly comparable). For each doubling of computer resources AlphaGo gains about 60 points of Elo." It's a lot of hardware.
- javierluraschi 11y ago
  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.
  - nazka 11y ago
    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. Facebook, Google and others seem to agree that, at the end, the quantity and quality of data are more important than the algorithm itself. So how much is it at play here? Knowing that will be able to show us why it is performing well and how much we can appreciate their work.
    - javierluraschi 11y ago
      You can take a look at their paper... http://airesearch.com/wp-content/uploads/2016/01/deepmind-mastering-go.pdf http://airesearch.com/wp-content/uploads/2016/01/deepmind-ma... 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.

panic 11y ago

It's totally impressive! I'm excited for the future of DeepMind and to see what other kinds of things we can build from neural networks. I'm just saying that we have to be careful with what kind of intelligence we ascribe to an algorithm like this. An AI is not going to take over the world by impressing us with its game-playing skills.

nl 11y ago
DeepMind is the company/lab Google bought. The algorithm is more properly called AlphaGo.
JabavuAdams 11y ago
> An AI is not going to take over the world by impressing us with its game-playing skills. What if you train an AI to play an RTS where matter, energy, and time are the resources and the goal is to take over the world?
- argonaut 11y ago
  I'm not going to fear an AI whose idea of "the world" is a computer game, an AI that isn't even aware of the existence of the real world, and isn't even aware of the existence of the set of real world actions.
  - PascalsMugger 11y ago
    I think, almost by definition, you won't be afraid of anything until it's already coming for you. By that time it will already have the capability to decide your future.
    - argonaut 11y ago
      The thing is you are essentially making an unfalsifiable argument, invoking the existence of something that is merely imaginary.
  - JabavuAdams 11y ago
    I should have put "RTS" in quotes. I'm referring to the real world -- the original RTS.

taneq 11y ago

> Forest for the trees. Heh, nice one.

nazka 11y ago

> but it's surprising that the pattern recognition coupled with some tree search seems to be all you need to play Go as well as humans. Is it really all we need? Or it is more that they threw a lot of hardware to it? What if if a part of its efficiency is because they threw a lot of GPUs with a huge network, rather than having a NN efficient by itself? We see that: "AlphaGos Elo when it beat Fan Hui was 3140 using 1202 CPUs and 176 GPUs. Lee Sedol has an equivalent Elo to 3515 on the same scale (Elos on different scales aren't directly comparable). For each doubling of computer resources AlphaGo gains about 60 points of Elo." It's a lot of hardware.

javierluraschi 11y ago
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.
- nazka 11y ago
  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. Facebook, Google and others seem to agree that, at the end, the quantity and quality of data are more important than the algorithm itself. So how much is it at play here? Knowing that will be able to show us why it is performing well and how much we can appreciate their work.
  - javierluraschi 11y ago
    You can take a look at their paper... http://airesearch.com/wp-content/uploads/2016/01/deepmind-mastering-go.pdf http://airesearch.com/wp-content/uploads/2016/01/deepmind-ma... 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.