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That's incorrect. The features that AlphaGo uses are not pixel level features, but board states - and the architecture between AlphaGo and the Atari network is
by Fede_V 11y ago
That's incorrect. The features that AlphaGo uses are not pixel level features, but board states - and the architecture between AlphaGo and the Atari network is completely different.
It's still an incredibly achievement - but it's important to be accurate.
- DavidSJ 11y agoFor 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.
- drawnwren 11y agoSorry, 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 portions do share some similarities with the Atari system, at a basic level this is a machine that was designed and trained to play Go. AlphaGO is 'essentially the same' as the Atari system in the same way that all Neural Networks are 'essentially the same.' Is this an extremely impressive accomplishment? Yes. However, doesn't seem to qualify as anything close to generalizable. [1] http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html http://googleresearch.blogspot.com/2016/01/alphago-mastering...
- DavidSJ 11y agoI didn't say AlphaGo is essentially the same as Deep Q Networks. I said the convolutional neural network part of it is essentially the same. We agree that the integration of that CNN into the rest of the system is very different.
- Houshalter 11y agoIt's best to say that alphago uses neural networks, which are extremely general. The same way planes and cars both use internal combustion engines. ICEs are extremely general. They produce mechanical energy from gas, and are totally uncaring whether you put them into a plane or a car. The body of the plane is necessary, but isn't really the interesting part. Likewise NNs are uncaring what application you put them into. Give them a different input and a different goal, and they will learn to do that instead. Alphago gave it's NN's control over a monte carlo search tree, and that turned out to be enough to beat Go. They could plug the same AI into a car and it would learn to control that instead. Note that even without the monte carlo search system, it was able to beat most amateurs, and predict the moves experts would make most of the time.
- argonaut 11y agoEven without the neural net system, AI is able to beat most amateurs, and predict moves experts would make.
- Houshalter 11y agoI'm not sure that's correct. MCTS has well known weaknesses, and isn't even a predictive algorithm. MCTS on it's own couldn't get anywhere near beating the top Go champion, that requires deepminds neural networks.
- argonaut 11y agohttp://www.milesbrundage.com/blog-posts/alphago-and-ai-progress http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr... The best Go program before AlphaGo was CrazyStone, ranked at 5-dan ("high amateur" range).
- Houshalter 11y agoThere's a massive skill difference between ameatures and professionals. It couldn't even beat the top ameatures.
- eru 11y agoActually, putting a piece of software in front that infers board states from a video feed would be an easy problem.