Teaching Deep Convolutional Neural Networks to Play Go · HackerPapers discussions

Teaching Deep Convolutional Neural Networks to Play Go — mentioned by deepnet

Tue, 03 Nov 2015 19:41:07 GMT

This Facebook Video suggests it has had months of either training or development, which sounds might be it learning by playing, reinforcement learning in this space could be very interesting - more details would be good. The two breakthrough papers using Deep Conv Nets to play Go - trained on Expert Games to predict the Experts Next Move. This is made possible by the huge archives of expert play from online Go servers that have reached Big Data sizes in the last few years. Teaching Deep Convolutional Neural Networks to Play Go - Clark & Storkey Edinburgh Informatics 2015…

Paper and discussion history. A mention is not an endorsement.

Teaching Deep Convolutional Neural Networks to Play Go — mentioned by Jun8

Mon, 14 Sep 2015 19:14:53 GMT

Well, it didn't really teach itself, it was carefully designed and was fed a huge (155M games generated from 5M actual games) data set. Coupled with an semi-supervised approach (rate each position whether the AI playing itself wins or loses). Similar approach yielded strong AIs for go, too (http://arxiv.org/abs/1412.3409), yet nowhere near the IM level for chess. One comment in the article that stuck to me was (and this is central to AI discussions) : "While Deep Blue was searching some 200 million positions per second, Kasparov was probably searching no more than five a second. And yet he…

Paper and discussion history. A mention is not an endorsement.

Teaching Deep Convolutional Neural Networks to Play Go — mentioned by Retric

Sun, 09 Aug 2015 00:03:17 GMT

Classification is a huge part of chess programs. You need to quickly evaluate a millions of boards to decide which side has the advantage. However, do to the well understood rules we can write efficient classifiers by hand. Go is a much harder problem in large part because it's really hard to accurately classify which board is better off. Teaching Deep Convolutional Neural Networks to Play Go: "Our convolutional neural networks can consistently defeat the well known Go program GNU Go... It is also able to win some games against state of the art Go playing program Fuego while using a fraction…

Paper and discussion history. A mention is not an endorsement.

Teaching Deep Convolutional Neural Networks to Play Go — mentioned by lucidrains

Sat, 08 Aug 2015 23:15:22 GMT

Naveen: http://arxiv.org/pdf/1412.3409.pdf anyways, that's not my point. i'm saying deep learning for image analysis has been a huge success, and people should explore ways to apply this success to medical imaging. but just for your info, neural networks are slowly being generalized to a lot of other challenges. look around for topics on recurrent neural networks, memory networks, reinforcement learning, etc etc. i don't think we've fully finished exploring the many ways neural networks can help solve life's challenges just yet.

Paper and discussion history. A mention is not an endorsement.

Teaching Deep Convolutional Neural Networks to Play Go — mentioned by kastnerkyle

Thu, 06 Aug 2015 21:31:02 GMT

The literature is mostly image based, but as other users have said voice, text, and video have also seen substantial gains. It has also been used to great effect for taxi destination prediction (heterogenous information) [1], drug discovery [2], and general operations on graphs [3]. There are also lots of examples beyond classification - structured prediction such as image segmentation [4], predicting depth, surface normals, and pixels from only RGB [5], text to text translation [6], image captioning [7], Turing machines [8], speech synthesis [9], question answering (Q/A) [10][11]…

Paper and discussion history. A mention is not an endorsement.

Teaching Deep Convolutional Neural Networks to Play Go — mentioned by deepnet

Wed, 29 Jul 2015 20:49:13 GMT

Monte Carlo Tree Search ( Random playout ) is currently the best computer strategy for evaluating a Go position. This is likely due to the way Go works , random playout provides a rough estimate of who controls what territory ( this is how Go is scored ). Recently two deep-learning papers showed very impressive results. http://arxiv.org/abs/1412.3409 http://arxiv.org/abs/1412.6564 The neural networks were tasked with predicting what move an expert would make given a position. The MCTS takes a long time 100,000 playouts are typical - once trained the neural nets are orders of magnitude faster.…

Paper and discussion history. A mention is not an endorsement.

Teaching Deep Convolutional Neural Networks to Play Go — mentioned by deepnet

Wed, 13 May 2015 09:12:20 GMT

*references Teaching Deep Convolutional Neural Networks to Play Go by Clarke & Storkey http://arxiv.org/abs/1412.3409 Move Evaluation in Go Using Deep Convolutional Neural Networks by Maddison , Huang , Silver & Sutskever http://arxiv.org/abs/1412.6564 Tesauro's TD-Gammon http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node108.htm... Playing Atari with Deep Reinforcement Learning , Mnih et al. https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf Otoro's CNE Bibliography http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/ Neuro Evolution - http://en.wikipedia.org/wiki/Neuroevolution

Paper and discussion history. A mention is not an endorsement.

Teaching Deep Convolutional Neural Networks to Play Go — HN submission

Mon, 15 Dec 2014 18:11:43 GMT

Teaching Deep Convolutional Neural Networks to Play Go

71 points; 17 discussion replies on HN. Paper and source history. A submission is not an endorsement.