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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1711.09667/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1711.09667/feed.xml" rel="self" type="application/rss+xml" /><item><title>DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess — mentioned by ScottAaronson</title><link>https://news.ycombinator.com/item?id=17427983</link><guid isPermaLink="false">urn:hackerpapers:mention:1711.09667:17427983</guid><pubDate>Fri, 29 Jun 2018 21:28:49 GMT</pubDate><description>&lt;p&gt;You should probably wait for an AMA with someone who&amp;#x27;s actually an expert in AI, and ask them this question. Since you asked me, though, I did a quick Google search and found the following paper: https://arxiv.org/pdf/1711.09667.pdf where indeed they use machine learning to induce the rules of chess from a large number of played games (and then learn to play better than any human). It doesn&amp;#x27;t surprise me at all that this would be feasible with current tools, although I haven&amp;#x27;t studied the paper yet, and would be curious to know how many games you need before you&amp;#x27;ve learned all the rules…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1711.09667/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.NE</category></item></channel></rss>
