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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Algorithmic Information Theory: a brief non-technical guide to the field · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/cs_0703024/</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/cs_0703024/feed.xml" rel="self" type="application/rss+xml" /><item><title>Algorithmic Information Theory: a brief non-technical guide to the field — mentioned by antidesitter</title><link>https://news.ycombinator.com/item?id=17747665</link><guid isPermaLink="false">urn:hackerpapers:mention:cs_0703024:17747665</guid><pubDate>Mon, 13 Aug 2018 02:25:35 GMT</pubDate><description>&lt;p&gt;Solomonoff&amp;#x27;s article is available here: http://www.raysolomonoff.com/publications/alp-theory-and-app.... This is also a good guide: https://arxiv.org/pdf/cs/0703024.pdf. There is nothing in that link that I can apply to a problem. It is all too general and abstract. Algorithmic probability is a framework for general prediction, so it has applications in reinforcement learning (see AIXI), grammar discovery in linguistics, text compression, continuation of integer sequences, etc. See http://www.scholarpedia.org/article/Applications_of_algorith.... I am certain that to calculate the probability…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/cs_0703024/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.IT</category></item></channel></rss>
