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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mathematics, word problems, common sense, and artificial intelligence · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2301.09723/</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/2301.09723/feed.xml" rel="self" type="application/rss+xml" /><item><title>Mathematics, word problems, common sense, and artificial intelligence — mentioned by nyrikki</title><link>https://news.ycombinator.com/item?id=37373565</link><guid isPermaLink="false">urn:hackerpapers:mention:2301.09723:37373565</guid><pubDate>Sun, 03 Sep 2023 19:32:05 GMT</pubDate><description>&lt;p&gt;Feed forward Neural Networks, like LLMs using attention are effectively Directed acyclic graphs, so it has little it can do to &amp;#x27;evaluate from the outside&amp;#x27; Even if you can move to second-order logic logically-valid formulas in second-order logic is not recursively enumerable. AS RE problems are those which a Turing machine can answer &amp;quot;YES&amp;quot; in a finite amount of time, but a &amp;quot;NO&amp;quot; answer might never come, moving out of RE spaces like DAGs is not computationally feasible. The problem with &amp;#x27;pattern finding&amp;#x27; is that it can learn some even HoL problems if it is in the corpus, but will fail on even…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2301.09723/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
