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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neurosymbolic AI -- Why, What, and How · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2305.00813/</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/2305.00813/feed.xml" rel="self" type="application/rss+xml" /><item><title>Neurosymbolic AI -- Why, What, and How — mentioned by PaulHoule</title><link>https://news.ycombinator.com/item?id=39543395</link><guid isPermaLink="false">urn:hackerpapers:mention:2305.00813:39543395</guid><pubDate>Wed, 28 Feb 2024 21:03:56 GMT</pubDate><description>&lt;p&gt;There is a lot of work on neurosymbolic systems, see this review https://arxiv.org/pdf/2305.00813.pdf I&amp;#x27;d note that it would be much more powerful to produce code to run in an SMT solver https://en.wikipedia.org/wiki/Satisfiability_modulo_theories as opposed to Prolog. It&amp;#x27;s clear to me how you can get the LLM to generate code for such a system but many of the tough problems about decidibility are as tough as they ever were. On top of that there is the problem of efficient logical inference. For instance the Cyc system https://cyc.com/platform/ has more than 1000 specialized inference engines…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2305.00813/"&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>
