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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Planning with Pixels in (Almost) Real Time · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1801.03354/</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/1801.03354/feed.xml" rel="self" type="application/rss+xml" /><item><title>Planning with Pixels in (Almost) Real Time — mentioned by YeGoblynQueenne</title><link>https://news.ycombinator.com/item?id=43630663</link><guid isPermaLink="false">urn:hackerpapers:mention:1801.03354:43630663</guid><pubDate>Wed, 09 Apr 2025 10:34:51 GMT</pubDate><description>&lt;p&gt;&amp;gt;&amp;gt; This is exactly correct, LLMs did scale with huge data, symbolic AI did not. So why? Like the rock salad you&amp;#x27;re mixing up two disparate contexts here. Symbolic AI like SAT solvers and planners is not trying to learn from data and there&amp;#x27;s no context in which it has to &amp;quot;scale with huge data&amp;quot;. Instead, what modern SAT solvers and planners do is even harder than &amp;quot;scaling with data&amp;quot; - which, after all, today means having imba hardware and using it well. SAT solving and planning can&amp;#x27;t do that: SAT is NP-complete and planning is PSPACE-complete so it really doesn&amp;#x27;t matter how much you &amp;quot;scale&amp;quot;…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1801.03354/"&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>
